A setting calculation modeling method, device and equipment of an active power distribution network and a medium

Abstract

The application discloses a kind of active power distribution network setting calculation modeling method, device, equipment and medium.Therein, the method is by obtaining the distributed photovoltaic parameter management data of target power distribution network, distributed photovoltaic access quantity and equipment parameter;According to the comparison result of distributed photovoltaic access quantity and preset threshold value, and distributed photovoltaic parameter management data, determine first modeling mode, wherein first modeling mode includes conventional load modeling mode and current source modeling mode, current source modeling mode includes controlled current source fine modeling mode, constant current source fine modeling mode and constant current source aggregation modeling mode;If determine that first modeling mode is current source modeling mode, then according to preset priority, determine second modeling mode, based on second modeling mode and equipment parameter, target power distribution network is modeled and setting calculation is carried out, and the setting value interval is determined.This technical solution, by analyzing the distributed photovoltaic parameter management data of active power distribution network and distributed photovoltaic access quantity, to realize the differential modeling of active power distribution network, improve the adaptability of setting value.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, apparatus, equipment and medium for setting calculation modeling of active distribution networks. Background Technology

[0002] Traditional distribution networks are characterized by a single power source and a radial network structure. When a short circuit or other fault occurs, protection can be achieved simply by disconnecting the circuit breaker on the system side. However, with the deepening development of "dual-carbon construction," a large number of distributed photovoltaic power sources are being connected to traditional distribution networks. The traditional radial grid is transforming into a multi-terminal active network, leading to problems such as fault current distribution, reverse fault current, amplification and external suction effects, and extended arc extinction time at fault points. These developments place higher demands on the effectiveness of traditional distribution network defenses.

[0003] For the setting calculation modeling of distributed photovoltaic high-penetration distribution network protection, current relevant guidelines and technical regulations clearly state that distributed photovoltaic power generation units should preferably adopt a controlled current source model, or can be simplified to a constant current source model. The relay protection should adapt to the safe and stable operation needs of photovoltaic power plants and power systems, and meet the requirements of reliability, selectivity, sensitivity and speed. However, the following problems still exist:

[0004] 1) The calculation efficiency of the controlled current source model for photovoltaic power generation units is relatively low. For example, based on a certain setting calculation software test using a photovoltaic controlled current source model rotating 15 power stations, the calculation time for the maximum short-circuit current of a certain switch reached 46.3 minutes. For each additional power station, the calculation time increased by approximately 2.0654 times. When the number of power stations increased to 30, the estimated calculation time was 40,000 hours. In addition, the method of manually analyzing the boosting effect of photovoltaic power generation or external suction effect to calculate the maximum and minimum short-circuit current is still time-consuming and requires a high level of analytical ability from the calculation personnel, which may lead to errors in power generation and shutdown.

[0005] 2) For lines with a large number of photovoltaic (PV) power stations, detailed modeling is labor-intensive and inefficient. In recent years, a multiplication method has been proposed to model PV power stations with the same transformer substation, taking each PV power station as a unit. However, a single 10 kV line may connect to as many as 50 distributed PV power stations. Modeling each PV power station as a unit still requires detailed modeling. Detailed modeling places high demands on distribution network parameter management and involves a large workload of parameter collection and maintenance. Therefore, detailed modeling is not suitable for lines with a large number of grid-connected PV power stations.

[0006] 3) The calculation using the photovoltaic “controlled current source model - fine modeling” is highly dependent on the tuning calculation software. Tests show that when using the photovoltaic “controlled current source model - fine modeling” for manual calculation, it takes about 7 days to calculate with 2 photovoltaic models involved. When there are more than 3 photovoltaic models, it is necessary to rely entirely on the tuning calculation software, which is not conducive to its widespread application.

[0007] Therefore, how to provide a model that can perform tuning calculations quickly and accurately is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to provide a method, apparatus, equipment, and medium for setting calculation modeling of active distribution networks, in order to solve the problems of long calculation time and low efficiency in active distribution network setting calculation. The objective of this invention is to achieve differentiated modeling of active distribution networks and improve the adaptability of setting values.

[0009] To achieve the above objectives, the beneficial effects of the present invention are: by analyzing the distributed photovoltaic parameter management data and distributed photovoltaic access volume of the active distribution network, differentiated modeling of the active distribution network is realized, thereby improving the adaptability of the set values.

[0010] According to one aspect of the present invention, a method for setting calculation modeling of an active distribution network is provided, the method comprising:

[0011] Acquire distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network;

[0012] Based on the comparison results between the distributed photovoltaic access volume and the preset threshold, and the distributed photovoltaic parameter management data, a first modeling method is determined; wherein, the first modeling method includes a conventional load modeling method and a current source modeling method;

[0013] If the first modeling method is determined to be a current source modeling method, then a second modeling method is determined according to a preset priority. Based on the second modeling method and the equipment parameters, the target distribution network is modeled and set to determine the range of set values. The current source modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method. The preset priority, from low to high, is the controlled current source fine modeling method, the constant current source fine modeling method, and the constant current source aggregation modeling method.

[0014] According to another aspect of the present invention, a setting calculation modeling apparatus for an active distribution network is provided, the apparatus comprising:

[0015] The parameter acquisition module is used to acquire distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network.

[0016] The modeling method determination module is used to determine a first modeling method based on the comparison result between the distributed photovoltaic access volume and the preset threshold, as well as the distributed photovoltaic parameter management data; wherein, the first modeling method includes a conventional load modeling method and a current source modeling method;

[0017] The modeling and setting calculation module is used to determine a second modeling method according to a preset priority if the first modeling method is determined to be a current source modeling method, and to perform modeling and setting calculations on the target distribution network based on the second modeling method and the equipment parameters to determine the setting value range; wherein, the current source modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method, and the preset priority from low to high is the controlled current source fine modeling method, the constant current source fine modeling method, and the constant current source aggregation modeling method.

[0018] According to another aspect of the present invention, an electronic device is provided, the device comprising:

[0019] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the active distribution network setting calculation modeling method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the active power distribution network setting calculation modeling method according to any embodiment of the present invention.

[0021] The technical solution provided by this invention acquires distributed photovoltaic (PV) parameter management data, distributed PV grid connection volume, and equipment parameters of the target distribution network. Based on the comparison between the distributed PV grid connection volume and a preset threshold, and the distributed PV parameter management data, a first modeling method is determined. This first modeling method includes a conventional load modeling method and a current source modeling method. If the first modeling method is determined to be a current source modeling method, a second modeling method is determined according to a preset priority. Based on the second modeling method and equipment parameters, the target distribution network is modeled and set, and the setpoint range is determined. The second modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method. This technical solution analyzes the distributed PV parameter management data and distributed PV grid connection volume of the active distribution network to achieve differentiated modeling of the active distribution network, improving the adaptability of the setpoints.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for setting calculation and modeling of an active distribution network according to Embodiment 1 of the present invention.

[0024] Figure 2 This is a schematic diagram of a fixed value range provided in Embodiment 1 of the present invention;

[0025] Figure 3 This is a flowchart illustrating a method for setting calculation and modeling of an active distribution network according to Embodiment 2 of the present invention.

[0026] Figure 4 This is a schematic diagram of a constant current source model provided in Embodiment 2 of the present invention;

[0027] Figure 5 This is a schematic diagram of a controlled current source model provided in Embodiment 2 of the present invention;

[0028] Figure 6 This is a schematic diagram of an active power distribution network provided in Embodiment 2 of the present invention;

[0029] Figure 7 This is a flowchart illustrating a setting calculation modeling method for an active distribution network provided in Embodiment 3 of the present invention.

[0030] Figure 8 This is a schematic diagram of an equivalent external suction effect provided in Embodiment 3 of the present invention;

[0031] Figure 9 This is an equivalent schematic diagram of the enhancing effect provided in Embodiment 3 of the present invention;

[0032] Figure 10 This is a schematic diagram of the structure of an active distribution network aggregation model provided in Embodiment 3 of the present invention;

[0033] Figure 11 This is a schematic diagram of another active distribution network aggregation model provided in Embodiment 3 of the present invention;

[0034] Figure 12 This is a schematic diagram of the structure of an active distribution network setting calculation modeling device provided in Embodiment 4 of the present invention;

[0035] Figure 13 This is a schematic diagram of the structure of a device that implements a setting calculation modeling method for an active power distribution network according to an embodiment of this application. Detailed Implementation

[0036] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Example 1

[0038] Figure 1This is a flowchart illustrating a method for setting calculation modeling of an active distribution network according to Embodiment 1 of the present invention. This embodiment is applicable to the construction of a setting calculation model for an active distribution network. The method can be executed by an active distribution network setting calculation modeling device, which can be implemented in hardware and / or software and can be configured in a device with data processing capabilities. Figure 1 As shown, the method includes:

[0039] S110. Obtain distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network.

[0040] The target distribution network can be an active distribution network, that is, a distribution network that is connected to distributed photovoltaic power sources and has bidirectional power flow, or an active distribution network.

[0041] Distributed photovoltaic (PV) parameter management data can include the collection and maintenance status of distributed PV parameters. This includes, for example, the management standards for distributed PV parameters, the update cycle for distributed PV parameters, and the collected distributed PV parameters.

[0042] Distributed photovoltaic (PV) grid connection capacity refers to the total capacity of distributed PV power generation devices in the target distribution network. Distributed PV power generation is built near the user's site, and its operation mode is mainly for user-side self-consumption, with surplus electricity fed into the grid. The grid connection capacity is usually measured in kilowatts (kW) or megawatts (MW).

[0043] Specifically, information on existing and en route distributed photovoltaic (PV) projects can be obtained from the contract data of the target distribution network, including the current capacity and connection location of the distributed PV projects. Based on this, the capacities of all distributed PV projects in the target distribution network are summed to obtain the total distributed PV connection capacity. To avoid frequent adjustments to settings due to the continuous connection of distributed PV, this invention uses the projected distributed PV capacity in the calculation. Therefore, it is also necessary to obtain the available distributed PV capacity of the target distribution network, and the sum of the available capacity and the current capacity is taken as the projected distributed PV capacity.

[0044] It should be noted that, considering that the actual power generation of distributed photovoltaics may vary at different times, an average calculation can be performed using power monitoring data over a period of time to obtain a more accurate current distributed photovoltaic grid connection volume.

[0045] Equipment parameters can be the network topology information of the target distribution network, including the following parameters: 1) Topology: including the topological connections between equipment such as the 10kV busbar of the substation, overhead lines, cable lines, distribution transformers, circuit breakers, and distributed photovoltaic systems; 2) Basic parameters of distribution lines: including line type, length, model, resistance per unit length, reactance, and current carrying capacity. Line types are divided into overhead lines and cables; 3) Basic parameters of distribution transformers: including transformer model, turns ratio, rated capacity, and short-circuit impedance; 4) Switchgear data: including the current opening / closing status of circuit breakers; 5) Distributed photovoltaic parameters: photovoltaic capacity, photovoltaic grid connection location, photovoltaic inverter control strategy, etc.

[0046] S120. Based on the comparison result between the distributed photovoltaic grid connection volume and the preset threshold, and the distributed photovoltaic parameter management data, a first modeling method is determined. The first modeling method includes a conventional load modeling method and a current source modeling method.

[0047] Among them, the preset standards may be whether the management standards for distributed photovoltaic parameters are unified, whether the update cycle of distributed photovoltaic parameters is timely, or whether the collected distributed photovoltaic parameters are comprehensive and accurate.

[0048] The preset threshold can be a threshold obtained based on historical work experience.

[0049] Specifically, for lines with distributed photovoltaic (PV) grid connection volume less than the preset threshold and poor distributed PV parameter management, it indicates that distributed PV accounts for a small proportion of the distribution network and has a relatively small impact on the distribution network. In this case, the first modeling method can be determined as the conventional load modeling method. For lines with distributed PV grid connection volume greater than the preset threshold and good distributed PV parameter management, the first modeling method can be determined as the current source modeling method.

[0050] Among them, the conventional load modeling method is to treat distributed photovoltaic as a traditional static or dynamic load model during the model building process, that is, not to consider the impact of distributed photovoltaic on short-circuit current, and usually no modeling is required.

[0051] The advantage of the above technical solution is that by performing quantitative or qualitative analysis on distributed photovoltaic parameter management data and distributed photovoltaic access volume, it is possible to select the appropriate modeling method according to different situations, and better analyze the operating status and characteristics of the target distribution network.

[0052] S130. If the first modeling method is determined to be a current source modeling method, then a second modeling method is determined according to a preset priority. Based on the second modeling method and the equipment parameters, the target distribution network is modeled and set, and the set value range is determined. The current source modeling methods include controlled current source fine modeling, constant current source fine modeling, and constant current source aggregation modeling. The preset priorities, from low to high, are the controlled current source fine modeling, the constant current source fine modeling, and the constant current source aggregation modeling.

[0053] The preset priority can be the order of the controlled current source fine modeling method, the constant current source fine modeling method, and the constant current source aggregate modeling method.

[0054] Because different modeling methods simplify the target distribution network in different ways, the resulting models have inconsistent ranges for setting values ​​when calculating short-circuit current, or even no range at all. Therefore, this invention can determine the setting range based on the calculation accuracy and setting range of each modeling method; for example, the lower the calculation accuracy and the smaller the range, the higher the priority.

[0055] Among these methods, the refined modeling of controlled current sources treats distributed photovoltaic (PV) systems as a controlled current source during model building, thus more accurately reflecting the output characteristics of PV systems. By modeling the control strategy in detail, the short-circuit state of the distribution network can be analyzed more accurately.

[0056] Among them, the constant current source fine modeling method can treat distributed photovoltaic as a constant current source during the model building process, which simplifies the model of distributed photovoltaic to a certain extent, but can still reflect its impact on the distribution network relatively accurately.

[0057] One approach to constant current source aggregation modeling involves combining multiple distributed photovoltaic (PV) systems into a single, larger constant current source during the model building process. This reduces computational complexity, especially when there are many distributed PV systems scattered across the distribution network. Aggregation simplifies the complex multi-distributed PV system into a relatively simple model, facilitating tuning calculations.

[0058] In some embodiments of the present invention, a first modeling method is determined based on the comparison result of the distributed photovoltaic access volume with a preset threshold and the distributed photovoltaic parameter management data, including: if the distributed photovoltaic parameter management data meets a preset standard and the distributed photovoltaic access volume is greater than a preset threshold, then the first modeling method is determined to be a current source modeling method; otherwise, the first modeling method is determined to be a conventional load modeling method.

[0059] This invention models the target distribution network using conventional load modeling, controlled current source fine modeling, constant current source fine modeling, and constant current source aggregation modeling, respectively. Based on the constructed setting calculation model, it calculates the setting ranges of the XX outgoing switch in overcurrent stage I, overcurrent stage II, and overcurrent stage III. The results are as follows: Figure 2 As shown, the red area represents the constant value range solved by the model constructed based on the conventional load modeling method, the orange area represents the constant value range solved by the model constructed based on the controlled current source fine modeling method, the blue area represents the constant value range solved by the model constructed based on the constant current source fine modeling method, and the green area represents the constant value range solved by the model constructed based on the constant current source aggregation modeling method.

[0060] Depend on Figure 2 It can be seen that the model constructed based on the conventional load modeling method has the largest setpoint range and the lowest calculation accuracy; the model constructed based on the controlled current source fine modeling method has a relatively large setpoint range and the highest calculation accuracy; the model constructed based on the constant current source aggregation modeling method has the smallest setpoint range and the lowest calculation accuracy; the models constructed based on the controlled current source fine modeling method and the constant current source fine modeling method require a large amount of parameter collection and system modeling work; the model constructed based on the constant current source aggregation modeling method does not need to consider the actual location of photovoltaics, greatly reduces the amount of parameter collection and modeling work, has the smallest calculation amount, and can be manually calculated without relying on the setting calculation system, which is convenient for promotion and application.

[0061] For example, if the preset priorities are from high to low as constant current source aggregation modeling, constant current source fine modeling, and controlled current source fine modeling, then the modeling and tuning calculation will be performed first based on the constant current source aggregation modeling and equipment parameters; if the range of tuning values ​​cannot be determined, then the modeling and tuning calculation will be performed based on the constant current source fine modeling and equipment parameters; if the range of tuning values ​​cannot be determined, then the modeling and tuning calculation will be performed based on the controlled current source fine modeling and equipment parameters.

[0062] When modeling the target distribution network, the target distribution network can be equivalently processed based on the second modeling method to construct a setting calculation model. Then, the voltage and current of the lines in the target distribution network can be calculated using equipment parameters to determine the range of setting values.

[0063] In some embodiments of the present invention, after determining the first modeling method based on the comparison result of the distributed photovoltaic access volume and the preset threshold, and the distributed photovoltaic parameter management data, the method further includes: if the first modeling method is determined to be a conventional load modeling method, then each distributed photovoltaic in the target distribution network is taken as a load unit, and a setting calculation model is constructed in combination with the equipment parameters.

[0064] Specifically, each distributed photovoltaic unit in the target distribution network is treated as a load unit, and no modeling is required. The target distribution network is still calculated for setting values ​​as a single-source radial power grid.

[0065] The advantage of this scheme is that by analyzing the distributed photovoltaic parameter management data and the amount of distributed photovoltaic access in the active distribution network, differentiated modeling of the active distribution network can be achieved, thus improving the adaptability of the settings.

[0066] Example 2

[0067] Figure 3 This is a flowchart illustrating a setting calculation modeling method for an active distribution network provided in Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiment. Figure 3 As shown, the method includes:

[0068] S210. Obtain distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network.

[0069] S220. Based on the comparison result between the distributed photovoltaic grid connection volume and the preset threshold, and the distributed photovoltaic parameter management data, a first modeling method is determined. The first modeling method includes a conventional load modeling method and a current source modeling method.

[0070] S230. If the first modeling method is determined to be a current source modeling method, then the second modeling method is determined to be a constant current source aggregation modeling method according to a preset priority. Based on the constant current source aggregation modeling method and the equipment parameters, the target distribution network is modeled and set to determine whether there is a set value range. The current source modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method.

[0071] Specifically, the first step is to select aggregated distributed photovoltaic (PV) systems based on factors such as geographical location, access voltage level, and power generation characteristics. For example, distributed PV systems within the same regional PV power station or connected to the same substation and line can be aggregated. Next, for the selected distributed PV clusters, the parameters of the equivalent constant current source are calculated, such as current magnitude and direction. For instance, this could be the average of the maximum output current of each distributed PV system under typical operating conditions, or a weighted average calculation based on the capacity and power generation characteristics of the distributed PV systems. Then, based on the calculated equivalent current source parameters, constant current source units are constructed. Finally, the constant current source units are combined with the line equipment parameters of the target distribution network for setting calculations to determine if a setting value range exists.

[0072] S240. If not, the second modeling method is determined to be the constant current source fine modeling method, and the target distribution network is modeled and set based on the constant current source fine modeling method and the equipment parameters to determine whether there is a set value range.

[0073] It should be noted that if the target distribution network is modeled and set based on the constant current source aggregation modeling method and equipment parameters, and a set value range is determined, then the set value range will be used as the final output result.

[0074] The constant current source model calculates the photovoltaic (PV) system based on a constant current source providing the "maximum allowable current," typically considered as 1.2 times the rated current. A single PV unit uses the constant current source model, while multiple PV units are modeled separately according to their actual connection location and capacity. During short-circuit current calculations, the system automatically identifies PV models with boosting or external suction effects based on the PV effect, observation points, and fault points, shortening calculation time and effectively improving fault tolerance.

[0075] Specifically, each power generation unit of a photovoltaic power station can be multiplied and equated to a constant current source unit, and modeled and calculated according to the actual location of the distributed photovoltaic power station connected to the distribution network. Figure 4 This is a schematic diagram of a constant current source model provided in Embodiment 2 of the present invention. The model is developed according to the "QGDW 12207-2022 Guidelines for Modeling New Energy Power Stations for Relay Protection Setting Calculation". The photovoltaic system provides the maximum allowable current and is set at 1.2 times the rated current. When there is a three-phase short circuit, the angle is consistent with the angle of the short-circuit current provided by the system. When there is a two-phase short circuit, the negative sequence suppression characteristic of the photovoltaic system is canceled and the angle is consistent with the angle of the short-circuit current provided by the system.

[0076] In some embodiments of the present invention, modeling and setting calculations are performed on the target distribution network based on the constant current source fine modeling method and the equipment parameters to determine whether there is a setting value range. This includes: modeling the target distribution network based on the constant current source fine modeling method and the equipment parameters to obtain a constant current source fine model of the target distribution network; determining the distributed photovoltaic units participating in the setting calculation in the constant current source fine model according to the photovoltaic effect, fault points, and observation points; and performing setting calculations based on the distributed photovoltaic units participating in the setting calculation and the equipment parameters to determine whether there is a setting value range.

[0077] Specifically, the location of the fault point and the observation point can be used to determine the auxiliary and external suction effects of distributed photovoltaic systems.

[0078] In some embodiments of the present invention, the distributed photovoltaic units participating in the tuning calculation in the fine model of the constant current source are determined based on the photovoltaic effect, the fault point, and the observation point, including: if the photovoltaic effect is an external suction effect, then the distributed photovoltaic units between the fault point and the observation point are taken as the distributed photovoltaic units participating in the tuning calculation; if the photovoltaic effect is an amplifying effect, then the distributed photovoltaic units outside the fault point and the observation point are taken as the distributed photovoltaic units participating in the tuning calculation.

[0079] When performing tuning calculations, if the boosting effect needs to be considered, the distributed photovoltaics participating in the boosting effect will be automatically turned on; if the external water absorption effect needs to be considered, the distributed photovoltaics participating in the external water absorption effect will be automatically turned on.

[0080] For example, when performing setting calculations for the overcurrent stage I, the boosting effect needs to be considered but the external draining effect is not considered, so the distributed photovoltaics participating in the boosting effect will be automatically turned on; as another example, when performing setting calculations for the overcurrent stage III, the minimum short-circuit current is considered, and only the photovoltaics with external draining effect are considered, so the distributed photovoltaics participating in the boosting effect will be turned off.

[0081] S250. If it still does not exist, then the second modeling method is determined to be the controlled current source fine modeling method, and the target distribution network is modeled and set based on the controlled current source fine modeling method and the equipment parameters to determine whether there is a set value range.

[0082] It should be noted that if the target distribution network is modeled and set based on the constant current source fine modeling method and equipment parameters, and a set value range is determined, then the set value range is taken as the final output result.

[0083] The controlled current source model can reflect the short-circuit current characteristics throughout the low-voltage ride-through period. The short-circuit current is controlled by factors such as the inverter control target and terminal voltage. Short-circuit calculations require iterative iterations based on the mapping relationship between voltage and current, resulting in relatively high calculation accuracy but also a relatively large computational load. A single photovoltaic (PV) unit uses the controlled current source model, while multiple PV units are modeled separately according to their actual connection location and capacity. During short-circuit current calculations, the setting calculation system automatically identifies PV models with boosting and external suction effects based on the PV effect, observation points, and fault points, shortening calculation time and effectively improving fault tolerance.

[0084] Specifically, each power generation unit of a photovoltaic power station can be multiplied and equated to a controlled current source unit, and modeled and calculated according to the actual location of the photovoltaic power station connected to the distribution network. Figure 5 This is a schematic diagram of a controlled current source model provided in Embodiment 2 of the present invention. The model was developed in accordance with the "QGDW 12207-2022 Guidelines for Modeling New Energy Power Stations for Relay Protection Setting Calculation".

[0085] In some embodiments of the present invention, modeling and setting calculations are performed on the target distribution network based on the controlled current source fine modeling method and the equipment parameters to determine whether there is a setting value range. This includes: modeling the target distribution network based on the controlled current source fine modeling method and the equipment parameters to obtain a controlled current source fine model of the target distribution network; determining the distributed photovoltaic units participating in the setting calculation in the controlled current source fine model according to the photovoltaic effect, fault points, and observation points; and performing setting calculations based on the distributed photovoltaic units participating in the setting calculation and the equipment parameters to determine whether there is a setting value range.

[0086] Specifically, the location of the fault point and the observation point can be used to determine the auxiliary and external suction effects of distributed photovoltaic systems.

[0087] In some embodiments of the present invention, the distributed photovoltaic units participating in the tuning calculation in the fine model of the controlled current source are determined based on the photovoltaic effect, the fault point, and the observation point, including: if the photovoltaic effect is an external suction effect, then the distributed photovoltaic units between the fault point and the observation point are taken as the distributed photovoltaic units participating in the tuning calculation; if the photovoltaic effect is an amplifying effect, then the distributed photovoltaic units outside the fault point and the observation point are taken as the distributed photovoltaic units participating in the tuning calculation.

[0088] When performing tuning calculations, if the boosting effect needs to be considered, the distributed photovoltaics participating in the boosting effect will be automatically turned on; if the external water absorption effect needs to be considered, the distributed photovoltaics participating in the external water absorption effect will be automatically turned on.

[0089] For example, when performing setting calculations for the overcurrent stage I, the boosting effect needs to be considered but the external draining effect is not considered, so the distributed photovoltaics participating in the boosting effect will be automatically turned on; as another example, when performing setting calculations for the overcurrent stage III, the minimum short-circuit current is considered, and only the photovoltaics with external draining effect are considered, so the distributed photovoltaics participating in the boosting effect will be turned off.

[0090] For example, we will use an active power distribution network as an example to illustrate this. Figure 6 This is a schematic diagram of an active power distribution network provided in Embodiment 2 of the present invention. Figure 6 As shown, Line 1 and Line 2 are connected to the 10kV busbar. Line 1 has, in sequence, an outgoing switch XL1, a sectionalizing switch FD11, a sectionalizing switch FD12, and a boundary switch FJ11. A branch switch FZ11 is located on the branch between outgoing switch XL1 and sectionalizing switch FD11. The end of branch switch FZ11 is connected to a distributed photovoltaic system. PV11 There is a branch switch FZ12 on the branch between sectionalizing switch FD11 and sectionalizing switch FD12. The end of branch switch FZ12 is connected to a distributed photovoltaic system. PV12There is a branch switch FZ13 on the branch between the sectionalizing switch FD12 and the dividing switch FJ11. The end of the branch switch FZ13 is connected to a distributed photovoltaic system. PV13 Line 2 consists of, in sequence, an outgoing switch XL2, a sectionalizing switch FD21, a sectionalizing switch FD22, and a boundary switch FJ21. A branch switch FZ21 is located on the branch between outgoing switch XL2 and sectionalizing switch FD21. The end of branch switch FZ21 is connected to a distributed photovoltaic system. PV21 There is a branch switch FZ22 on the branch between sectionalizing switch FD21 and sectionalizing switch FD22. Distributed photovoltaic system I is connected after branch switch FZ22. PV22 and I PV23 There is a branch switch FZ23 on the branch between the sectionalizing switch FD22 and the dividing switch FJ21. The end of the branch switch FZ23 is connected to a distributed photovoltaic system. PV24 .

[0091] If based on the refined modeling method of controlled current sources and equipment parameters, such as... Figure 6 The active distribution network shown is modeled to... Figure 6 Taking branch switch FZ22 as an example, this analysis examines the distributed photovoltaic (PV) units involved in the setting calculation in the refined model of the controlled current source. When calculating the short-circuit current on the low-voltage side of the distribution transformer, the fault point is set on the low-voltage side of the transformer. Using the amplification effect, the system identifies the PV units outside the switch and the fault point as PV units that need to participate in the short-circuit calculation, i.e., I... PV11 I PV12 I PV13 I PV21 and I PV24 Automatically participates in calculations; when calculating the short-circuit current of a fault at the end of the line, the external suction effect is selected, and the system identifies the photovoltaic units between the switch and the fault point as photovoltaic units that need to participate in the short-circuit calculation, i.e., I. PV22 and I PV23 It automatically participates in the calculation.

[0092] The beneficial effect of the above technical solution is that by automatically identifying distributed photovoltaic units that participate in the boosting and external suction effects, the setting calculation time is shortened and the fault tolerance rate is effectively improved.

[0093] The active distribution network setting calculation modeling method provided in this embodiment of the invention improves the setting calculation efficiency by successively adopting the constant current source aggregation modeling method, the constant current source fine modeling method, and the controlled current source fine modeling method through an advanced method.

[0094] Example 3

[0095] Figure 7This is a flowchart illustrating a modeling method for setting calculations in an active distribution network according to Embodiment 3 of the present invention. This embodiment optimizes Embodiment 2, specifically optimizing the modeling and setting calculations of the target distribution network based on the constant current source aggregation modeling method and the equipment parameters, and determining whether there exists a range of setting values. For example... Figure 7 As shown, the method includes:

[0096] S310. Obtain distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network.

[0097] S320. Based on the comparison results between the distributed photovoltaic access volume and the preset threshold, and the distributed photovoltaic parameter management data, a first modeling method is determined; wherein, the first modeling method includes a conventional load modeling method and a current source modeling method, and the current source modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method.

[0098] S330. If the first modeling method is determined to be the current source modeling method, then the second modeling method is determined to be the constant current source aggregation modeling method according to the preset priority. The external suction effect of each switch in the target distribution network is analyzed, the external suction aggregation point of the distributed photovoltaic after each switch is determined, and the distributed photovoltaic after each switch is aggregated into an external suction aggregation unit at each external suction aggregation point.

[0099] The external drain effect refers to the phenomenon in an active distribution network where, when a fault occurs in the downstream distribution line of a distributed photovoltaic grid connection point, part of the short-circuit current of the distributed photovoltaic system flows to the system, resulting in a reduction in the short-circuit current of the upstream switch.

[0100] Among them, the external water collection point can be a location that can represent the external water collection effect of distributed photovoltaics, such as the access point of distributed photovoltaics, the branch point of the line, or other representative locations.

[0101] For example, the location of the fault point can be determined by analyzing the network topology of the target distribution network; then, based on the fault type and location, power system analysis methods can be used to determine the external current distribution of distributed photovoltaic (PV) systems; finally, a suitable external current aggregation point can be selected based on the distribution of the external current. Alternatively, the external current aggregation point can be determined based on experience and historical operating data, such as selecting line nodes near the PV grid connection point as the aggregation point. Another example is selecting locations where the external current flow of PV systems is significant in historical fault data as aggregation points.

[0102] In some embodiments of the present invention, the external drain effect of each switch in the target distribution network is analyzed to determine the external drain aggregation point of the distributed photovoltaic after each switch. This includes: for each switch in the target distribution network, based on the short-circuit current provided by the distributed photovoltaic, the equivalent impedance of the bus, the impedance between the distributed photovoltaic access point and the bus, the impedance between the distributed photovoltaic access point and the fault point, and the voltage of the distributed photovoltaic access point, determining the change in the external drain short-circuit current of each switch in the target distribution network at different access points; and taking the access point corresponding to the maximum change in external drain short-circuit current as the external drain aggregation point.

[0103] Among them, the short-circuit current provided by distributed photovoltaic (PV) can be the current provided by distributed PV to the system when a short-circuit fault occurs in the distribution network. Its magnitude depends on factors such as the capacity and output characteristics of distributed PV and the grid state at the time of the fault.

[0104] Among them, the equivalent impedance of the busbar refers to the equivalent impedance of the upstream system on the busbar. According to the superposition principle, when distributed photovoltaic power is connected to the distribution network, the short-circuit current that can be equivalently provided to it will flow through the busbar impedance to the grounding point of the system's equivalent voltage source, thereby affecting the short-circuit current at the protection device.

[0105] The impedance between the distributed photovoltaic access point and the fault point can be the sum of the line impedance between the distributed photovoltaic access point and the fault point and the equivalent impedance of other electrical components.

[0106] Specifically, the type and location of the fault in the distribution network can be determined first, such as three-phase short circuit, two-phase short circuit, etc., and the specific location of the fault point in the distribution network. Then, based on the short-circuit current provided by distributed photovoltaic (PV), bus impedance, equivalent impedance between the PV connection point and the bus, equivalent impedance between the PV connection point and the fault point, and voltage at the PV connection point, the change in external short-circuit current of the protection device at different connection points of the PV can be determined. Finally, the connection point corresponding to the maximum change in external short-circuit current is the external short-circuit aggregation point. The maximum change in external short-circuit current indicates that the PV has the greatest external short-circuit current absorption effect on the protection device.

[0107] In some embodiments of the present invention, the connection point corresponding to the maximum change in external short-circuit current is taken as the external flow aggregation point, including: if the switch is an outgoing switch, a branch switch or a boundary switch, the lower port of the switch is taken as the external flow aggregation point.

[0108] Figure 8 This is an equivalent schematic diagram of an external suction effect provided in Embodiment 3 of the present invention. Figure 8 Let's take an example to explain. Figure 8In this paper, taking the 2J switch at position 3 as an example, the external suction effect is analyzed, and the fault point is set at position 5. The changes in the short-circuit current of the 2J switch are determined for distributed photovoltaic systems at positions 3, 4, and 6. Three-phase short circuits and two-phase short circuits are discussed separately.

[0109] During a three-phase short circuit:

[0110] Among them, the change in short-circuit current of switch 2J when the distributed photovoltaic system is at position 3 is:

[0111] ΔI PV =I PV ((Z 34 +Z 45 ) / (Z s +Z 12 +Z 23 +Z 34 +Z 45 ))

[0112] The change in short-circuit current of the 2J switch at position 4 in the distributed photovoltaic system is as follows:

[0113] ΔI PV =I PV (Z 45 / (Z s +Z 12 +Z 23 +Z 34 +Z 45 )).

[0114] In the above formula, ΔI PV I represents the change in short-circuit current due to external suction. PV Z represents the short-circuit current provided by distributed photovoltaic power. s Z represents the bus impedance. 12 Z represents the line impedance between position 1 and position 2. 23 Z represents the line impedance between positions 2 and 3. 34 Z represents the line impedance between positions 3 and 4. 45 This indicates the line impedance between position 4 and position 5.

[0115] During a three-phase short circuit, the distributed photovoltaic system at position 6 has no effect on the short-circuit current of the 2J switch.

[0116] As can be seen from the above analysis, when the distributed photovoltaic system is in position 3 during a three-phase short circuit, the change in the external short-circuit current of the 2J switch is the largest, that is, the external suction effect is the greatest. Therefore, position 3 can be regarded as the external suction aggregation point.

[0117] When two phases are short-circuited:

[0118] Among them, the change in short-circuit current of switch 2J when the distributed photovoltaic system is at position 3 is:

[0119]

[0120] The change in short-circuit current of the 2J switch at position 4 in the distributed photovoltaic system is as follows:

[0121]

[0122] In the above formula, ΔI PV I represents the change in short-circuit current due to external suction. PV Z represents the short-circuit current provided by distributed photovoltaic power. s Z represents the equivalent impedance of the busbar. 12 Z represents the line impedance between position 1 and position 2. 23 Z represents the line impedance between positions 2 and 3. 34 Z represents the line impedance between positions 3 and 4. 45 U represents the line impedance between positions 4 and 5. T3 U is the phase voltage at position 3. T4 For the 4-phase voltage at position, U M This represents the phase voltage at the fault point.

[0123] As can be seen from the above analysis, the change in external short-circuit current of distributed photovoltaic power generation during a two-phase short circuit is related to the impedance before and after the T-junction and the voltage of the T-junction. It is difficult to determine the location of the maximum external short-circuit effect. Therefore, the external short-circuit aggregation point of the three-phase short circuit can be taken as the external short-circuit aggregation point of the two-phase short circuit, that is, the location below position 3 is taken as the external short-circuit aggregation point, and the maximum output short-circuit current of the photovoltaic power generation is taken as the change in short-circuit current of the 2J switch.

[0124] Therefore, when the protection device is an outgoing switch, branch switch, or boundary switch, the distributed photovoltaic system can be equivalent to the lower end of the switch.

[0125] S340. Analyze the boosting effect of each switch in the target distribution network, determine the boosting aggregation point of the distributed photovoltaic in front of each switch, and aggregate the distributed photovoltaic in front of each switch into boosting aggregation units at each boosting aggregation point.

[0126] The "enhancing effect" refers to the phenomenon in an active distribution network where distributed photovoltaic power on non-faulty lines provides fault current to faulty lines, thereby increasing the current flowing through the faulty lines.

[0127] Among them, the aggregation point for boosting can be a location that can represent the boosting effect of distributed photovoltaics, such as a critical node near the fault point, or a node near the distributed photovoltaic access point.

[0128] For example, by analyzing the network topology of the target distribution network, the potential locations of faults and the distribution of fault currents can be determined; then, based on the access location and output characteristics of distributed photovoltaic systems, the magnitude of the boosting current at different locations can be calculated. Alternatively, locations with large and representative boosting currents can be selected as boosting aggregation points. Furthermore, boosting aggregation points can also be determined based on empirical judgment and historical fault operation data.

[0129] In some embodiments of the present invention, the amplifying effect of each switch in the target distribution network is analyzed to determine the amplifying aggregation point of distributed photovoltaic (PV) power before each switch. This includes: for distributed PV power on other lines, the downstream outlet of the outgoing switch of the other lines is taken as the amplifying aggregation point of the distributed PV power on the switch of this line; for distributed PV power on this line, based on the short-circuit current provided by the distributed PV power, the equivalent impedance of the bus, the impedance between the distributed PV access point and the bus, and the impedance between the distributed PV access point and the fault point, the amplifying short-circuit current change of the switch at different access points of the distributed PV power on this line is determined; the access point corresponding to the maximum amplifying short-circuit current change is taken as the amplifying aggregation point.

[0130] Specifically, since the boosting effect of photovoltaic power on other lines is independent of the photovoltaic grid connection location, the photovoltaic power on this line has the greatest external boosting effect on the outgoing switch when it is equivalent to the switch bottom. Therefore, the switch bottom is selected as the aggregation point for the boosting effect of distributed photovoltaic power on the switch of this line from other lines.

[0131] For distributed photovoltaic (PV) systems on this line, the type and location of faults in the distribution network can be determined first, such as three-phase short circuits, two-phase short circuits, etc., and the specific location of the fault point in the distribution network. Then, based on the short-circuit current provided by the distributed PV, the bus impedance, the equivalent impedance between the distributed PV connection point and the bus, and the equivalent impedance between the distributed PV connection point and the fault point, the amount of short-circuit current increase provided by the protection device at different connection points can be determined. Finally, the connection point corresponding to the maximum amount of short-circuit current increase is the aggregation point of the increase. The maximum amount of short-circuit current increase indicates that the distributed PV has the greatest effect on increasing the short-circuit current at the protection device.

[0132] In some embodiments of the present invention, the connection point corresponding to the maximum increase in short-circuit current change is used as the photovoltaic aggregation point, including: if the switch is an outgoing switch, the lower port of the outgoing switch of the other lines of the station bus is used as the photovoltaic aggregation point of the other lines of the station bus; if the switch is a branch switch or a boundary switch, the lower port of the outgoing switch of the other lines of the station bus is used as the photovoltaic aggregation point of the other lines of the station bus, and the upper port of the switch is used as the photovoltaic aggregation point of this line.

[0133] Figure 9 This is an equivalent schematic diagram of the enhancing effect provided in Embodiment 3 of the present invention. Figure 9 Let's take an example to explain. Figure 9 In this paper, the auxiliary effect of switch 2J at position 3 is analyzed. The fault point is set on the low voltage side of the distribution transformer. The change of short circuit current of switch 2J at position 1, position 2, position 3 and position 6 of distributed photovoltaic are determined respectively.

[0134] Among them, the change in short-circuit current of the 2J switch in position 1 of the distributed photovoltaic system is:

[0135] ΔI PV =I PV (Z s / (Z s +Z 12 +Z 23 +Z 34 +Z 45 ))

[0136] The change in short-circuit current of the 2J switch when the distributed photovoltaic system is in position 2 or position 6 is:

[0137] ΔI PV =I PV ((Z s +Z 12 ) / (Z s +Z 12 +Z 23 +Z 34 +Z 45 ))

[0138] When the distributed photovoltaic system is at position 3, the change in short-circuit current of switch 2J is:

[0139] ΔI PV =I PV ((Z s +Z 12 +Z 23 ) / (Z s +Z 12 +Z 23 +Z 34 +Z 45 ));

[0140] In the above equations, ΔI PV I represents the change in short-circuit current due to the boosting effect. PV Z represents the short-circuit current provided by distributed photovoltaic power. s Z represents the equivalent impedance of the busbar. 12 Z represents the line impedance between position 1 and position 2. 23 Z represents the line impedance between positions 2 and 3. 34 Z represents the line impedance between positions 3 and 4. 45Z represents the line impedance between positions 4 and 5. T This indicates the short-circuit impedance of the distribution transformer.

[0141] As can be seen from the above analysis, when the distributed photovoltaic is at position 3, the change in short-circuit current assisted by the 2J switch is the largest, that is, the assisted effect is the largest. Therefore, position 3 can be regarded as the photovoltaic assisted aggregation point of this line.

[0142] Therefore, it can be inferred that when the protection device is a branch switch or a boundary switch, the distributed photovoltaic system of this line can be equivalent to the switch top.

[0143] When the protection device is an outgoing switch, the boosting effect of distributed photovoltaic power on the other lines of this busbar is independent of the photovoltaic connection location. Therefore, the lower end of the switch on the other lines of this busbar can be used as the boosting aggregation point.

[0144] S350. Based on the external pumping polymerization unit, the auxiliary polymerization unit, the equipment parameters, and the photovoltaic effect, modeling and tuning calculations are performed to determine whether there is a range of tuning values.

[0145] Specifically, the photovoltaic unit participating in the tuning calculation can be selected as an external absorption aggregation unit or an auxiliary aggregation unit based on the photovoltaic effect, so as to establish a tuning calculation model corresponding to the target distribution network, and perform tuning calculation on the tuning calculation model based on the equipment parameters to determine whether there is a range of tuning values.

[0146] In some embodiments of the present invention, modeling and tuning calculations are performed based on the external suction aggregation unit, the auxiliary aggregation unit, the equipment parameters, and the photovoltaic effect to determine whether there is a range of tuning values. This includes: modeling the distributed photovoltaic in the target distribution network based on the external suction aggregation unit, the auxiliary aggregation unit, the equipment parameters, and the photovoltaic effect to determine a constant current source aggregation model corresponding to the target distribution network; and performing tuning calculations based on the constant current source aggregation model to determine whether there is a range of tuning values.

[0147] For example, with Figure 6 Based on the schematic diagram of the active power distribution network shown, it is modeled using the constant current source aggregation modeling method described in this embodiment of the invention, as follows: Figure 10 As shown, Figure 10 This is a schematic diagram of an active distribution network aggregation model provided in Embodiment 3 of the present invention.

[0148] Specifically, I of line 1 PV11 I PV12 and I PV13 All of them are aggregated after the outgoing line switch XL1, and named I. PV1 The available photovoltaic capacity of Line 1 is aggregated after the outgoing switch XL1 and named I.PV1开放 Both serve as equivalent power sources for the boosting effect on other lines and equivalent power sources for the external suction effect of this line; I of line 2 PV21 I PV22 I PV23 and I PV24 All of them are aggregated after the outgoing line switch XL2, named I PV2 The available photovoltaic capacity of Line 2 is aggregated after the outgoing switch XL2 and named I. PV2开放 Both serve as equivalent power sources for the amplifying effect on other lines and equivalent power sources for the external suction effect of this line. The photovoltaic unit processing method for this line, oriented towards the calculation of short-circuit currents at branch and boundary switches, is based on... Figure 7 The branch switch FZ22 in the middle is explained, and the I of line 2 is connected. PV21 I PV22 I PV23 and I PV24 I PV2开放 All converge in front of branch switch FZ22, for I PV2 with I PV2开放 The sub-model of the sum, named I PV2(1) As the equivalent power source for the boosting effect of this line, it will power all the photovoltaic I connected to the branch switch FZ22. PV22 I PV23 Equivalent to the lower port of the switch, named I PV2(2) As an equivalent power source for external suction effect.

[0149] Among them, the part after the outgoing switch is the mother photovoltaic model, and the part before FZ22 is the daughter photovoltaic model, i.e., I. PV2(1) =I PV2 +I PV2开放 It can be automatically calculated when changes are made, and is equivalent to a sub-photovoltaic model.

[0150] It should be noted that, although I PV22 I PV23 This is not the equivalent power source for the boosting effect of branch switch FZ22, but for the sake of simplifying calculations, the equivalent power source for the boosting effect of all branches and boundary switches along the entire line is equivalent to I. PV2 with I PV2开放 The sum of these factors makes the calculation results more reliable. To improve modeling efficiency, the extremes of aggregation modeling can be appropriately reduced, such as omitting I during modeling. PV2(1) , will I PV2 and I PV2开放 As an equivalent power source for the boosting effect of the FZ22 circuit.

[0151] In this invention, an aggregated model can be built after the outgoing switch, and two aggregated models can be built before and after each branch or boundary switch. The auxiliary photovoltaic equivalent power supply or external photovoltaic equivalent power supply can be turned on automatically according to actual needs. The auxiliary photovoltaic equivalent power supply and the outgoing external photovoltaic equivalent power supply of the entire line can only maintain one parent photovoltaic model at the outgoing switch of the line, and the rest of the positions are its child photovoltaic models. If the photovoltaic capacity changes or there is a connecting line, only the parameters of the parent photovoltaic model are modified.

[0152] For example, Figure 11 This is a schematic diagram of another active distribution network aggregation model provided in Embodiment 3 of the present invention. Figure 11 As shown, I PV1 I PV2 For the parent photovoltaic model, I PV1(1) I PV2(1) This is a sub-photovoltaic model. If the calculation of the XL1 switch avoids the first tripable circuit breaker or the short-circuit current on the low-voltage side of the distribution transformer, then the other line bus model I is activated. PV2 If the sensitivity of the XL1 switch setting is calculated, then the bus model I of this line is turned on. PV1 If the short-circuit current on the low-voltage side of the FZ1 switch transformer is to be calculated, then the bus model I of other lines should be turned on. PV2 FZ1 Switch-Assisted Photovoltaic Equivalent Power Supply I PV1(1) If the sensitivity of the FZ1 switch setting is calculated, then the external photovoltaic equivalent power source I is turned on by the FZ1 switch. PV1(2) .

[0153] The above modeling method improves the reliability of branch and sectionalizing switches in avoiding faults on the low-voltage side of the distribution transformer. A qualitative analysis was conducted based on the photovoltaic-assisted external absorption mechanism and the distribution network protection setting principles. The assistance can expand the protection range, causing cascading trips. However, branch switches can ensure selectivity through time coordination with lower-level protection. The setting calculation principles allow the sectionalizing switch to trip and clear the fault when a fault occurs on the low-voltage side of the distribution transformer. Therefore, to improve modeling efficiency, the extremes of aggregated modeling can be appropriately reduced. The sub-photovoltaic model can be omitted during modeling, and the parent photovoltaic model can be used as the equivalent power source for the assistance effect of the branch and sectionalizing switches on this line.

[0154] S360. If not, the second modeling method is determined to be the constant current source fine modeling method, and the target distribution network is modeled and set based on the constant current source fine modeling method and the equipment parameters to determine whether there is a set value range.

[0155] S370. If it still does not exist, then the second modeling method is determined to be the controlled current source fine modeling method, and the target distribution network is modeled and set based on the controlled current source fine modeling method and the equipment parameters to determine whether there is a set value range.

[0156] This invention provides a method for setting calculation modeling of active distribution networks. This method constructs a setting calculation model through constant current source aggregation modeling, and uses the sum of the current grid-connected capacity and the available capacity of each line as the photovoltaic access capacity of the line. This avoids the problem of frequent setting value calculation caused by the successive access of photovoltaics, and improves the efficiency of setting calculation.

[0157] Example 4

[0158] Figure 12 This is a schematic diagram of the structure of an active distribution network setting calculation and modeling device provided in Embodiment 4 of the present invention. Figure 12 As shown, the device includes:

[0159] The parameter acquisition module 410 is used to acquire distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network.

[0160] The modeling method determination module 420 is used to determine a first modeling method based on the comparison result between the distributed photovoltaic access volume and the preset threshold, as well as the distributed photovoltaic parameter management data; wherein, the first modeling method includes a conventional load modeling method and a current source modeling method;

[0161] The modeling and setting calculation module 430 is used to determine a second modeling method according to a preset priority if the first modeling method is determined to be a current source modeling method, and to perform modeling and setting calculations on the target distribution network based on the second modeling method and the equipment parameters to determine the setting value range; wherein, the current source modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method, and the preset priority from low to high is the controlled current source fine modeling method, the constant current source fine modeling method, and the constant current source aggregation modeling method.

[0162] The advantage of this scheme is that by analyzing the distributed photovoltaic parameter management data and the amount of distributed photovoltaic access in the active distribution network, differentiated modeling of the active distribution network can be achieved, thus improving the adaptability of the settings.

[0163] Furthermore, the modeling method determination module 420 includes:

[0164] The current source modeling unit is used to determine the first modeling method as the current source modeling method if the distributed photovoltaic parameter management data meets the preset standard and the distributed photovoltaic access amount is greater than the preset threshold.

[0165] The conventional load modeling method unit is used to determine the first modeling method as the conventional load modeling method if otherwise.

[0166] Furthermore, the modeling and tuning calculation module 430 includes:

[0167] The constant current source aggregation modeling unit is used to model and set the target distribution network based on the constant current source aggregation modeling method and the equipment parameters if the second modeling method is determined to be the constant current source aggregation modeling method according to the preset priority, and to determine whether there is a set value range.

[0168] A constant current source fine modeling unit is used to determine the second modeling method as constant current source fine modeling method if it does not exist, and to perform modeling and setting calculations on the target distribution network based on the constant current source fine modeling method and the equipment parameters, and to determine whether there is a setting value range.

[0169] The controlled current source fine modeling unit is used to determine the second modeling method as the controlled current source fine modeling method if it still does not exist, and to perform modeling and setting calculations on the target distribution network based on the controlled current source fine modeling method and the equipment parameters, and to determine whether there is a setting value range.

[0170] Furthermore, the fine-scale modeling unit for constant current sources includes:

[0171] A fine model construction subunit for constant current source is used to model the target distribution network based on the fine modeling method of constant current source and the equipment parameters, so as to obtain a fine model of constant current source of the target distribution network.

[0172] The distributed photovoltaic unit determination sub-unit is used to determine the distributed photovoltaic units participating in the tuning calculation in the fine model of the constant current source based on the photovoltaic effect, fault point, and observation point.

[0173] The setting value range determination sub-unit is used to perform setting calculations based on the distributed photovoltaic units participating in the setting calculation and the equipment parameters, and to determine whether there is a setting value range.

[0174] Furthermore, the distributed photovoltaic unit defines sub-units, specifically for:

[0175] If the photovoltaic effect is an external suction effect, then the distributed photovoltaic unit between the fault point and the observation point is taken as the distributed photovoltaic unit participating in the tuning calculation;

[0176] If the photovoltaic effect is a boosting effect, then the distributed photovoltaic units outside the fault point and the observation point will be included as distributed photovoltaic units participating in the tuning calculation.

[0177] Furthermore, the controlled current source fine modeling method unit includes:

[0178] A controlled current source fine model construction subunit is used to model the target distribution network based on the controlled current source fine modeling method and the equipment parameters, so as to obtain the controlled current source fine model of the target distribution network;

[0179] The distributed photovoltaic unit determination sub-unit is used to determine the distributed photovoltaic units participating in the tuning calculation in the fine model of the controlled current source based on the photovoltaic effect, fault point, and observation point.

[0180] The setting value range determination sub-unit is used to perform setting calculations based on the distributed photovoltaic units participating in the setting calculation and the equipment parameters, and to determine whether there is a setting value range.

[0181] Furthermore, the distributed photovoltaic unit defines sub-units, specifically for:

[0182] If the photovoltaic effect is an external suction effect, then the distributed photovoltaic unit between the fault point and the observation point is taken as the distributed photovoltaic unit participating in the tuning calculation;

[0183] If the photovoltaic effect is a boosting effect, then the distributed photovoltaic units outside the fault point and the observation point will be included as distributed photovoltaic units participating in the tuning calculation.

[0184] Furthermore, the constant current source aggregation modeling unit includes:

[0185] The external water supply aggregation unit determines the sub-unit, which is used to analyze the external water supply effect of each switch in the target distribution network, determine the external water supply aggregation point of the distributed photovoltaic after each switch, and aggregate the distributed photovoltaic after each switch into an external water supply aggregation unit at each external water supply aggregation point;

[0186] The boosting aggregation unit determines the sub-unit, which is used to analyze the boosting effect of each switch in the target distribution network, determine the boosting aggregation point of the distributed photovoltaic in front of each switch, and aggregate the distributed photovoltaic in front of each switch into boosting aggregation units at each boosting aggregation point;

[0187] The setting value range determination sub-unit is used to perform modeling and setting calculations based on the external absorption polymerization unit, the auxiliary polymerization unit, the equipment parameters, and the photovoltaic effect to determine whether there is a setting value range.

[0188] Furthermore, the external pumping unit defines sub-units, specifically used for:

[0189] For each switch in the target distribution network, the change in external short-circuit current of each switch in the target distribution network at different access points is determined based on the short-circuit current provided by distributed photovoltaic, the equivalent impedance of the bus, the impedance between the distributed photovoltaic access point and the bus, the impedance between the distributed photovoltaic access point and the fault point, and the voltage of the distributed photovoltaic access point.

[0190] The connection point corresponding to the maximum change in external short-circuit current is taken as the external convergence point.

[0191] Furthermore, the connection point corresponding to the maximum change in external short-circuit current is taken as the external flow aggregation point, including:

[0192] If the switch is an outgoing switch, branch switch, or boundary switch, then the lower end of the switch is taken as the external water collection point.

[0193] Furthermore, the amplification unit determines the sub-units, specifically for:

[0194] For distributed photovoltaic systems on other lines, the downstream outlet of the outgoing switch on the other lines will be used as the aggregation point for the distributed photovoltaic systems on the other lines to the switch on this line.

[0195] For distributed photovoltaic (PV) systems on this line, the short-circuit current variation of the switch at different connection points is determined based on the short-circuit current provided by the distributed PV, the equivalent impedance of the bus, the impedance between the distributed PV connection point and the bus, and the impedance between the distributed PV connection point and the fault point. The connection point corresponding to the maximum variation of the short-circuit current is taken as the aggregation point.

[0196] Furthermore, the connection point corresponding to the maximum increase in short-circuit current change is designated as the aggregation point, including:

[0197] If the switch is an outgoing switch, then the lower end of the outgoing switch of the other lines of the busbar of this station shall be used as the photovoltaic boosting aggregation point of the other lines of the busbar of this station.

[0198] If the switch is a branch switch or a boundary switch, then the lower port of the outgoing switch of the other lines of the busbar of this station shall be used as the photovoltaic boosting aggregation point of the other lines of the busbar of this station, and the upper port of this switch shall be used as the photovoltaic boosting aggregation point of this line.

[0199] Furthermore, the range of setpoint values ​​determines the sub-unit, specifically used for:

[0200] Based on the external agglomeration unit, the auxiliary agglomeration unit, the equipment parameters, and the photovoltaic effect, the distributed photovoltaic in the target distribution network is modeled, and the constant current source aggregation model corresponding to the target distribution network is determined;

[0201] Based on the constant current source aggregation model, tuning calculations are performed to determine whether there is a range of tuning values.

[0202] Furthermore, the device also includes:

[0203] The conventional load modeling module is used to determine the first modeling method based on the comparison results of the distributed photovoltaic access volume and the preset threshold, as well as the distributed photovoltaic parameter management data. If the first modeling method is determined to be the conventional load modeling method, then each distributed photovoltaic in the target distribution network is taken as a load unit, and the target distribution network is modeled and set in combination with the equipment parameters to determine the range of set values.

[0204] The active power distribution network setting calculation modeling device in this embodiment of the invention can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0205] The active power distribution network setting calculation modeling device in this embodiment of the invention can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application does not specifically limit the specific operating system used.

[0206] The active distribution network setting calculation modeling device provided in this embodiment of the invention can realize each process implemented in the above method embodiment, and has the corresponding functional modules and beneficial effects of the execution method.

[0207] Example 5

[0208] Figure 13A schematic diagram of the structure of a device 10 that can be used to implement embodiments of this application is shown. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0209] like Figure 13 As shown, device 10 includes at least one processor 11 and a memory, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc., communicatively connected to at least one processor 11. The memory stores computer programs executable by at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of device 10. The processor 11, ROM 12, and RAM 13 are interconnected via bus 14. Input / output (I / O) interface 15 is also connected to bus 14.

[0210] Multiple components in device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0211] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the setting calculation modeling method for active power distribution networks.

[0212] In some embodiments, the active distribution network setting calculation modeling method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the active distribution network setting calculation modeling method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the active distribution network setting calculation modeling method by any other suitable means (e.g., by means of firmware).

[0213] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0214] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0215] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0216] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or haptic feedback); and input from the user can be received in any form (including sound input, voice input, or haptic input).

[0217] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0218] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0219] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0220] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for setting calculation and modeling of an active distribution network, characterized in that, The method includes: Acquire distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network; Based on the comparison results between the distributed photovoltaic access volume and the preset threshold, and the distributed photovoltaic parameter management data, a first modeling method is determined; wherein, the first modeling method includes a conventional load modeling method and a current source modeling method; If the first modeling method is determined to be a current source modeling method, then a second modeling method is determined according to a preset priority. Based on the second modeling method and the equipment parameters, the target distribution network is modeled and set, and the set value range is determined. The current source modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method. The preset priority, from low to high, is the controlled current source fine modeling method, the constant current source fine modeling method, and the constant current source aggregation modeling method.

2. The method according to claim 1, characterized in that, Based on the comparison between the distributed photovoltaic (PV) grid connection volume and the preset threshold, and the distributed PV parameter management data, a first modeling method is determined, including: If the distributed photovoltaic parameter management data meets the preset standard and the distributed photovoltaic access volume is greater than the preset threshold, then the first modeling method is determined to be the current source modeling method. Otherwise, the first modeling method is determined to be the conventional load modeling method.

3. The method according to claim 1, characterized in that, A second modeling method is determined according to a preset priority. Based on the second modeling method and the equipment parameters, the target distribution network is modeled and set, and the range of set values ​​is determined, including: If the second modeling method is determined to be the constant current source aggregation modeling method according to the preset priority, then the target distribution network is modeled and set based on the constant current source aggregation modeling method and the equipment parameters to determine whether there is a set value range. If not, the second modeling method is determined to be the constant current source fine modeling method, and the target distribution network is modeled and set based on the constant current source fine modeling method and the equipment parameters to determine whether there is a set value range. If it still does not exist, then the second modeling method is determined to be the controlled current source fine modeling method, and the target distribution network is modeled and set based on the controlled current source fine modeling method and the equipment parameters to determine whether there is a set value range.

4. The method according to claim 3, characterized in that, Based on the aforementioned constant current source fine modeling method and the aforementioned equipment parameters, the target distribution network is modeled and set to determine whether there exists a range of setpoint values, including: The target distribution network is modeled based on the constant current source fine modeling method and the equipment parameters to obtain the constant current source fine model of the target distribution network; Based on the photovoltaic effect, fault points, and observation points, the distributed photovoltaic units participating in the tuning calculation in the fine model of the constant current source are determined; Based on the distributed photovoltaic units participating in the tuning calculation and the equipment parameters, a tuning calculation is performed to determine whether there is a range of tuning values.

5. The method according to claim 4, characterized in that, Based on the photovoltaic effect, fault points, and observation points, the distributed photovoltaic units participating in the tuning calculation in the fine model of the constant current source are determined, including: If the photovoltaic effect is an external suction effect, then the distributed photovoltaic unit between the fault point and the observation point is taken as the distributed photovoltaic unit participating in the tuning calculation; If the photovoltaic effect is a boosting effect, then the distributed photovoltaic units outside the fault point and the observation point will be included as distributed photovoltaic units participating in the tuning calculation.

6. The method according to claim 3, characterized in that, Based on the controlled current source fine modeling method and the equipment parameters, the target distribution network is modeled and set to determine whether there is a range of setpoint values, including: The target distribution network is modeled based on the controlled current source fine modeling method and the equipment parameters to obtain the controlled current source fine model of the target distribution network; Based on the photovoltaic effect, fault point, and observation point, the distributed photovoltaic units participating in the tuning calculation in the fine model of the controlled current source are determined; Based on the distributed photovoltaic units participating in the tuning calculation and the equipment parameters, a tuning calculation is performed to determine whether there is a range of tuning values.

7. The method according to claim 6, characterized in that, Based on the photovoltaic effect, fault point, and observation point, the distributed photovoltaic units participating in the tuning calculation in the refined model of the controlled current source are determined, including: If the photovoltaic effect is an external suction effect, then the distributed photovoltaic unit between the fault point and the observation point is taken as the distributed photovoltaic unit participating in the tuning calculation; If the photovoltaic effect is a boosting effect, then the distributed photovoltaic units outside the fault point and the observation point will be included as distributed photovoltaic units participating in the tuning calculation.

8. The method according to claim 3, characterized in that, Based on the constant current source aggregation modeling method and the equipment parameters, the target distribution network is modeled and set to determine whether there is a range of setpoint values, including: The external water supply effect of each switch in the target distribution network is analyzed to determine the external water supply aggregation point of the distributed photovoltaic after each switch, and the distributed photovoltaic after each switch is aggregated into an external water supply aggregation unit at each external water supply aggregation point. The boosting effect of each switch in the target distribution network is analyzed to determine the boosting aggregation point of the distributed photovoltaic in front of each switch, and the distributed photovoltaic in front of each switch is aggregated into boosting aggregation unit at each boosting aggregation point; Modeling and tuning calculations are performed based on the external absorption polymerization unit, the auxiliary polymerization unit, the equipment parameters, and the photovoltaic effect to determine whether there is a range of tuning values.

9. The method according to claim 8, characterized in that, The external water supply effect of each switch in the target distribution network is analyzed to determine the external water supply aggregation point of distributed photovoltaic power generation after each switch, including: For each switch in the target distribution network, the change in external short-circuit current of each switch in the target distribution network at different access points is determined based on the short-circuit current provided by distributed photovoltaic, the equivalent impedance of the bus, the impedance between the distributed photovoltaic access point and the bus, the impedance between the distributed photovoltaic access point and the fault point, and the voltage of the distributed photovoltaic access point. The connection point corresponding to the maximum change in external short-circuit current is taken as the external convergence point.

10. The method according to claim 9, characterized in that, The connection point corresponding to the maximum change in external short-circuit current is taken as the external flow aggregation point, including: If the switch is an outgoing switch, branch switch, or boundary switch, then the lower end of the switch is taken as the external water collection point.

11. The method according to claim 8, characterized in that, The amplifying effect of each switch in the target distribution network is analyzed to determine the amplifying aggregation point of distributed photovoltaic power before each switch, including: For distributed photovoltaic systems on other lines, the downstream outlet of the outgoing switch on the other lines will be used as the aggregation point for the distributed photovoltaic systems on the other lines to the switch on this line. For distributed photovoltaic (PV) systems on this line, the short-circuit current variation of the switch at different connection points is determined based on the short-circuit current provided by the distributed PV, the equivalent impedance of the bus, the impedance between the distributed PV connection point and the bus, and the impedance between the distributed PV connection point and the fault point. The connection point corresponding to the maximum variation of the short-circuit current is taken as the aggregation point.

12. The method according to claim 11, characterized in that, The connection point corresponding to the maximum increase in short-circuit current change is taken as the aggregation point, including: If the switch is an outgoing switch, then the lower end of the outgoing switch of the other lines of the busbar of this station shall be used as the photovoltaic boosting aggregation point of the other lines of the busbar of this station. If the switch is a branch switch or a boundary switch, then the lower port of the outgoing switch of the other lines of the busbar of this station shall be used as the photovoltaic boosting aggregation point of the other lines of the busbar of this station, and the upper port of this switch shall be used as the photovoltaic boosting aggregation point of this line.

13. The method according to claim 8, characterized in that, Based on the external absorption polymerization unit, the auxiliary polymerization unit, the equipment parameters, and the photovoltaic effect, modeling and tuning calculations are performed to determine whether there is a range of tuning values, including: Based on the external agglomeration unit, the auxiliary agglomeration unit, the equipment parameters, and the photovoltaic effect, the distributed photovoltaic in the target distribution network is modeled, and the constant current source aggregation model corresponding to the target distribution network is determined; Based on the constant current source aggregation model, tuning calculations are performed to determine whether there is a range of tuning values.

14. The method according to claim 1, characterized in that, After determining the first modeling method based on the comparison result between the distributed photovoltaic grid connection volume and the preset threshold, and the distributed photovoltaic parameter management data, the method further includes: If the first modeling method is determined to be a conventional load modeling method, then each distributed photovoltaic unit in the target distribution network is taken as a load unit, and the target distribution network is modeled and set in combination with the equipment parameters to determine the range of set values.

15. A setting calculation and modeling apparatus for an active distribution network, used to implement the setting calculation and modeling method for an active distribution network as described in any one of claims 1-14, characterized in that, The device includes: The parameter acquisition module is used to acquire distributed photovoltaic parameter management data, distributed photovoltaic grid connection volume, and equipment parameters of the target distribution network. The modeling method determination module is used to determine a first modeling method based on the comparison result between the distributed photovoltaic access volume and the preset threshold, as well as the distributed photovoltaic parameter management data; wherein, the first modeling method includes a conventional load modeling method and a current source modeling method; The modeling and setting calculation module is used to determine a second modeling method according to a preset priority if the first modeling method is determined to be a current source modeling method, and to perform modeling and setting calculations on the target distribution network based on the second modeling method and the equipment parameters to determine the setting value range; wherein, the current source modeling method includes a controlled current source fine modeling method, a constant current source fine modeling method, and a constant current source aggregation modeling method; the preset priority from low to high is the controlled current source fine modeling method, the constant current source fine modeling method, and the constant current source aggregation modeling method.

16. An electronic device, characterized in that, The device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the setting calculation modeling method for an active distribution network as described in any one of claims 1-14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the setting calculation modeling method for the active power distribution network as described in any one of claims 1-14.

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