Power distribution network capacity planning method and device based on electric vehicle charging facility access

Abstract

The invention discloses a power distribution network capacity planning method and device based on electric vehicle charging facility access. The method comprises the following steps: acquiring available capacity of a power distribution network; acquiring the required capacity of the charging facility; target capacity parameters are determined based on the available capacity of the power distribution network and the required capacity of the charging facility, and the target capacity parameters comprise at least one of the following parameters: the remaining access capacity of the power grid and the newly increased capacity demand; and generating display information based on the target capacity parameter, the display information being used for displaying to the target object, and the display information being used for the target object to plan the power distribution network, thereby solving the technical problem that a power planning method in the prior art is difficult to meet requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of power planning, and in particular to a method and device for planning the capacity of a distribution network based on access of electric vehicle charging facilities. Background Art

[0002] As a low-carbon means of transportation in cities, electric vehicles are one of the important means to achieve "carbon peak" and "carbon neutrality". For the power grid, the large-scale access of electric vehicles can be regarded as an overall load, which has an overall impact on the power grid's ability to accept electric vehicles in terms of charging power and maximum load. With the large-scale and extensive access of electric vehicle charging facilities and the continuous development of related technologies, it brings major challenges to the planning of distribution network systems. The inaccurate prediction of charging load of charging facilities, unreasonable distribution and large-scale disorderly access seriously challenge the carrying capacity of distribution networks. To this end, it is necessary to study the distribution network capacity planning method for the access of electric vehicle charging facilities to realize the reasonable configuration of the distribution capacity of charging facilities to the power grid. In general, the research includes charging demand prediction of charging facilities, power balance analysis of power grids, information release, etc.

[0003] In terms of charging demand forecasting for charging facilities, combined with relevant planning data, the distribution network and charging facilities in the current year and the planned year are analyzed. Combined with the reported capacity of charging facilities, the charging facility practical coefficient table is referred to to obtain the charging demand forecast results. In terms of power grid power balance analysis, a distribution network model is built to calculate the remaining access capacity and new capacity demand of the power grid at the regional level, substation level, distribution transformer level, line level, and substation level. In terms of information release, the remaining access capacity of the power grid is released to the public to guide the investment layout of charging facilities, and the new capacity demand is released to planners to guide distribution network planning. In general, the existing power planning methods still have many shortcomings in the three aspects of charging demand forecasting for charging facilities, power grid power balance analysis, and information release.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiment of the present invention provides a method and device for planning the capacity of a distribution network based on access to electric vehicle charging facilities, so as to solve the technical problem that the power planning methods in the existing technology are difficult to meet the needs.

[0006] According to one aspect of an embodiment of the present invention, a method for planning the capacity of a distribution network based on access of electric vehicle charging facilities is provided, comprising: obtaining the available capacity of the distribution network; obtaining the required capacity of the charging facilities; determining target capacity parameters based on the available capacity of the distribution network and the required capacity of the charging facilities, wherein the target capacity parameters include at least one of the following: remaining access capacity of the power grid and new capacity demand; generating display information based on the target capacity parameters, the display information being used for displaying to a target object, and the display information being used for the target object to plan the distribution network.

[0007] Furthermore, before obtaining the available capacity of the distribution network, it also includes: obtaining the rated capacity of the distribution network and the occupied capacity of the distribution network; determining the available capacity of the distribution network based on the rated capacity of the distribution network and the occupied capacity of the distribution network, wherein the available capacity of the distribution network is used for, and the available capacity of the distribution network includes at least one of the following: the available capacity of the distribution network in the current year and the available capacity of the distribution network in the planned year.

[0008] Furthermore, before obtaining the demand capacity of the charging facilities, it includes: obtaining the reported capacity of the charging facilities, wherein the charging facilities include at least one of the following: public charging facilities, dedicated charging facilities and private charging facilities; determining the charging demand forecast result based on the reported capacity of the charging facilities; obtaining a typical daily grid supply load; and determining the demand capacity of the charging facilities based on the typical daily grid supply load and the charging demand forecast result.

[0009] Furthermore, obtaining the reported capacity of the charging facilities includes: obtaining the target reported capacity of the target level, wherein the target level includes at least one of the following: regional level, substation level, distribution transformer level, line level, and substation level, and the target reported capacity of each target level includes at least one of the following: the current annual reported capacity of charging facilities and the planned annual reported capacity of charging facilities; determining the reported capacity of the charging facilities based on the target reported capacity.

[0010] Furthermore, based on the reported capacity of the charging facilities, the charging demand forecast result is determined, including: obtaining the practical coefficient of the charging facilities, wherein the practical coefficient of the charging facilities includes at least one of the following: the practical coefficient of the public charging facilities, the practical coefficient of the dedicated charging facilities, and the practical coefficient of the private charging facilities; based on the reported capacity of the charging facilities and the practical coefficient of the charging facilities, the charging demand forecast result is determined.

[0011] Furthermore, before obtaining the practical coefficient of the charging facility, it includes: obtaining the reported capacity of the typical charging facility; obtaining the actual charging load of the typical charging facility; and determining the practical coefficient of the charging facility based on the reported capacity of the typical charging facility and the actual charging load of the typical charging facility.

[0012] Furthermore, display information is generated based on the target capacity parameter, including: when the target capacity parameter is determined to be the remaining access capacity of the power grid, obtaining the existing reported capacity of the charging facility; determining the remaining reported capacity of the charging facility based on the remaining access capacity of the power grid and the practical coefficient of the charging facility; determining the maximum reported capacity of the charging facility based on the existing reported capacity of the charging facility and the remaining reported capacity of the charging facility; determining the critical penetration rate in the display information based on the maximum reported capacity of the charging facility and the rated capacity of the distribution network.

[0013] According to another aspect of an embodiment of the present invention, a distribution network capacity planning device based on access of electric vehicle charging facilities is also provided, including: a first acquisition module, the first acquisition module is used to acquire the available capacity of the distribution network; a second acquisition module, the second acquisition module is used to acquire the required capacity of the target charging facility; a determination module, the determination module is used to determine the target capacity parameters based on the available capacity of the distribution network and the required capacity of the target charging facility, wherein the target capacity parameters include at least one of the following: the remaining access capacity of the power grid and the new capacity demand; a generation module, the generation module is used to generate display information based on the target capacity parameters, the display information is used to display to the target object, and the display information is used for the target object to plan the distribution network.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0016] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0017] In an embodiment of the present invention, the charging demand forecast result is determined by the reported capacity of the charging facility, and the target capacity parameters are determined according to the typical daily network load and the charging demand forecast result, and display information for the target object to plan the distribution network is generated based on the target capacity parameters, thereby improving the accuracy and efficiency of the coordinated planning of electric vehicle charging facilities and distribution networks, thereby solving the technical problem that the power planning method in the prior art is difficult to meet the demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 is a flow chart of an optional distribution network capacity planning method based on access of electric vehicle charging facilities according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of an optional distribution network capacity planning device based on access of electric vehicle charging facilities according to an embodiment of the present invention;

[0021] The accompanying drawings include the following reference numerals:

[0022] 61. A first acquisition module;

[0023] 62. Second acquisition module;

[0024] 63. Determine the module;

[0025] 64. Generate module. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present invention, an embodiment of a distribution network capacity planning method based on access of electric vehicle charging facilities is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0029] Figure 1 is a method according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0030] Step S100, obtaining the available capacity of the distribution network.

[0031] In step S100, it is necessary to collect existing data of the distribution network, including the rated capacity of the distribution network (i.e., the maximum power supply capacity of the distribution network when it is designed) and the occupied capacity of the distribution network, so as to calculate the available capacity of the distribution network in the current year and the planned year. The available capacity of the distribution network refers to the additional power capacity that the power grid can provide after meeting the existing load demand, which is crucial for evaluating whether the power grid is sufficient to support the access of charging facilities in the future.

[0032] Step S200, obtaining the required capacity of the charging facility.

[0033] In step S200, the reported capacity data of charging facilities is obtained, which includes the reported capacity of public, dedicated and private charging facilities, and the charging demand forecast results calculated by the practical coefficient table. The demand capacity of charging facilities reflects the charging load that electric vehicle users may generate in the future and is an important input data for power grid power balance analysis.

[0034] Step S300, determining a target capacity parameter based on the available capacity of the distribution network and the required capacity of the charging facility, wherein the target capacity parameter includes at least one of the following: remaining access capacity of the power grid and additional capacity demand.

[0035] In step S300, the target capacity parameters usually include the remaining access capacity of the power grid and the new capacity demand. The remaining access capacity of the power grid is the additional power capacity that the power grid can access after considering the maximum new network load on a typical day; the new capacity demand is the additional power supply capacity that the power grid needs to increase in order to meet future load growth. If the available capacity of the distribution network is greater than the required capacity of the charging facility, the remaining access capacity of the power grid is obtained; if the available capacity of the distribution network is less than the required capacity of the charging facility, the new capacity demand is obtained. These parameters are an important basis for distribution network planning. They help power grid operators and planners understand the current status and future needs of the power grid, so as to formulate reasonable expansion plans.

[0036] Step S400, generating display information based on the target capacity parameter, the display information is used to display to the target object, and the display information is used for the target object to plan the distribution network.

[0037] In step S400, this information includes the maximum reported capacity of the charging facility, the remaining access capacity of the power grid, the demand for new capacity, the current penetration rate, and the critical penetration rate, etc., which is intended to clearly show the current power carrying capacity of the power grid and future demand forecasts to target objects such as power grid enterprise planners, governments, and charging facility investment operators, so as to guide them in the reasonable layout of charging facilities and the planning of distribution networks. The generation and sharing of display information helps relevant parties make data-based decisions and improve the synergy between the power grid and charging facilities. This information will be provided to different target objects, such as charging facility investment operators and power grid enterprise planners. The display information can help these target objects understand the carrying capacity of the power grid, guide the reasonable layout of charging facilities and the planning and upgrading of distribution networks, so as to achieve the optimal allocation of power grid resources.

[0038] Through the above steps, the charging demand forecast result is determined by the reported capacity of the charging facilities, and the target capacity parameters are determined according to the typical daily network load and charging demand forecast results. Based on the target capacity parameters, display information for the target object to plan the distribution network is generated, which improves the accuracy and efficiency of the coordinated planning of electric vehicle charging facilities and distribution networks, thereby solving the technical problem that the power planning method in the prior art is difficult to meet the demand, and can effectively respond to the challenges brought by the access of electric vehicle charging facilities, and provide support for the sustainable development of the power grid.

[0039] Optionally, before obtaining the available capacity of the distribution network, the following steps are also included:

[0040] Step S101, obtaining the rated capacity and occupied capacity of the distribution network.

[0041] It should be noted that the rated capacity of the distribution network refers to the maximum amount of electricity that the distribution network supplies to users within a certain time frame, such as a day, a week or a month. The rated capacity of the distribution network is an important indicator for evaluating the carrying capacity of the power grid, which is usually based on historical load data and forecast analysis. The occupied capacity of the distribution network refers to the actual power capacity of the current distribution network, including the total capacity of power generation, transmission and substation equipment. The occupied capacity of the distribution network is the maximum power supply that the distribution network can provide, which determines the extent to which the power grid can handle the load.

[0042] Step S102, based on the rated capacity of the distribution network and the occupied capacity of the distribution network, determine the available capacity of the distribution network, wherein the available capacity of the distribution network includes at least one of the following: the available capacity of the distribution network in the current year and the available capacity of the distribution network in the planned year.

[0043] In step S102, the data of the rated capacity and the occupied capacity of the distribution network are collected, and the available capacity of the distribution network can be calculated. The calculation formula of the available capacity of the distribution network is:

[0044] The available capacity of the distribution network = the occupied capacity of the distribution network - the rated capacity of the distribution network.

[0045] This means the additional power capacity that the distribution network can still provide after handling the maximum load.

[0046] It should be noted that the current annual available capacity of the distribution network reflects the remaining capacity of the current power grid, while the planned annual available capacity of the distribution network takes into account future grid expansion, technological improvements and load forecasts to evaluate the power supply capacity of the power grid at the planning time.

[0047] Optionally, before obtaining the required capacity of the charging facility, including:

[0048] Step S210, obtaining the reported capacity of the charging facilities, wherein the charging facilities include at least one of the following: public charging facilities, dedicated charging facilities and private charging facilities.

[0049] In step S200, the system needs to collect and process the reported data of various charging facilities (including public, dedicated and private charging facilities), which is the basis for subsequent analysis. The reported capacity usually refers to the power capacity planned to be installed by the charging facilities in the power grid, which reflects the potential impact of the charging facilities on the power demand of the power grid.

[0050] Step S220, determining the charging demand prediction result based on the reported capacity of the charging facilities.

[0051] In step S220, the actual charging load is predicted using the reported capacity and utility coefficient of the charging facility. The utility coefficient reflects the usage of the charging facility at various times of the day and is the ratio of the actual charging load to the reported capacity. By analyzing historical data and charging behavior patterns, the utility coefficients of different types of charging facilities can be obtained. The charging demand forecast results should include load forecasts at the regional level, substation level, distribution transformer level, line level, and substation level to form a charging demand forecast curve.

[0052] Step S230, obtaining a typical daily grid load.

[0053] In step S230, the typical daily load curve can reflect the power demand under normal operation of the power grid, which is usually obtained through statistical analysis of historical data and corresponds to 24 points or 96 points in a day.

[0054] Step S240, determining the required capacity of the charging facilities based on the typical day grid load and charging demand forecast results.

[0055] In step S240, the typical daily grid load is superimposed on the charging demand forecast result to obtain the required capacity of the charging facility to analyze the impact of the electric vehicle charging load on the distribution network.

[0056] Through the above steps, the impact of electric vehicle charging facility access on the distribution network can be comprehensively and accurately analyzed, providing scientific guidance for the rational layout of charging infrastructure.

[0057] Optionally, obtain the reported capacity of the charging facility, including:

[0058] Step S211, obtain the target reported capacity of the target level, wherein the target level includes at least one of the following: regional level, substation level, distribution transformer level, line level, and substation level; the target reported capacity of each target level includes at least one of the following: the current annual charging facility reported capacity and the planned annual charging facility reported capacity.

[0059] In step S211, the charging facility installed capacity for the current year and the planned year is obtained for different target levels (such as regional level, substation level, distribution transformer level, line level, and substation level). The charging facility installed capacity for the current year reflects the capacity of the charging facilities that are currently planned or actually exist, while the charging facility installed capacity for the planned year is the charging facility capacity based on future forecasts and planning targets. The purpose of obtaining these data is to perform charging demand forecasting and grid capacity analysis in subsequent steps to ensure the precision and accuracy of the analysis.

[0060] Step S212, determining the reported capacity of the charging facility based on the target reported capacity.

[0061] In step S212, the specific charging facility capacity is determined based on the target capacity of the target level obtained in S110. This step may involve the collation, analysis and calculation of data to ensure that the capacity data is suitable for subsequent charging demand forecasts. For example, the system may need to aggregate or subdivide the capacity data based on the type (such as public, dedicated, private) and location of the charging facility to meet the analysis needs of different levels.

[0062] Through the above steps, it is ensured that the data used in charging facility capacity planning at different levels is the latest and most accurate, thereby improving the practicality and reliability of the entire capacity planning method. Through careful data analysis and processing, future charging demand can be predicted more accurately, providing strong support for distribution network planning.

[0063] Optionally, based on the reported capacity of the charging facilities, a charging demand forecast result is determined, including:

[0064] Step S221, obtaining a charging facility utility coefficient, wherein the charging facility utility coefficient includes at least one of the following: a public charging facility utility coefficient, a dedicated charging facility utility coefficient, and a private charging facility utility coefficient.

[0065] It should be noted that in step S221, the practical coefficient is the ratio of the actual charging load of the charging facility to the reported capacity, which reflects the actual use of the charging facility in various periods of the day. In this step, it is necessary to obtain the practical coefficients of different types of charging facilities (public, dedicated, private). These coefficients are usually determined based on historical data, user behavior analysis, and market forecasts. For example, public charging facilities may have a higher usage rate during peak hours in the morning and evening, while private charging facilities may have a higher usage rate at night. By analyzing these characteristics, a practical coefficient table can be established for each type of charging facility to more accurately predict charging demand.

[0066] Step S222, determining the charging demand forecast result based on the reported capacity of the charging facilities and the practical coefficient of the charging facilities.

[0067] In step S222, after obtaining the practical coefficient of the charging facility, the actual charging demand can be predicted in combination with the reported capacity of the charging facility. In this step, the reported capacity is multiplied by the practical coefficient to obtain the actual charging load of each type of charging facility at various times of the day. The system then adds up these load data to form a charging demand prediction curve. The curve should reflect the distribution of charging demand at different levels (such as regions, substations, distribution transformers, lines, and substations), which will help with subsequent grid power balance analysis and planning.

[0068] In the above steps, by combining the utility coefficient of charging facilities with the reported capacity, the present invention can more accurately predict the charging demand, thereby providing a scientific basis for the capacity planning of the power grid. This not only helps power grid operators to reasonably plan the power grid capacity and avoid the waste of power resources, but also guides charging facility investors to make reasonable layouts to meet future charging needs.

[0069] Optionally, before obtaining the utility coefficient of the charging facility, the following steps are included:

[0070] Step S201, obtaining the reported capacity of typical charging facilities.

[0071] In step S201, it is necessary to collect the reported capacity data of typical charging facilities. Typical charging facilities refer to charging facilities that are representative in a specific area or level. Their characteristics (such as type, specification, location, etc.) can reflect the general situation of charging facilities in the area or level. Typical charging facilities include at least one of the following: typical public charging facilities, typical dedicated charging facilities, and typical private charging facilities. The reported capacity refers to the power capacity applied for by the charging facility in the power grid, which is usually the maximum possible output power of the charging facility.

[0072] Step S202, obtaining the actual charging load of a typical charging facility.

[0073] In step S202, it is necessary to obtain the actual charging load data of these typical charging facilities over a period of time. The actual charging load refers to the power actually consumed by the charging facility at a specific time point, which is affected by multiple factors, including the charging behavior, charging time, and charging frequency of electric vehicle users. These data can be obtained through the operation records of the charging facilities, smart meter readings, or the power grid monitoring system.

[0074] Step S203, determining the utility coefficient of the charging facility based on the reported capacity of the typical charging facility and the actual charging load of the typical charging facility.

[0075] In step S203, the actual charging load of the typical charging facility is compared with the reported capacity to calculate the practical coefficient. The practical coefficient is the ratio of the actual charging load to the reported capacity, which can intuitively reflect the utilization efficiency and load characteristics of the charging facility. By analyzing historical data, the practical coefficient for each time point of the day (for example, 24 or 96 time points) can be obtained. These coefficients will be used for subsequent charging demand forecasts to help more accurately estimate the power demand of charging facilities at different times and levels.

[0076] In the above steps, the calculation of the utility coefficient is crucial for distribution network capacity planning, because it provides a quantitative indicator of the load characteristics of charging facilities, enabling grid operators and planners to more effectively predict and plan grid capacity based on the actual use of charging facilities to cope with the growth of electric vehicle charging demand. At the same time, by analyzing the utility coefficients of different types of charging facilities (such as public, dedicated, and private), we can better understand the charging behavior of different user groups and provide data support for the layout of charging facilities and the optimization of the power grid.

[0077] Optionally, the display information is generated based on the target capacity parameter, including:

[0078] Step S401, determining the current penetration rate in the display information based on the reported capacity of the charging facility and the rated capacity of the distribution network.

[0079] It should be noted that the penetration rate is a measure of the ratio of the installed capacity of charging facilities to the rated capacity of the distribution network, which indicates the degree to which charging facilities occupy the carrying capacity of the power grid. The formula for calculating the penetration rate is:

[0080] Penetration rate = reported capacity of charging facilities / rated capacity of distribution network.

[0081] In step S401, the reported capacity of the charging facility is first obtained, which is the power capacity applied for by the charging facility in the power grid, reflecting the expected power demand of the charging facility. Then the rated capacity of the distribution network is obtained, that is, the maximum power supply capacity when the distribution network is designed. This includes the comprehensive capacity of power generation, transmission and transformation, and is the upper limit of the power grid's ability to stably supply power. Finally, the above data is used to calculate the current penetration rate of the charging facility access to the distribution network. The calculation results of the current penetration rate will be used as part of the display information to help power grid planners and charging facility investors intuitively understand the degree of access to charging facilities and the possible impact on the power grid. Low penetration rate indicates that the power grid has a large margin for access to charging facilities, while high penetration rate may cause power supply shortages or power grid stability problems, and corresponding measures need to be taken for regulation. By calculating the penetration rate, the method of the present invention provides quantitative indicators to help decision makers conduct more accurate analysis and planning, ensure that the access of electric vehicle charging facilities not only meets user needs, but also does not threaten the normal operation of the power grid, and promotes the sustainable development of the power system.

[0082] Optionally, the display information is generated based on the target capacity parameter, including:

[0083] Step S410, when it is determined that the target capacity parameter is the remaining access capacity of the power grid, the existing reported capacity of the charging facility is obtained.

[0084] In step S410, the system collects the power capacity data of the current charging facilities that have been reported, which reflects the actual power demand of the charging facilities in the current power grid. The existing reported capacity is the basis for calculating the remaining access capacity of the power grid and planning the demand for additional capacity.

[0085] Step S420, determining the remaining reported capacity of the charging facility based on the remaining access capacity of the power grid and the practical coefficient of the charging facility.

[0086] In step S420, the system uses the remaining access capacity of the power grid (i.e., how much extra capacity the power grid has available for charging facilities) and the utility coefficient of charging facilities to calculate the charging facility capacity that the power grid can still accept, i.e., the remaining capacity. This step ensures that the capacity of the newly added charging facilities will not exceed the carrying capacity of the power grid.

[0087] Step S430, determining the maximum reported capacity of the charging facility based on the existing reported capacity of the charging facility and the remaining reported capacity of the charging facility.

[0088] It should be noted that the maximum capacity of charging facilities in a region or level can be obtained by adding the existing capacity of charging facilities to the remaining capacity. The maximum capacity is an important reference for the layout of charging facilities and grid planning, ensuring that the access of charging facilities in the future will not exceed the power supply capacity of the grid.

[0089] Step S440, determining the critical penetration rate in the display information based on the maximum reported capacity of the charging facility and the rated capacity of the distribution network.

[0090] It should be noted that the critical permeability indicates that when the installed capacity of charging facilities reaches a certain proportion of the power supply capacity of the power grid, the power grid begins to have carrying capacity problems. In step S440, the system determines the critical permeability by comparing the maximum installed capacity of the charging facilities with the rated capacity of the distribution network. The calculation of the critical permeability helps power grid planners identify the capacity bottleneck of the power grid and plan the expansion of the power grid in advance. It also provides charging facility investors with a quantitative indicator of the power grid carrying capacity to guide their investment decisions.

[0091] Through these steps, the present invention provides a systematic method to generate display information, including the remaining access capacity of the power grid, the maximum reported capacity of charging facilities, and the critical penetration rate, which are key parameters for evaluating the impact of electric vehicle charging facilities accessing the distribution network. These display information can help power grid planners and charging facility investors make more scientific and reasonable decisions.

[0092] Through the above steps, the carrying capacity of the distribution network at different time points can be accurately evaluated, which is crucial for predicting charging demand, analyzing grid power balance, and planning distribution network expansion in the subsequent steps. The calculated distribution network available capacity data will serve as the basis for the subsequent capacity planning process to ensure that the grid can safely and efficiently adapt to the access needs of future electric vehicle charging facilities.

[0093] Combination Figure 2 As shown, in a specific embodiment of the present invention, an embodiment of a distribution network capacity planning device is also provided. It should be noted that the device can be used to execute the above-mentioned distribution network capacity planning method.

[0094] Example 2

[0095] A distribution network capacity planning device based on access of electric vehicle charging facilities includes: a first acquisition module 61, a second acquisition module 62, a determination module 63 and a generation module 64, wherein the first acquisition module 61 is used to acquire the available capacity of the distribution network; the second acquisition module 62 is used to acquire the required capacity of the target charging facility; the determination module 63 is used to determine the target capacity parameters based on the available capacity of the distribution network and the required capacity of the target charging facility, wherein the target capacity parameters include at least one of the following: the remaining access capacity of the power grid and the additional capacity demand; the generation module 64 is used to generate display information based on the target capacity parameters, the display information is used to display to the target object, and the display information is used for the target object to plan the distribution network.

[0096] Example 3

[0097] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0098] Example 4

[0099] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0100] Example 5

[0101] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0102] Example 6

[0103] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0104] Example 7

[0105] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0106] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0107] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0110] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0112] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A distribution network capacity planning method based on access to electric vehicle charging facilities, characterized in that: include: Obtain the available capacity of the distribution network; Obtain the required capacity of charging facilities; Determine a target capacity parameter based on the available capacity of the distribution network and the required capacity of the charging facility, wherein the target capacity parameter includes at least one of the following: remaining access capacity of the power grid and additional capacity demand; Display information is generated based on the target capacity parameter, the display information is used to display to the target object, and the display information is used for the target object to plan the distribution network.

2. The method according to claim 1, characterized in that Before obtaining the available capacity of the distribution network, it includes: Obtain the rated capacity and occupied capacity of the distribution network; Based on the rated capacity of the distribution network and the occupied capacity of the distribution network, the available capacity of the distribution network is determined, wherein the available capacity of the distribution network includes at least one of the following: the available capacity of the distribution network in the current year and the available capacity of the distribution network in the planned year.

3. The method according to claim 1 or 2, characterized in that: Before obtaining the required capacity of the charging facility, including: Obtaining the reported capacity of a charging facility, wherein the charging facility includes at least one of the following: a public charging facility, a dedicated charging facility, and a private charging facility; Determining a charging demand forecast result based on the reported capacity of the charging facility; Obtain typical daily grid supply load; Based on the typical daily grid supply load and the charging demand forecast result, the required capacity of the charging facility is determined.

4. The method according to claim 3, characterized in that: Obtaining the reported capacity of the charging facility, including: Obtain the target installed capacity of the target level, wherein the target level includes at least one of the following: regional level, substation level, distribution transformer level, line level, and substation level, and the target installed capacity of each target level includes at least one of the following: the current annual charging facility installed capacity and the planned annual charging facility installed capacity; The reported capacity of the charging facility is determined based on the target reported capacity.

5. The method according to claim 3, characterized in that: Determining the charging demand forecast result based on the reported capacity of the charging facility includes: Obtaining a charging facility utility coefficient, wherein the charging facility utility coefficient includes at least one of the following: a public charging facility utility coefficient, a dedicated charging facility utility coefficient, and a private charging facility utility coefficient; The charging demand forecast result is determined based on the reported capacity of the charging facility and the practical coefficient of the charging facility.

6. The method according to claim 3, characterized in that: Before obtaining the utility coefficient of the charging facility, including: Obtain the reported capacity of typical charging facilities; Obtaining an actual charging load of the typical charging facility; The utility coefficient of the charging facility is determined based on the reported capacity of the typical charging facility and the actual charging load of the typical charging facility.

7. The method according to claim 5, characterized in that Generating the display information based on the target capacity parameter includes: When it is determined that the target capacity parameter is the remaining access capacity of the power grid, obtaining the existing reported capacity of the charging facility; Determining the remaining reported capacity of the charging facility based on the remaining access capacity of the power grid and the utility coefficient of the charging facility; Determining the maximum capacity of the charging facility based on the existing capacity of the charging facility and the remaining capacity of the charging facility; Based on the maximum reported capacity of the charging facility and the rated capacity of the distribution network, the critical penetration rate in the display information is determined.

8. A distribution network capacity planning device based on access to electric vehicle charging facilities, characterized in that: include: A first acquisition module, wherein the first acquisition module is used to acquire the available capacity of the distribution network; A second acquisition module, the second acquisition module is used to acquire the required capacity of the target charging facility; A determination module, the determination module is used to determine a target capacity parameter based on the available capacity of the distribution network and the required capacity of the target charging facility, wherein the target capacity parameter includes at least one of the following: a remaining access capacity of the power grid and a new capacity requirement; A generation module, the generation module is used to generate display information based on the target capacity parameter, the display information is used to display to the target object, and the display information is used for the target object to plan the distribution network.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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