A planning state power distribution network-oriented distributed photovoltaic macroscopic bearing capacity evaluation method and device

Abstract

This invention relates to the field of distributed photovoltaic (PV) carrying capacity assessment technology, specifically providing a method and apparatus for assessing the macroscopic carrying capacity of distributed PV in planned distribution networks. The method includes: determining the typical daily average reverse load rate of transformers and the average reverse power of transmission lines in the planned distribution network during the planning target year based on macroscopic basic data of the distribution network in the planned area during the planning target year; and assessing the macroscopic carrying capacity of distributed PV in the planned distribution network during the planning target year based on the typical daily average reverse load rate of transformers and the average reverse power of transmission lines. The technical solution provided by this invention effectively solves the problem of assessing the macroscopic carrying capacity of planned distribution networks to accommodate distributed PV, providing macroscopic decision support for distribution network development planning, and possessing strong guiding significance and operability.

Description

Technical Field

[0001] This invention relates to the field of distributed photovoltaic carrying capacity assessment technology, specifically to a method and apparatus for assessing the macroscopic carrying capacity of distributed photovoltaic power in planned distribution networks. Background Technology With the large-scale, high-density integration of distributed photovoltaic (PV) power into distribution networks, traditional passive distribution networks are rapidly evolving into active networks. This has led to increasingly prominent problems such as voltage exceeding limits, reverse power flow, protection malfunctions, and power quality deterioration. The early, extensive grid management model is no longer adequate to meet the dual demands of safety and grid absorption. There is an urgent need to establish a scientific, unified, and operable technical assessment and calculation mechanism to quantify the actual capacity of distribution networks to accommodate distributed PV. Against this backdrop, assessing and calculating the carrying capacity of distribution networks to accommodate distributed PV has become a crucial link and key foundational work in coordinating new energy development with grid security, supporting orderly grid connection, and precise planning. It not only avoids the passive situation of "connecting first, then managing," but also provides precise decision-making basis for grid planning, equipment upgrades, and flexible resource allocation, effectively balancing the contradiction between new energy development and grid security. Conducting relatively accurate and operable carrying capacity assessments and calculations has become a rigid requirement and core capability for power grid companies and energy authorities.

[0002] Current methods for assessing the carrying capacity of distributed photovoltaic (PV) power grids have evolved from early localized verification based primarily on static indicators such as equipment thermal stability and voltage deviation, to a comprehensive assessment system integrating system-level security, time-series dynamic characteristics, and multi-objective coordination. This aims to support improved renewable energy absorption capacity while ensuring grid security. Mainstream technical approaches include deterministic power flow methods, time-series simulation methods, optimization model methods, and machine learning methods, each suitable for application scenarios with varying accuracy and efficiency requirements, and each method has its own advantages and disadvantages. Deterministic power flow methods are computationally efficient and highly practical in engineering, but their results are conservative and ignore time-series fluctuations. Time-series simulation methods have clear physical meaning and can capture extreme events, but they rely on high-quality data and have a heavy computational burden. Optimization model methods can solve for the theoretical maximum grid connection capacity and support coordinated planning of measures, but the models are complex and difficult to solve. Machine learning methods have the potential for second-level evaluation and are suitable for online applications, but their interpretability is weak, and their generalization ability is limited by training data and network topology changes. Summary of the Invention

[0003] To overcome the above-mentioned shortcomings, this invention proposes a method and apparatus for assessing the macroscopic carrying capacity of distributed photovoltaic power grids under planned distribution conditions.

[0004] Firstly, a method for assessing the macro-carrying capacity of distributed photovoltaic (PV) power grids under planned distribution network conditions is provided, the method comprising: Based on the macro-basic data of the distribution network in the planning area in the planning target year, determine the predicted maximum output value of distributed photovoltaic power and the typical midday load of the distribution network in the planning area in the planning target year; Based on the predicted maximum output of distributed photovoltaic power in the planned area distribution network in the planning target year and the typical day midday load, the typical day distributed photovoltaic power back transmission power of the planned area distribution network in the planning target year is determined. Based on the typical daily distributed photovoltaic reverse power of the distribution network in the planning area during the planning target year, the average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planning area during the planning target year are determined. The macro-carrying capacity of distributed photovoltaic power in the planned area's distribution network in the planned target year is assessed based on the typical daily transformer average reverse load rate and line average reverse power in the planned target year.

[0005] Preferably, the predicted maximum output of distributed photovoltaic power in the planned area distribution network in the planning target year is equal to the predicted total installed capacity of distributed photovoltaic power in the planned area distribution network in the planning target year multiplied by the local maximum output coefficient of distributed photovoltaic power.

[0006] Preferably, the typical midday load of the distribution network in the planning area in the planning target year = (the predicted load of the distribution network in the planning area in the planning target year / the county-level load simultaneity rate) × the load rate of the distribution network in the planning area in the planning target year.

[0007] Preferably, the typical daily distributed photovoltaic back-feeding power of the distribution network in the planning area during the planning target year is equal to the predicted maximum output of distributed photovoltaic power in the planning area during the planning target year minus the midday load of the distribution network in the planning area during the planning target year.

[0008] Preferably, the average reverse load rate of transformers on a typical day in the planning target year of the distribution network in the planning area is equal to the distributed photovoltaic reverse power on a typical day in the planning target year of the distribution network in the planning area / the total planned capacity of transformers in the planning target year of the distribution network in the planning area.

[0009] Preferably, the average daily reverse power of the distribution network in the planning area during the planning target year is equal to the average daily distributed photovoltaic reverse power of the distribution network in the planning area during the planning target year, divided by the total number of planned lines in the distribution network in the planning area during the planning target year.

[0010] Preferably, the assessment of the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planning area in the planning target year includes: Determine whether the average reverse load rate of transformers or the average reverse power of lines in the planned area distribution network in the planning target year exceeds its corresponding preset threshold. If so, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is insufficient; otherwise, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is sufficient.

[0011] Furthermore, the threshold corresponding to the average reverse load rate of transformers on a typical day in the planned distribution network of the planning area in the planning target year is 80% of the rated capacity of transformers of the corresponding voltage level in the planned distribution network of the planning area, and the threshold corresponding to the average reverse load rate of lines on a typical day in the planned distribution network of the planning area in the planning target year is 80% of the rated capacity of lines of the corresponding voltage level in the planned distribution network of the planning area.

[0012] Preferably, after assessing the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year based on the average reverse load rate of transformers and the average reverse power of lines on a typical day in the planning target year, the process includes: Obtain the predicted growth step size of the distributed photovoltaic installed capacity that causes the load of the main equipment of the distribution network in the planning area to reach the limit, and use this value as the critical installed capacity for reverse heavy load of the distribution network in the planning area. The difference between the critical installed capacity of the distribution network under reverse heavy load in the planning area and the predicted installed capacity of distributed photovoltaic power in the planning area is used as the capacity margin of the distributed photovoltaic carrying capacity of the distribution network in the planning target year. Specifically, when the capacity margin of distributed photovoltaic power generation in the planned area is negative in the planning target year, its absolute value is taken as the limited capacity of the distributed photovoltaic power generation in the planned area in the planning target year.

[0013] Secondly, a distributed photovoltaic macro-carrying capacity assessment device for planned distribution networks is provided, the distributed photovoltaic macro-carrying capacity assessment device for planned distribution networks comprising: The first determining module is used to determine the predicted maximum output value of distributed photovoltaic power and typical midday load of the distribution network in the planning target year based on the macro-basic data of the distribution network in the planning area in the planning target year. The second determining module is used to determine the typical daily distributed photovoltaic back-feeding power of the distribution network in the planning target year based on the predicted maximum output value of the distributed photovoltaic power grid in the planning target year and the typical day midday load of the distribution network in the planning area. The third determining module is used to determine the average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planning area in the planning target year based on the typical daily distributed photovoltaic reverse power of the distribution network in the planning area in the planning target year. The evaluation module is used to evaluate the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planning target year.

[0014] Thirdly, a computer device is provided, comprising: one or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for assessing the macroscopic carrying capacity of distributed photovoltaic power for planned distribution networks is implemented.

[0015] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed, implements the aforementioned method for assessing the macroscopic carrying capacity of distributed photovoltaic power for planned distribution networks.

[0016] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: This invention provides a method and apparatus for assessing the macroscopic carrying capacity of distributed photovoltaic (PV) power in a planned distribution network, comprising: determining the predicted maximum output of PV power and the typical daytime load of the distribution network in the planned target year based on the macroscopic basic data of the distribution network in the planned area; determining the typical daytime PV reverse power of the distribution network in the planned target year based on the predicted maximum output of PV power and the typical daytime load of the distribution network in the planned area; determining the typical daytime average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planned target year based on the typical daytime PV reverse power of the distribution network in the planned area; and assessing the macroscopic carrying capacity of PV power in the planned area in the planned target year based on the typical daytime average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planned area. The technical solution provided by this invention starts from the actual working needs and conditions of distribution network planning. Utilizing limited information such as the predicted scale of the distribution network in the planning target year, the installed capacity of distributed photovoltaic (PV) power, and the predicted load of the distribution network, and combining this with the differentiated characteristics of the distribution network, an evaluation model is constructed from the perspective of source-grid-load coordination. Addressing the main contradictions, it performs macro-level calculations and analyses of the distributed PV carrying capacity during typical midday PV peak generation periods. This invention significantly reduces data requirements, eliminates complex modeling processes, and effectively solves the problem of assessing the macro-level carrying capacity of planned distribution networks to accommodate distributed PV power. It can provide macro-level decision support for distribution network development planning and has strong guiding significance and operability. Attached Figure Description

[0017] Figure 1This is a schematic diagram of the main steps of the distributed photovoltaic macro-carrying capacity assessment method for planned distribution networks according to an embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a distributed photovoltaic macroscopic carrying capacity assessment method for planned distribution networks, according to an embodiment of the present invention. Figure 1 As shown, the distributed photovoltaic macro-carrying capacity assessment method for planned distribution networks in this embodiment of the invention mainly includes the following steps: Step S101: Based on the macro-basic data of the distribution network in the planning area in the planning target year, determine the predicted maximum output of distributed photovoltaic power and the typical midday load of the distribution network in the planning area in the planning target year; Step S102: Determine the typical daily distributed photovoltaic power backflow of the distribution network in the planning area during the planning target year based on the predicted maximum output of distributed photovoltaic power in the planning target year and the typical midday load of the distribution network in the planning area; Step S103: Determine the average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planning area on a typical day in the planning target year, based on the typical daily distributed photovoltaic reverse power of the distribution network in the planning area in the planning target year; Step S104: Evaluate the macro-carrying capacity of distributed photovoltaic power in the planned area distribution network in the planned target year based on the typical daily transformer average reverse load rate and line average reverse power in the planned target year.

[0021] In this embodiment, the typical daytime load, typical day distributed photovoltaic reverse power, typical day transformer average reverse load rate and line average reverse power, and the objects of the distributed photovoltaic macro-carrying capacity assessment all refer to the distribution network of the planning area, and need to be calculated separately for voltage levels.

[0022] Since this invention is based on a general assessment of a small amount of macro data and does not involve detailed calculations, in order to focus on the main contradictions in the carrying capacity assessment, this invention focuses on the calculation of the transmission capacity of the main equipment. At the same time, it is assumed that the medium-voltage lines are all 10kV open-loop operation and the high-voltage lines are all 110kV and mainly connected to the terminal substations in open-loop operation.

[0023] In this embodiment, the macro-level basic data of the distribution network includes the projected total installed capacity of distributed photovoltaic power in the target year, the maximum output factor of local distributed photovoltaic power, the projected load of the distribution network in the target year, the load simultaneity rate of the local distribution network at the district / county level, the local distribution network load rate, the planned total capacity of transformers in the target year, and the planned total number of transmission lines in the target year. The latter five types of data need to be collected separately by voltage level.

[0024] In this embodiment, the predicted maximum output of distributed photovoltaic power in the planned area distribution network in the planning target year is equal to the predicted total installed capacity of distributed photovoltaic power in the planned area distribution network in the planning target year multiplied by the local maximum output coefficient of distributed photovoltaic power.

[0025] Among them, the maximum output coefficient of distributed photovoltaic is the ratio of maximum output to installed capacity, which is determined by local sunlight conditions and photovoltaic module performance. When taking the value, it is also necessary to take into account the local spring and autumn sunlight conditions, and it is slightly lower than the annual maximum output coefficient of distributed photovoltaic.

[0026] In this embodiment, the typical midday load of the planned area distribution network in the planning target year = (the predicted load value of the planned area distribution network in the planning target year / the county-level load simultaneity rate) × the load rate of the planned area distribution network in the planning target year.

[0027] Typical days generally refer to peak photovoltaic (PV) output days in spring and autumn. On these days, the load is low at midday, while distributed PV output is high, resulting in the largest difference between the two and the most significant potential for backfeeding. Therefore, only this period needs to be assessed. The midday load on typical days cannot be directly predicted, but it can be approximated as roughly equal to the annual average load of the target year. The annual average load can be calculated by multiplying the target year's distribution network load forecast by the load factor. The load forecast values ​​for each level of distribution network are already obtained during distribution network planning. The district / county level load simultaneity rate can be collected from local power grid companies. If the distribution network in the planning area is itself at the district / county level, this value is 1. The distribution network load factor is significantly affected by the nature of the load. Industrial loads with continuous and stable production (such as electrometallurgy and chemical industries) have higher daily load factors, typically above 0.9; single-shift industrial load factors are lower, while two-shift and three-shift industries are relatively higher (e.g., building materials and textile industries 0.8-0.9, machinery manufacturing 0.6-0.7). Urban residential electricity load factor is generally 0.3-0.4, transportation sector is about 0.4 (electrified railways can reach 0.7), while rural electricity load factor varies greatly depending on industrial shifts or seasonal agricultural irrigation and drainage (0.1-0.2 in winter, and above 0.9 in summer). The load factor of medium-voltage distribution networks is generally lower than that of high-voltage distribution networks.

[0028] In this embodiment, the typical daily distributed photovoltaic back-feeding power of the planned area distribution network in the planning target year = the predicted maximum output of distributed photovoltaic power of the planned area distribution network in the planning target year - the typical day midday load of the planned area distribution network in the planning target year.

[0029] In this embodiment, the average reverse load rate of transformers on a typical day in the planning target year of the planned area distribution network = the distributed photovoltaic reverse power on a typical day in the planning target year of the planned area distribution network / the total planned capacity of transformers in the planning target year of the planned area distribution network.

[0030] In this embodiment, the average daily reverse power of the distribution network in the planning area during the planning target year is equal to the average daily distributed photovoltaic reverse power of the distribution network in the planning area during the planning target year, divided by the total number of planned lines in the distribution network in the planning area during the planning target year.

[0031] In this embodiment, the assessment of the macro-carrying capacity of the distributed photovoltaic power grid in the planned area's distribution network during the planned target year, based on the typical daily transformer average reverse load rate and average line reverse power of the planned area's distribution network in the planned target year, includes: Determine whether the average reverse load rate of transformers or the average reverse power of lines in the planned area distribution network in the planning target year exceeds its corresponding preset threshold. If so, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is insufficient; otherwise, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is sufficient.

[0032] In one implementation, the threshold corresponding to the average reverse load rate of transformers on a typical day in the planned distribution network of the planning area in the planning target year is 80% of the rated capacity of transformers of the corresponding voltage level in the planned distribution network of the planning area, and the threshold corresponding to the average reverse load rate of lines on a typical day in the planned distribution network of the planning area in the planning target year is 80% of the rated capacity of lines of the corresponding voltage level in the planned distribution network of the planning area.

[0033] In this embodiment, the assessment of the macro-carrying capacity of the distributed photovoltaic power grid in the planned area during the planned target year, based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planned area during the planned target year, includes: Obtain the predicted growth step size of the distributed photovoltaic installed capacity that causes the load of the main equipment of the distribution network in the planning area to reach the limit, and use this value as the critical installed capacity for reverse heavy load of the distribution network in the planning area. The difference between the critical installed capacity of the distribution network under reverse heavy load in the planning area and the predicted installed capacity of distributed photovoltaic power in the planning area is used as the capacity margin of the distributed photovoltaic carrying capacity of the distribution network in the planning target year. Specifically, when the capacity margin of distributed photovoltaic power generation in the planned area is negative in the planning target year, its absolute value is taken as the limited capacity of the distributed photovoltaic power generation in the planned area in the planning target year.

[0034] In one specific implementation, a region is predicted to experience a significant increase in distributed photovoltaic (PV) installed capacity. The planning department has proposed two target annual installed capacity projections: 150 million kilowatts and 180 million kilowatts. The task is to assess and analyze whether the region's distribution network, in conjunction with the target annual distribution network construction scale, has the overall capacity to support the installed capacity under both projections, and to provide the distribution network carrying capacity margin for each projection.

[0035] (1) Set boundary conditions and collect macroeconomic basic data. Considering the region's new energy development, it is expected that the development of distributed photovoltaic (PV) power in rural areas will slow down, while the installed capacity in cities and industrial parks will gradually increase, but the total distribution will still be dominated by rural areas. Simultaneously, considering the region's power grid and load development, it is believed that the main contradiction in carrying capacity remains in rural areas, while cities and industrial parks do not have significant carrying capacity issues. Therefore, this calculation and analysis focuses on rural distribution networks (mainly C and D category power supply areas). Using information such as the region's distribution network scale, distributed PV installed capacity, and distribution network load forecasts for the target year, and combining the characteristics of rural distribution networks, a model is constructed from the perspective of source-grid-load coordination to calculate and analyze the carrying capacity of distributed PV power during typical midday peak PV generation periods.

[0036] To address the main challenges, the focus is on calculating the transmission capacity of the main equipment. It is assumed that all medium-voltage lines operate at 10kV open-loop, and all high-voltage lines operate at 110kV, primarily connecting to terminal substations in open-loop operation. The projected annual grid load for 110kV (66kV) and 10kV is 198 million kW and 208 million kW respectively, with rural areas accounting for approximately 50%, or about 99 million kW and 104 million kW respectively. At that time, approximately 80% of the total distributed photovoltaic capacity in the region will be located in rural areas, with approximately 375 million kVA of distribution transformer capacity, 38,000 medium-voltage lines, 286 million kVA of substation capacity, and 7,000 high-voltage lines.

[0037] The maximum output of photovoltaic power is typically at midday. Based on the method proposed in this invention, it is assumed that the midday load is approximately equal to the average load, and the accuracy fully meets the engineering calculation requirements for planning purposes. At this time, the backfeed situation is most prominent. The maximum output factor of distributed photovoltaic power is set to 0.9, the simultaneity rate of high and medium voltage loads relative to the total rural power grid load in the district / county is 0.77, and the high and medium voltage load rates are 0.4 and 0.3, respectively. The equipment is selected according to the Class C power supply area (see DL / T 5729-2023 "Technical Guidelines for Distribution Network Planning and Design").

[0038] (2) The maximum output of distributed photovoltaic power in the target year is shown in Table 1: Table 1

[0039] (3) The typical daytime load of the target year is shown in Table 2: Table 2

[0040] Table 3 shows the relevant data for the midday high-pressure load forecast on a typical day in the target year: Table 3

[0041] (4) The calculated reverse power for a typical day in the target year is shown in Table 4: Table 4

[0042] (5) The average reverse load rate of transformers and the average reverse power of the line on typical days of the target year are calculated as shown in Table 5: Table 5

[0043] (6) Assess the macroscopic carrying capacity of the distribution network Scenario 1 – Distributed photovoltaic installed capacity of 150 million kilowatts: The target annual maximum output of distributed photovoltaic (PV) power in rural areas is approximately 108 million kilowatts. Combining the maximum output of distributed PV with the midday loads of high and medium voltage lines, the reverse power transmission from high and medium voltage lines is estimated to be 57 million and 67 million kilowatts, respectively. Further analysis of the rural distribution network construction scale data for the target annual year yields an average reverse load rate of approximately 18% for distribution transformers, an average reverse power transmission from medium voltage lines of approximately 1799 kilowatts, an average reverse load rate from main transformers of approximately 19.8%, and an average reverse power transmission from high voltage lines of approximately 7920 kilowatts. Reverse power transmission will be common in distribution networks at all levels, but not under heavy load; the overall carrying capacity is sufficient to meet demand.

[0044] Scenario 2 – Distributed photovoltaic installed capacity of 180 million kilowatts: The target annual output of distributed photovoltaic (PV) power in rural areas is approximately 130 million kilowatts. Combining the maximum output of distributed PV with the midday loads of high and medium voltage lines, the reverse power transmission to high and medium voltage lines is estimated to be 78 million and 89 million kilowatts, respectively. Further analysis of the rural distribution network construction scale data for the target annual period yields an average reverse load rate of approximately 23.7% for distribution transformers, an average reverse power transmission to medium voltage lines of approximately 2374 kilowatts, an average reverse load rate to main transformers of approximately 27.4%, and an average reverse power transmission to high voltage lines of approximately 10944 kilowatts. Reverse power transmission will be common in distribution networks at all levels, but not under heavy load; the overall carrying capacity is sufficient to meet demand.

[0045] (7) Calculate the bearing capacity margin The installed capacity of distributed photovoltaic power generation is increased in increments until the load on the main equipment reaches the limit. The installed capacity at this point is the critical installed capacity for reverse overload.

[0046] The calculation results are shown in Table 6: Table 6

[0047] Calculations show that when the total installed capacity of distributed photovoltaic power in the region gradually increases to 350 million kilowatts in the target year, the medium-voltage source-load ratio (i.e., the ratio of installed capacity to maximum load) will be about 2. At that time, the average reverse power of medium-voltage lines will be about 5,637 kilowatts, which will be the first to reach 80% load rate of typical medium-voltage lines (selected according to a cross-section of 150mm2) in Class C power supply areas. This is the critical value for reverse heavy load. At this time, other major operating indicators are still within the limits. Therefore, 350 million kilowatts is the critical installed capacity for reverse heavy load of the distribution network in the region in the target year.

[0048] It is evident that while the overall carrying capacity of the regional distribution network can meet the demand under both predicted installed capacity scenarios, considering that various resources in the actual distribution network are not evenly distributed, there may be insufficient carrying capacity in some areas with relatively low construction standards, concentrated installed capacity, and low source-load matching. For example, when the medium-voltage source-load ratio reaches 2 in a local area, the medium-voltage distribution lines may be overloaded in reverse. Further calculations show that under the predicted installed capacity of 150 million kilowatts, the carrying capacity margin of the regional distribution network for distributed photovoltaic power is approximately (350-150) = 200 million kilowatts; under the predicted installed capacity of 180 million kilowatts, the carrying capacity margin of the regional distribution network for distributed photovoltaic power is approximately (350-180) = 170 million kilowatts.

[0049] Example 2 Based on the same inventive concept, this invention also provides a distributed photovoltaic macro-carrying capacity assessment device for planned distribution networks, the distributed photovoltaic macro-carrying capacity assessment device for planned distribution networks comprising: The first determining module is used to determine the predicted maximum output value of distributed photovoltaic power and typical midday load of the distribution network in the planning target year based on the macro-basic data of the distribution network in the planning area in the planning target year. The second determining module is used to determine the typical daily distributed photovoltaic back-feeding power of the distribution network in the planning target year based on the predicted maximum output value of the distributed photovoltaic power grid in the planning target year and the typical day midday load of the distribution network in the planning area. The third determining module is used to determine the average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planning area in the planning target year based on the typical daily distributed photovoltaic reverse power of the distribution network in the planning area in the planning target year. The evaluation module is used to evaluate the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planning target year.

[0050] Preferably, the predicted maximum output of distributed photovoltaic power in the planned area distribution network in the planning target year is equal to the predicted total installed capacity of distributed photovoltaic power in the planned area distribution network in the planning target year multiplied by the local maximum output coefficient of distributed photovoltaic power.

[0051] Preferably, the typical midday load of the distribution network in the planning area in the planning target year = (the predicted load of the distribution network in the planning area in the planning target year / the county-level load simultaneity rate) × the load rate of the distribution network in the planning area in the planning target year.

[0052] Preferably, the typical daily distributed photovoltaic back-feeding power of the distribution network in the planning area during the planning target year is equal to the predicted maximum output of distributed photovoltaic power in the planning area during the planning target year minus the midday load of the distribution network in the planning area during the planning target year.

[0053] Preferably, the average reverse load rate of transformers on a typical day in the planning target year of the distribution network in the planning area is equal to the distributed photovoltaic reverse power on a typical day in the planning target year of the distribution network in the planning area / the total planned capacity of transformers in the planning target year of the distribution network in the planning area.

[0054] Preferably, the average daily reverse power of the distribution network in the planning area during the planning target year is equal to the average daily distributed photovoltaic reverse power of the distribution network in the planning area during the planning target year, divided by the total number of planned lines in the distribution network in the planning area during the planning target year.

[0055] Preferably, the assessment of the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planning area in the planning target year includes: Determine whether the average reverse load rate of transformers or the average reverse power of lines in the planned area distribution network in the planning target year exceeds its corresponding preset threshold. If so, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is insufficient; otherwise, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is sufficient.

[0056] Furthermore, the threshold corresponding to the average reverse load rate of transformers on a typical day in the planned distribution network of the planning area in the planning target year is 80% of the rated capacity of transformers of the corresponding voltage level in the planned distribution network of the planning area, and the threshold corresponding to the average reverse load rate of lines on a typical day in the planned distribution network of the planning area in the planning target year is 80% of the rated capacity of lines of the corresponding voltage level in the planned distribution network of the planning area.

[0057] Preferably, after assessing the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year based on the average reverse load rate of transformers and the average reverse power of lines on a typical day in the planning target year, the process includes: Obtain the predicted growth step size of the distributed photovoltaic installed capacity that causes the load of the main equipment of the distribution network in the planning area to reach the limit, and use this value as the critical installed capacity for reverse heavy load of the distribution network in the planning area. The difference between the critical installed capacity of the distribution network under reverse heavy load in the planning area and the predicted installed capacity of distributed photovoltaic power in the planning area is used as the capacity margin of the distributed photovoltaic carrying capacity of the distribution network in the planning target year. Specifically, when the capacity margin of distributed photovoltaic power generation in the planned area is negative in the planning target year, its absolute value is taken as the limited capacity of the distributed photovoltaic power generation in the planned area in the planning target year.

[0058] Example 3 Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may 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. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby realizing the steps of the distributed photovoltaic macro-carrying capacity assessment method for planned distribution networks described in the above embodiments.

[0059] Example 4 Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the distributed photovoltaic macro-carrying capacity assessment method for planned distribution networks described in the above embodiments.

[0060] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the macroscopic carrying capacity of distributed photovoltaic power in planned distribution networks, characterized in that, The method includes: Based on the macro-basic data of the distribution network in the planning area in the planning target year, determine the predicted maximum output value of distributed photovoltaic power and the typical midday load of the distribution network in the planning area in the planning target year; Based on the predicted maximum output of distributed photovoltaic power in the planned area distribution network in the planning target year and the typical day midday load, the typical day distributed photovoltaic power back transmission power of the planned area distribution network in the planning target year is determined. Based on the typical daily distributed photovoltaic reverse power of the distribution network in the planning area during the planning target year, the average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planning area during the planning target year are determined. The macro-carrying capacity of distributed photovoltaic power in the planned area's distribution network in the planned target year is assessed based on the typical daily transformer average reverse load rate and line average reverse power in the planned target year.

2. The method as described in claim 1, characterized in that, The predicted maximum output of distributed photovoltaic power in the planned area distribution network in the planning target year = the predicted total installed capacity of distributed photovoltaic power in the planned area distribution network in the planning target year × the local maximum output coefficient of distributed photovoltaic power.

3. The method as described in claim 1, characterized in that, The typical midday load of the distribution network in the planned area in the planning target year = (the predicted load of the distribution network in the planned area in the planning target year / the county-level load simultaneity rate) × the load rate of the distribution network in the planned area in the planning target year.

4. The method as described in claim 1, characterized in that, The typical daily distributed photovoltaic back-feeding power of the distribution network in the planned area during the planning target year is equal to the predicted maximum output of distributed photovoltaic power in the planned area during the planning target year minus the midday load of the distribution network in the planned area during the planning target year.

5. The method as described in claim 1, characterized in that, The average reverse load rate of transformers on a typical day in the planned target year of the distribution network in the planned area = the distributed photovoltaic reverse power on a typical day in the planned target year of the distribution network in the planned area / the total planned capacity of transformers in the planned target year of the distribution network in the planned area.

6. The method as described in claim 1, characterized in that, The average daily reverse power of the distribution network in the planned area during the planning target year is calculated as: average daily distributed photovoltaic reverse power of the distribution network in the planned area during the planning target year / total number of planned distribution network lines in the planned area during the planning target year.

7. The method as described in claim 1, characterized in that, The assessment of the macro-carrying capacity of the distributed photovoltaic power grid in the planned area during the planned target year, based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planned area, includes: Determine whether the average reverse load rate of transformers or the average reverse power of lines in the planned area distribution network in the planning target year exceeds its corresponding preset threshold. If so, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is insufficient; otherwise, determine that the distributed photovoltaic macro carrying capacity of the planned area distribution network in the planning target year is sufficient.

8. The method as described in claim 7, characterized in that, The threshold corresponding to the average reverse load rate of transformers on a typical day in the planned distribution network of the planning area during the planning target year is 80% of the rated capacity of transformers of the corresponding voltage level in the planned distribution network of the planning area, and the threshold corresponding to the average reverse load rate of lines on a typical day in the planned distribution network of the planning area during the planning target year is 80% of the rated capacity of lines of the corresponding voltage level in the planned distribution network of the planning area.

9. The method as described in claim 1, characterized in that, The assessment of the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year, based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planning area in the planning target year, includes: Obtain the predicted growth step size of the distributed photovoltaic installed capacity that causes the load of the main equipment of the distribution network in the planning area to reach the limit, and use this value as the critical installed capacity for reverse heavy load of the distribution network in the planning area. The difference between the critical installed capacity of the distribution network under reverse heavy load in the planning area and the predicted installed capacity of distributed photovoltaic power in the planning area is used as the capacity margin of the distributed photovoltaic carrying capacity of the distribution network in the planning target year. Specifically, when the capacity margin of distributed photovoltaic power generation in the planned area is negative in the planning target year, its absolute value is taken as the limited capacity of the distributed photovoltaic power generation in the planned area in the planning target year.

10. An apparatus for assessing the macroscopic carrying capacity of distributed photovoltaic power generation in a planned distribution network according to any one of claims 1-9, characterized in that, The device includes: The first determining module is used to determine the predicted maximum output value of distributed photovoltaic power and typical midday load of the distribution network in the planning target year based on the macro-basic data of the distribution network in the planning area in the planning target year. The second determining module is used to determine the typical daily distributed photovoltaic back-feeding power of the distribution network in the planning target year based on the predicted maximum output value of the distributed photovoltaic power grid in the planning target year and the typical day midday load of the distribution network in the planning area. The third determining module is used to determine the average reverse load rate of transformers and the average reverse power of lines of the distribution network in the planning area in the planning target year based on the typical daily distributed photovoltaic reverse power of the distribution network in the planning area in the planning target year. The evaluation module is used to evaluate the macro-carrying capacity of the distributed photovoltaic power grid in the planning target year based on the typical daily transformer average reverse load rate and line average reverse power of the distribution network in the planning target year.

11. A computer device, characterized in that, include: One or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the distributed photovoltaic macro-carrying capacity assessment method for planned distribution networks as described in any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the distributed photovoltaic macro-carrying capacity assessment method for planned distribution networks as described in any one of claims 1 to 9.

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