Distributed photovoltaic power system bearing capacity evaluation method, equipment and medium

Abstract

The invention is suitable for the technical field of power simulation, and provides a distributed photovoltaic access power system bearing capacity evaluation method and device and a medium, and the method comprises the steps: building a power distribution network simulation model of a target region, and determining a boundary condition, comprise a voltage out-of-limit range, load rates of lines and transformer equipment, a photovoltaic output curve of a typical day and an electrical load curve; accessing distributed photovoltaic elements into the power distribution network simulation model, performing time sequence simulation in combination with boundary conditions, and determining a system-level bearing capacity interval of the target area; selecting a typical moment in the typical day, acquiring capacity and operation limit value parameters and power supply range power balance parameters of each transformer in the target area at the typical moment, and determining an equipment-level bearing capacity interval of each transformer; and based on the system-level bearing capacity interval and the equipment-level bearing capacity interval of each transformer, the power system bearing capacity interval of the target area is determined, and the comprehensiveness and accuracy of power system bearing capacity evaluation are improved.

Description

Technical Field

[0001] This application belongs to the field of power simulation technology, and in particular relates to a method, equipment and medium for assessing the carrying capacity of a power system connected to distributed photovoltaic power. Background Technology

[0002] In recent years, with the increasing shortage of fossil energy and the aggravation of environmental pollution problems, renewable energy has developed rapidly under the promotion of national policies and technological development. Distributed photovoltaics, as a load-side solar energy source, has the advantages of being clean, efficient, economical, and flexible, and has been widely used in power systems, exhibiting the characteristics of "numerous points and wide coverage, with local high-density grid connection".

[0003] The large-scale integration of distributed photovoltaic (PV) power into the grid has altered the characteristics of traditional distribution networks, transforming them from passive to active networks and changing power flow from unidirectional to bidirectional. This has brought numerous adverse effects on voltage, power quality, and planning schemes, seriously threatening the safe and stable operation of the distribution network. Furthermore, distributed PV is typically located at the end of the distribution network, with lower voltage levels, lower information access rates, and poor observability and measurability. For grid dispatching departments, the safety risks caused by its disorderly and uncontrolled integration are often unpredictable, potentially leading to major accidents such as power outages, damage to electrical components, and fires.

[0004] To ensure the healthy operation of the power grid system after the integration of distributed photovoltaic (PV) power, it is essential to assess the future capacity margin of each node based on the stable operating boundary and actual operating status of the distribution network, providing guidance for the planning and construction of the distribution network. However, traditional methods for assessing the carrying capacity of power systems with distributed PV often employ static analysis or simplified dynamic analysis methods, focusing only on the system-level carrying capacity assessment of a specific local power system. They fail to analyze the operating status of the power system, thus failing to reflect the intermittency and volatility of distributed PV output, limiting the comprehensiveness and accuracy of the assessment. Therefore, improving the accuracy and comprehensiveness of power system carrying capacity assessment for distributed photovoltaic power has become an urgent problem to be solved. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this application provides a method, device, and medium for assessing the carrying capacity of power systems connected to distributed photovoltaic (PV) systems, aiming to improve the accuracy and comprehensiveness of the assessment of the carrying capacity of power systems connected to distributed PV systems.

[0006] Firstly, this application provides a method for assessing the carrying capacity of a power system connected to distributed photovoltaic power, the method comprising: S1. Establish a simulation model of the distribution network in the target area and determine the boundary conditions, including the voltage over-limit range, the load rate of the line and transformer equipment, the photovoltaic power output curve and the electricity load curve of a typical day; S2, In the power distribution network simulation model, distributed photovoltaic elements are connected, and time-series simulation is performed in combination with the boundary conditions to determine the system-level carrying capacity range of the target area; S3, Select a typical time from the typical day, obtain the capacity and operating limit parameters and power balance parameters of each transformer in the target area at the typical time, and determine the equipment-level bearing capacity range of each transformer; S4. Based on the system-level carrying capacity range and the equipment-level carrying capacity range of each transformer, determine the power system carrying capacity range of the target area.

[0007] In one possible implementation, step S2 includes: S21, Select the access point of the distributed photovoltaic system in the power distribution network simulation model, and connect the distributed photovoltaic element at the access point; S22, input the photovoltaic output curve and the electricity load curve into the distribution network simulation model of the distributed photovoltaic element, and perform iterative time-series simulation with the voltage over-limit range and the load rate of the line and transformer equipment as constraints to determine the system-level carrying capacity range of the target area.

[0008] In one possible implementation, the steps of the iterative timing simulation include: S221, based on the initial available power generation of each of the distributed photovoltaics, the first time-series simulation of the distribution network simulation model connected to the distributed photovoltaic elements is performed using optimized power flow calculation to obtain the first actual power generation of each of the distributed photovoltaics; based on the first actual power generation of each of the distributed photovoltaics and the initial available power generation, the first new energy utilization rate of each of the distributed photovoltaics is calculated, and the first total new energy utilization rate of all distributed photovoltaics is determined. S222, based on the preset increase in available power generation, adjust the available power generation of each of the distributed photovoltaics to the second available power generation, and use optimized power flow calculation to perform a second time-series simulation on the distribution network simulation model connected to the distributed photovoltaic elements to obtain the second actual power generation of each of the distributed photovoltaics; based on the second actual power generation and the second available power generation of each of the distributed photovoltaics, calculate the second new energy utilization rate of each of the distributed photovoltaics, and determine the second total new energy utilization rate of all distributed photovoltaics; Repeat step S222 until the total renewable energy utilization rate equals the renewable energy utilization rate target value. Based on the sum of the available power generation of each of the distributed photovoltaics at this time, determine the system-level carrying capacity range.

[0009] In one possible implementation, the target value for renewable energy utilization includes a lower limit and an upper limit. Accordingly, the process of determining the system-level carrying capacity range based on the sum of the available power generation of each of the distributed photovoltaic systems until the total renewable energy utilization rate equals the target renewable energy utilization rate includes: When the total new energy utilization rate is equal to the upper limit of the new energy utilization rate target, the lower limit of the system-level carrying capacity is determined based on the sum of the available power generation of each of the distributed photovoltaics at this time. When the total new energy utilization rate is equal to the target lower limit of the new energy utilization rate, the upper limit of the system-level carrying capacity is determined based on the sum of the available power generation of each of the distributed photovoltaics at this time. The system-level bearing capacity range is determined based on the lower limit of the system-level bearing capacity and the upper limit of the system-level bearing capacity.

[0010] In one possible implementation, the capacity and operating limit parameters include rated capacity, power factor, and maximum reverse load rate; The power balance parameters within the power supply range include the power load within the power supply range, the sum of the output of other types of power sources within the power supply range excluding distributed photovoltaic power, the maximum output coefficient of distributed photovoltaic power within the power supply range, the charging power of the grid-connected flexible adjustment resources within the power supply range, and the range of expected new flexible adjustment resource installation capacity within the power supply range.

[0011] In one possible implementation, the step of determining the equipment-level load-bearing capacity range of each of the transformers includes: Based on the capacity and operating limit parameters of each transformer and the power balance parameters of the power supply range, the equipment-level bearing capacity range calculation formula is used to calculate the equipment-level bearing capacity range of each transformer. The formula for calculating the equipment-level bearing capacity range is: ; Among them, S d S represents the equipment-level load-bearing capacity range of any of the aforementioned transformers. d-min S represents the lower limit of the equipment-level load-bearing capacity range. d-max P represents the upper limit of the equipment-level load-bearing capacity range, and P represents the electrical load within the power supply range of the transformer at the typical moment. G S is the sum of the output of other types of power sources, excluding distributed photovoltaic power, within the power supply range of the transformer at the typical moment; S is the rated capacity of the transformer. The power factor of the transformer; This represents the maximum reverse load rate of the transformer. P represents the maximum output coefficient of distributed photovoltaic power within the power supply range of the transformer. E P represents the charging power of the grid-connected flexible adjustment resources within the transformer's power supply range at the typical moment. ESS-min P represents the lower limit of the expected increase in the installed capacity of flexible regulation resources within the transformer's power supply range at the typical time point. ESS-max This represents the upper limit of the expected new flexible adjustment resource installed capacity within the transformer's power supply range at the typical time.

[0012] In one possible implementation, step S4 includes: Based on the equipment-level bearing capacity range of each transformer, the sum of the equipment-level bearing capacity ranges of all transformers with a rated voltage of a preset value in the target area is calculated to obtain the total equipment-level bearing capacity range; The minimum value between the lower limit of the total equipment-level carrying capacity range and the lower limit of the system-level carrying capacity range is determined as the lower limit of the power system carrying capacity range. The minimum value between the upper limit of the total equipment-level carrying capacity range and the upper limit of the system-level carrying capacity range is determined as the upper limit of the power system carrying capacity range. The power system carrying capacity range is determined based on the lower limit and the upper limit of the power system carrying capacity range.

[0013] In one possible implementation, after step S4, the method further includes: Based on the power system carrying capacity range and the grid-connected distributed photovoltaic installed capacity in the target area, the range of distributed photovoltaic capacity that can be opened is determined; Based on the available distributed photovoltaic capacity range and the registered but not grid-connected distributed photovoltaic installed capacity in the target area, the available capacity range for registration of distributed photovoltaic is determined. Based on the available distributed photovoltaic capacity range and the available registered capacity range of the distributed photovoltaic system, the available capacity assessment result of the target area is determined.

[0014] In a second aspect, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in the first aspect or any of the implementations thereof.

[0015] Thirdly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in the first aspect or any of the implementations thereof.

[0016] Fourthly, this application provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any of the implementations thereof.

[0017] The advantages of this application compared to existing technologies are as follows: First, a distribution network simulation model of the target area is established, with voltage over-limit range, load rate of lines and transformer equipment, typical daily photovoltaic output curves, and electricity load curves defined as boundary conditions. After connecting distributed photovoltaic elements into the distribution network simulation model, time-series simulation is performed in conjunction with the boundary conditions to determine the system-level carrying capacity range of the target area. The photovoltaic output curve reflects the intermittency and volatility of photovoltaic power generation, and the electricity load curve reflects the dynamic changes in user-side electricity demand. Using the typical daily photovoltaic output curve and electricity load curve as the basic simulation data enables the analysis of the power system operating status, reflects the intermittency and volatility of distributed photovoltaic output, and improves the accuracy of the system-level carrying capacity range assessment of the target area. In addition, typical times are selected on typical days, and based on the capacity and operating limit parameters of each transformer in the target area and the power balance parameters of the power supply range at typical times, the equipment-level carrying capacity range of each transformer is determined. Then, based on the system-level carrying capacity range and the equipment-level carrying capacity range of each transformer, the power system carrying capacity range of the target area is determined. The power system carrying capacity of the target area is assessed at both the system level and the equipment level. Then, a second verification is performed based on the system-level assessment results and the equipment-level assessment results to obtain the final power system carrying capacity range. Compared with assessing only the system-level or equipment-level carrying capacity, this method improves the comprehensiveness and accuracy of the power system carrying capacity assessment.

[0018] It is understood that the electronic equipment, computer-readable storage medium, and computer program products provided in this application have the same beneficial effects as the power system carrying capacity assessment method for distributed photovoltaic power access described above, and will not be repeated here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of this application, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for assessing the carrying capacity of a power system connected to distributed photovoltaic power, provided in an embodiment of this application; Figure 2 A flowchart illustrating another method for assessing the carrying capacity of a power system with distributed photovoltaic power generation, provided in an embodiment of this application; Figure 3 This is a flowchart illustrating an iterative timing simulation step in a power system carrying capacity assessment method for distributed photovoltaic power systems provided in an embodiment of this application. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of this application. However, those skilled in the art will understand that this application may be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0025] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0027] For ease of understanding, the technical solution of this application will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 This is a flowchart illustrating a method for assessing the carrying capacity of a power system with distributed photovoltaic power generation, provided in one embodiment of this application. Figure 2 A flowchart illustrating another method for assessing the carrying capacity of a power system with distributed photovoltaic power generation, as provided in an embodiment of this application, is shown below. Figure 1 and Figure 2 As shown, for ease of explanation, only the parts relevant to this embodiment are shown. The method provided in this embodiment includes the following steps: S1. Establish a simulation model of the distribution network in the target area and determine the boundary conditions, including the voltage over-limit range, the load rate of the lines and transformer equipment, the photovoltaic power output curve and the electricity load curve of a typical day.

[0029] Optionally, a comprehensive collection of power source, grid, load, flexible regulation resources, and related operational data within the target area's power system is conducted to establish a unified data management platform. This platform supports importing data in various formats, such as Excel, CSV, JSON, CIME, and SCADA. The voltage exceedance range, line and transformer load rates, typical daily photovoltaic output curves, and load curves for the target area are defined as boundary conditions. Under normal operating conditions, the deviation between the actual voltage and rated voltage at distribution network nodes should generally not exceed ±7%. Exceeding this range is considered a voltage exceedance. Based on this, the voltage range exceeding ±7% of the rated voltage at each distribution network node is defined as the voltage exceedance range for each node. The load rate of lines and transformers should generally not exceed 80%.

[0030] Preferably, one day each from spring, summer, autumn, and winter is selected as a typical day, and the photovoltaic output curve and 24-hour electricity load curve for each typical day are collected as boundary conditions. The photovoltaic output curve reflects the intermittency and volatility of photovoltaic power generation, while the 24-hour electricity load curve reflects the dynamic changes in user-side electricity demand. The minimum dimension of both the photovoltaic output curve and the 24-hour electricity load curve is the hour.

[0031] Preferably, a combined modeling approach using geographic wiring diagrams and electrical single-line diagrams is employed to accurately and quickly build a distribution network simulation model for the target area. The Geographic Information System (GIS) locations of nodes such as substations, ring main units, and switch stations are stored using the standard GeoJson format, simulating the actual wiring routes and connecting the nodes on the map with lines. The electrical single-line diagram supports distribution network components such as transformers, buses, switches, reactive power compensation, loads, and generators. The drawing board supports drag-and-drop, copy, cut, delete, paste, undo, redo, zoom, pan, and rotate operations for modules, and provides common wiring templates in electrical systems, such as single bus, single bus section, double bus, and ring main unit (2 inputs and 8 outputs), enabling the rapid construction of typical single-line diagram models.

[0032] S2, In the distribution network simulation model, distributed photovoltaic elements are connected, and time-series simulation is performed in combination with boundary conditions to determine the system-level carrying capacity range of the target area.

[0033] In one possible implementation, step S2 may include: S21. Select the access point for distributed photovoltaic power in the power distribution network simulation model, and connect distributed photovoltaic elements at the access point.

[0034] Preferably, by combining the actual equipment locations in the geographical wiring diagram and electrical single-line diagram, the access points for distributed photovoltaic (PV) power generation are determined from the distribution network simulation model to ensure that the model is consistent with the actual distribution network topology. At the selected access points, PV simulation components are added and their parameters, such as efficiency, rated power, and initial available power generation, are set to clarify the PV access location, access voltage level, and connection relationship with the distribution network.

[0035] S22. Input the photovoltaic output curve and the electricity load curve into the distribution network simulation model connected to the distributed photovoltaic elements. Use the voltage over-limit range and the load rate of the line and transformer equipment as constraints to perform iterative time-series simulation and determine the system-level carrying capacity range of the target area.

[0036] Optionally, a time-series simulation can be used to recreate the dynamic operating scenario of a typical day. Power flow calculations can be optimized to ensure that operating constraints are met, with hourly time steps to simulate the distribution network operation status for each period of the day. Since photovoltaic output and electricity load vary over time, the power flow (e.g., direction of power transmission, power magnitude, etc.) differs for each period. Therefore, iterative power flow calculations must be performed at each time step to find the optimal power flow solution that satisfies the constraints.

[0037] As an example, the steps of iterative timing simulation may optionally include: S221. Based on the initial available power generation of each distributed photovoltaic (PV) unit, the first time-series simulation of the distribution network simulation model connected to the distributed PV units is performed using optimized power flow calculation to obtain the first actual power generation of each distributed PV unit. Based on the first actual power generation and initial available power generation of each distributed PV unit, the first new energy utilization rate of each distributed PV unit is calculated, and the first total new energy utilization rate of all distributed PV units is determined.

[0038] Optionally, under the premise of meeting the constraints, after the first time-series simulation, the simulation model outputs the optimized photovoltaic installed capacity of each distributed photovoltaic, that is, the first actual power generation; by calculating the ratio of the first actual power generation of each distributed photovoltaic to the initial available power generation, the first new energy utilization rate of each distributed photovoltaic is obtained, and the sum of the first new energy utilization rates of all distributed photovoltaics is taken as the first total new energy utilization rate.

[0039] S222, based on the preset increase in available power generation, adjust the available power generation of each distributed photovoltaic (PV) to the second available power generation, and use optimized power flow calculation to perform a second time-series simulation on the distribution network simulation model connected to the distributed PV elements to obtain the second actual power generation of each distributed PV; based on the second actual power generation and the second available power generation of each distributed PV, calculate the second new energy utilization rate of each distributed PV, and determine the second total new energy utilization rate of all distributed PVs.

[0040] Preferably, based on the ratio of the preset increase in available power generation to the initial available power generation of each distributed photovoltaic power generation, the available power generation of each distributed photovoltaic power generation is adjusted to the second available power generation; then, based on the same implementation method in the above steps, the second total renewable energy utilization rate is calculated.

[0041] For example, the preset increase in available power generation is 1 megawatt (MW), and the distribution network simulation model only has two photovoltaic (PV) units, denoted as PV1 and PV2. If the initial available power generation of PV1 is 1MW and the initial available power generation of PV2 is also 1MW, then in the second time-series simulation, both PV1 and PV2 will increase by 0.5MW based on the preset increase in available power generation, and their second available power generation will both be 1.5MW. If the initial available power generation of PV1 is 1MW and the initial available power generation of PV2 is 2MW, then based on the preset increase in available power generation, PV1 will increase by 0.33MW, and its second available power generation will be 1.33MW, while PV2 will increase by 0.67MW, and its second available power generation will be 1.67MW.

[0042] Repeat step S222 until the total renewable energy utilization rate equals the renewable energy utilization rate target value. Based on the sum of the available power generation of each distributed photovoltaic power generation at this time, determine the system-level carrying capacity range.

[0043] Optionally, the target value for renewable energy utilization rate is determined by relevant official documents. Typically, the target value is a range, including a lower limit and an upper limit. When the total renewable energy utilization rate equals the upper limit, the lower limit of the system-level carrying capacity is determined based on the sum of the available power generation of each distributed photovoltaic system at that time. Similarly, when the total renewable energy utilization rate equals the lower limit, the upper limit of the system-level carrying capacity is determined based on the sum of the available power generation of each distributed photovoltaic system at that time. Finally, the system-level carrying capacity range is determined based on both the lower and upper limits.

[0044] For example, as the installed capacity (i.e., available power generation) of each photovoltaic system increases, the utilization rate of new energy will decrease. The preferred lower limit for the target new energy utilization rate is 90%, and the preferred upper limit is 95%. The sum of available power generation from all distributed photovoltaic systems when the total new energy utilization rate first reaches 95% is taken as the lower limit of the system-level carrying capacity, and the sum of available power generation from all distributed photovoltaic systems when the total new energy utilization rate first reaches 90% is taken as the upper limit of the system-level carrying capacity.

[0045] As another example, such as Figure 3As shown, the steps of the iterative time-series simulation may optionally include: initially calculating the total renewable energy utilization rate based on the current distribution network simulation model; determining whether the current iteration number is greater than the preset iteration number; if the current iteration number is greater than the preset iteration number, further determining whether the total renewable energy utilization rate is equal to the renewable energy utilization rate target value; if yes, the simulation is complete, and the sum of the available power generation of each distributed photovoltaic power generation unit is recorded; if no, an error message is displayed indicating insufficient iterations; if the current iteration number is not greater than the preset iteration number, the available power generation of each distributed photovoltaic power generation unit is increased according to the step size, and the simulation calculation is performed again; determining whether the total renewable energy utilization rate is equal to the renewable energy utilization rate target value; if yes, the simulation is complete, and the sum of the available power generation of each distributed photovoltaic power generation unit is recorded; if no, the iteration number is incremented by 1, and the step of determining whether the current iteration number is greater than the preset iteration number is performed again.

[0046] S3. Select typical times in typical days, obtain the capacity and operating limit parameters of each transformer in the target area and the power balance parameters of the power supply range under typical times, and determine the equipment-level bearing capacity range of each transformer.

[0047] Optionally, 12:00-3:00 on each typical day can be used as the typical time. Different typical times can also be set according to different regions. This application does not limit this.

[0048] In one possible implementation, capacity and operating limit parameters include rated capacity, power factor, and maximum reverse load rate; power balance parameters for the power supply area include the power load within the power supply area, the sum of the output of other types of power sources within the power supply area excluding distributed photovoltaic, the maximum output coefficient of distributed photovoltaic within the power supply area, the charging power of the grid-connected flexible adjustment resources within the power supply area, and the range of expected new flexible adjustment resource installation capacity within the power supply area.

[0049] As an example, the steps for determining the equipment-level load-bearing capacity range of each transformer may optionally include: Based on the capacity and operating limit parameters of each transformer and the power balance parameters of the power supply range, the equipment-level bearing capacity range calculation formula is used to calculate the equipment-level bearing capacity range of each transformer. The equipment-level bearing capacity range calculation formula is as follows: ; Among them, S d S represents the equipment-level load-bearing capacity range of any transformer. d-min S represents the lower limit of the equipment-level load-bearing capacity range. d-max P represents the upper limit of the equipment-level load-bearing capacity range, where P is the electrical load within the transformer's power supply range at a typical moment. G S represents the sum of the output of all other types of power sources within the transformer's power supply range, excluding distributed photovoltaic power, at the same typical moment; S is the rated capacity of the transformer. This is the power factor of the transformer; This represents the maximum reverse load rate of the transformer. P represents the maximum output coefficient of distributed photovoltaic power within the power supply range of this transformer. E This refers to the charging power of the grid-connected flexible adjustment resources within the transformer's power supply range at the same typical moment; P ESS-min P represents the lower limit of the expected increase in the installed capacity of flexible regulation resources within the power supply range of the transformer at the same typical moment. ESS-max This represents the upper limit of the expected increase in the installed capacity of flexible regulation resources within the power supply range of the transformer at the same typical moment.

[0050] S4. Based on the system-level carrying capacity range and the equipment-level carrying capacity range of each transformer, determine the power system carrying capacity range of the target area.

[0051] In one possible implementation, step S4 may include: Based on the equipment-level bearing capacity range of each transformer, the sum of the equipment-level bearing capacity ranges of all transformers with a preset rated voltage in the target area is calculated to obtain the total equipment-level bearing capacity range. The minimum value between the lower limit of the total equipment-level carrying capacity range and the lower limit of the system-level carrying capacity range is determined as the lower limit of the power system carrying capacity range. The minimum value between the upper limit of the total equipment-level carrying capacity range and the upper limit of the system-level carrying capacity range is determined as the upper limit of the power system carrying capacity range. The power system carrying capacity range is determined based on the lower limit and upper limit of the power system carrying capacity range.

[0052] Optionally, the preset value is 220 kV or 330 kV. The sum of the equipment-level bearing capacity ranges of all transformers with rated voltages of the preset value in the target area is determined as the total equipment-level bearing capacity range.

[0053] As an example, following the principle of local subordination to the whole, when the upper limit of the system-level carrying capacity interval is less than the upper limit of the total equipment-level carrying capacity interval, and the lower limit of the system-level carrying capacity interval is less than the lower limit of the total equipment-level carrying capacity interval, the system-level carrying capacity interval shall be taken as the power system carrying capacity interval of the target area.

[0054] For example, based on the system-level carrying capacity range and the equipment-level carrying capacity range of each transformer, the power system carrying capacity range verification formula is used to determine the power system carrying capacity range of the target area; the power system carrying capacity range verification formula is: ; Where S is the power system carrying capacity range of the target area, S S-minS represents the lower limit of the system-level load-bearing capacity range. S-max S represents the upper limit of the system-level load-bearing capacity range. D-min S represents the lower limit of the total equipment-level load-bearing capacity range. D-max This represents the upper limit of the total equipment-level load-bearing capacity range.

[0055] In one possible implementation, such as Figure 2 As shown, after step S4, the method also includes openable capacity calculation and target area level assessment.

[0056] As an example, the steps for open capacity calculation and target area grading assessment may optionally include: Based on the power system carrying capacity range and the grid-connected distributed photovoltaic installed capacity in the target area, the range of distributed photovoltaic capacity that can be opened is determined; Based on the available capacity range for distributed photovoltaic (PV) and the registered but not grid-connected distributed PV installed capacity in the target area, the available capacity range for distributed PV to be registered is determined. Based on the available capacity range of distributed photovoltaic (PV) and the available registration capacity range of distributed PV, the assessment results of the available capacity of the target area are determined.

[0057] Optionally, based on the power system carrying capacity range and the already grid-connected distributed photovoltaic (PV) installed capacity in the target area, the openable distributed PV capacity range is determined using a calculation formula. The calculation formula for the openable distributed PV capacity range is as follows: ; Among them, C s1 S represents the range of open distributed photovoltaic capacity, where S is the power system carrying capacity range of the target area. s-con This refers to the grid-connected distributed photovoltaic installed capacity in the target area.

[0058] Optionally, based on the available distributed photovoltaic (PV) capacity range and the registered but not yet grid-connected distributed PV installed capacity in the target area, the available registration capacity range is determined using the formula for calculating the available registration capacity range. The formula for calculating the available registration capacity range is as follows: ; Among them, C s2 For the open filing capacity range of distributed photovoltaic power, S s-reg This refers to the registered but not yet grid-connected distributed photovoltaic installed capacity in the target area.

[0059] Optionally, based on the available distributed photovoltaic capacity range and the available registration capacity range for distributed photovoltaic, the assessment results, assessment basis, and assessment level classification of the target area's available capacity are shown in Table 1, where Cs1min To allow for the lower limit of the open distributed photovoltaic capacity range, C s2min This is the lower limit of the open filing capacity range for distributed photovoltaic power.

[0060] Table 1 The technical solution shown in the above example, which uses color to delineate the openable capacity levels of target areas, can provide a valid basis for governments, power grid companies, and investors to make decisions.

[0061] In addition, the system-level and equipment-level load-bearing capacity assessment processes for the target area can be as follows: Figure 2 The assessments can be conducted in parallel, or the system-level load-bearing capacity assessment can be performed first, followed by the equipment-level load-bearing capacity assessment; or the equipment-level load-bearing capacity assessment can be performed first, followed by the system-level load-bearing capacity assessment. This application does not limit the scope of the assessment.

[0062] The technical solution provided in this application firstly establishes a distribution network simulation model for the target area, defining voltage over-limit ranges, load rates of lines and transformers, typical daily photovoltaic output curves, and electricity load curves as boundary conditions. After integrating distributed photovoltaic (PV) components into the distribution network simulation model, time-series simulation is performed based on the boundary conditions to determine the system-level carrying capacity range of the target area. The PV output curve reflects the intermittency and volatility of PV power generation, while the electricity load curve reflects the dynamic changes in user-side electricity demand. Using the typical daily PV output curve and electricity load curve as the basic simulation data enables the analysis of the power system's operating status, reflects the intermittency and volatility of distributed PV output, and improves the accuracy of the system-level carrying capacity range assessment for the target area. Furthermore, typical times are selected on typical days, and based on the capacity and operating limit parameters of each transformer in the target area at those typical times, as well as the power balance parameters of the supply range, the equipment-level carrying capacity range of each transformer is determined. Finally, based on the system-level carrying capacity range and the equipment-level carrying capacity range of each transformer, the power system carrying capacity range of the target area is determined. The power system carrying capacity of the target area is assessed at both the system level and the equipment level. Then, a second verification is performed based on the system-level assessment results and the equipment-level assessment results to obtain the final power system carrying capacity range. Compared with assessing only the system-level or equipment-level carrying capacity, this method improves the comprehensiveness and accuracy of the power system carrying capacity assessment.

[0063] In addition, based on the technical solution provided in this application, relevant software will be developed to conduct regular distributed photovoltaic carrying capacity assessment and analysis based on annual, quarterly, and monthly operating data, and a series of measures will be taken: improving load development level, constructing pumped storage power stations, promoting the construction of new power storage and the flexibility transformation of thermal power plants; furthermore, through measures such as the construction of new power storage, boosting the connection of combined current and voltage, line switching, equipment expansion, and the installation of distributed photovoltaic joint switching safety control devices, the overall carrying capacity level of the region can be scientifically improved.

[0064] On the other hand, this application also provides a computer storage medium storing executable program code; the executable program code is used to execute any of the above-mentioned power system carrying capacity assessment methods for accessing distributed photovoltaic power.

[0065] On the other hand, this application also provides an electronic device, including a memory and a processor; the memory stores program code that can be executed by the processor; the program code is used to execute any of the above-mentioned power system carrying capacity assessment methods for accessing distributed photovoltaic power.

[0066] For example, the program code may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the program code in an electronic device.

[0067] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The electronic device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the electronic device may also include input / output devices, network access devices, buses, etc.

[0068] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0069] The memory can be an internal storage unit of the electronic device, such as a hard drive or RAM. It can also be an external storage device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both internal and external storage units. The memory is used to store the program code and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output.

[0070] The computer storage medium and electronic device described above are created based on the above method. Their technical functions and beneficial effects will not be elaborated here. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0071] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for assessing the carrying capacity of a power system with distributed photovoltaic power access, characterized in that, The method includes: S1. Establish a simulation model of the distribution network in the target area and determine the boundary conditions, including the voltage over-limit range, the load rate of the line and transformer equipment, the photovoltaic power output curve and the electricity load curve of a typical day; S2, In the power distribution network simulation model, distributed photovoltaic elements are connected, and time-series simulation is performed in combination with the boundary conditions to determine the system-level carrying capacity range of the target area; S3, Select a typical time from the typical day, obtain the capacity and operating limit parameters and power balance parameters of each transformer in the target area at the typical time, and determine the equipment-level bearing capacity range of each transformer; S4. Based on the system-level carrying capacity range and the equipment-level carrying capacity range of each transformer, determine the power system carrying capacity range of the target area.

2. The method according to claim 1, characterized in that, Step S2 includes: S21, Select the access point of the distributed photovoltaic system in the power distribution network simulation model, and connect the distributed photovoltaic element at the access point; S22, input the photovoltaic output curve and the electricity load curve into the distribution network simulation model of the distributed photovoltaic element, and perform iterative time-series simulation with the voltage over-limit range and the load rate of the line and transformer equipment as constraints to determine the system-level carrying capacity range of the target area.

3. The method according to claim 2, characterized in that, The steps of the iterative timing simulation include: S221, based on the initial available power generation of each of the distributed photovoltaics, the first time-series simulation of the distribution network simulation model connected to the distributed photovoltaic elements is performed using optimized power flow calculation to obtain the first actual power generation of each of the distributed photovoltaics; based on the first actual power generation of each of the distributed photovoltaics and the initial available power generation, the first new energy utilization rate of each of the distributed photovoltaics is calculated, and the first total new energy utilization rate of all distributed photovoltaics is determined. S222, based on the preset increase in available power generation, adjust the available power generation of each of the distributed photovoltaics to the second available power generation, and use optimized power flow calculation to perform a second time-series simulation on the distribution network simulation model connected to the distributed photovoltaic elements to obtain the second actual power generation of each of the distributed photovoltaics; based on the second actual power generation and the second available power generation of each of the distributed photovoltaics, calculate the second new energy utilization rate of each of the distributed photovoltaics, and determine the second total new energy utilization rate of all distributed photovoltaics; Repeat step S222 until the total renewable energy utilization rate equals the renewable energy utilization rate target value. Based on the sum of the available power generation of each of the distributed photovoltaics at this time, determine the system-level carrying capacity range.

4. The method according to claim 3, characterized in that, The target value for new energy utilization rate includes a lower limit and an upper limit. Accordingly, the process of determining the system-level carrying capacity range based on the sum of the available power generation of each of the distributed photovoltaic systems until the total renewable energy utilization rate equals the target renewable energy utilization rate includes: When the total new energy utilization rate is equal to the upper limit of the new energy utilization rate target, the lower limit of the system-level carrying capacity is determined based on the sum of the available power generation of each of the distributed photovoltaics at this time. When the total new energy utilization rate is equal to the target lower limit of the new energy utilization rate, the upper limit of the system-level carrying capacity is determined based on the sum of the available power generation of each of the distributed photovoltaics at this time. The system-level bearing capacity range is determined based on the lower limit of the system-level bearing capacity and the upper limit of the system-level bearing capacity.

5. The method according to claim 1, characterized in that, The capacity and operating limit parameters include rated capacity, power factor, and maximum reverse load rate; The power balance parameters within the power supply range include the power load within the power supply range, the sum of the output of other types of power sources within the power supply range excluding distributed photovoltaic power, the maximum output coefficient of distributed photovoltaic power within the power supply range, the charging power of the grid-connected flexible adjustment resources within the power supply range, and the range of expected new flexible adjustment resource installation capacity within the power supply range.

6. The method according to claim 5, characterized in that, The steps for determining the equipment-level load-bearing capacity range of each of the aforementioned transformers include: Based on the capacity and operating limit parameters of each transformer and the power balance parameters of the power supply range, the equipment-level bearing capacity range calculation formula is used to calculate the equipment-level bearing capacity range of each transformer. The formula for calculating the equipment-level bearing capacity range is: ； Among them, S d S represents the equipment-level load-bearing capacity range of any of the aforementioned transformers. d-min S represents the lower limit of the equipment-level load-bearing capacity range. d-max P represents the upper limit of the equipment-level load-bearing capacity range, and P represents the electrical load within the power supply range of the transformer at the typical moment. G S is the sum of the output of other types of power sources, excluding distributed photovoltaic power, within the power supply range of the transformer at the typical moment; S is the rated capacity of the transformer. The power factor of the transformer; This represents the maximum reverse load rate of the transformer. P represents the maximum output coefficient of distributed photovoltaic power within the power supply range of the transformer. E P represents the charging power of the grid-connected flexible adjustment resources within the transformer's power supply range at the typical moment. ESS-min P represents the lower limit of the expected increase in the installed capacity of flexible regulation resources within the transformer's power supply range at the typical time point. ESS-max This represents the upper limit of the expected new flexible adjustment resource installed capacity within the transformer's power supply range at the typical time.

7. The method according to claim 1, characterized in that, Step S4 includes: Based on the equipment-level bearing capacity range of each transformer, the sum of the equipment-level bearing capacity ranges of all transformers with a rated voltage of a preset value in the target area is calculated to obtain the total equipment-level bearing capacity range; The minimum value between the lower limit of the total equipment-level carrying capacity range and the lower limit of the system-level carrying capacity range is determined as the lower limit of the power system carrying capacity range. The minimum value between the upper limit of the total equipment-level carrying capacity range and the upper limit of the system-level carrying capacity range is determined as the upper limit of the power system carrying capacity range. The power system carrying capacity range is determined based on the lower limit and the upper limit of the power system carrying capacity range.

8. The method according to any one of claims 1 to 7, characterized in that, After step S4, the method further includes: Based on the power system carrying capacity range and the grid-connected distributed photovoltaic installed capacity in the target area, the range of distributed photovoltaic capacity that can be opened is determined; Based on the available distributed photovoltaic capacity range and the registered but not grid-connected distributed photovoltaic installed capacity in the target area, the available capacity range for registration of distributed photovoltaic is determined. Based on the available distributed photovoltaic capacity range and the available registered capacity range of the distributed photovoltaic system, the available capacity assessment result of the target area is determined.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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