A configuration method and device of a power generation assembly, electronic equipment and storage medium

Abstract

The application discloses a configuration method and device of a power generation assembly, electronic equipment and a storage medium. The method comprises the following steps: acquiring first resource data and power distribution network information data in a target installation area; determining first installation data that can be accommodated by the target installation area for the power generation assembly based on the first resource data; determining second installation data that can be accommodated by a power distribution network for the power generation assembly according to the power distribution network information data and pre-established correction data; determining target installation data of the target installation area based on the first installation data and the second installation data; and determining a target configuration result in the target installation area according to the target installation data. The application can more accurately determine installation data of the power generation assembly in the area, thereby ensuring the safety of the power distribution network after the power generation assembly is configured.

Description

Technical Field

[0001] This invention relates to the field of power distribution network safety technology, and in particular to a method, apparatus, electronic device, and storage medium for configuring power generation components. Background Technology

[0002] With the advancement of carbon peaking and carbon neutrality goals, the penetration rate of various power generation components (such as distributed photovoltaic power generation) in the power distribution network is rapidly increasing. How to rationally allocate power generation components within a region is crucial for the region's rational planning and the safe and stable operation of the power distribution network.

[0003] Currently, when configuring power generation components within a region, it is necessary to first determine the installable data for those components within that region. In existing technologies, the method for determining this installable data typically involves determining the maximum number of components that can be installed in that region by considering the roof area resources of all buildings within that region. This maximum number serves as the installation data for that region, thus enabling the configuration of power generation components within that region.

[0004] However, the above method has low accuracy in determining installation data and does not match the actual installation data in the area, resulting in low security of the distribution network in the area after the power generation components are configured. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for configuring power generation components, which can more accurately determine the installation data of power generation components in a region, thereby ensuring the safety of the power distribution network after the power generation components are configured.

[0006] According to a first aspect of the present invention, a method for configuring a power generation component is provided, comprising: Acquire primary resource data and power distribution network information data within the target installation area; Based on the first resource data, determine the first installation data that the target installation area can accommodate for the power generation component; Based on the power distribution network information data and the pre-established correction data, determine the second installation data that the power distribution network can accommodate for the power generation components; Based on the first installation data and the second installation data, the target installation data for the target installation area is determined; Based on the target installation data, the target configuration result within the target installation area is determined.

[0007] According to a second aspect of the present invention, a configuration apparatus for a power generation component is provided, comprising: The acquisition module is used to acquire the first resource data and distribution network information data within the target installation area; The first determining module is used to determine, based on the first resource data, the first installation data that the target installation area can accommodate for the power generation component; The second determining module is used to determine, based on the distribution network information data and pre-established correction data, the second installation data that the distribution network can accommodate for the power generation components; The third determining module is used to determine the target installation data of the target installation area based on the first installation data and the second installation data; The configuration module is used to determine the target configuration result within the target installation area based on the target installation data.

[0008] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the configuration method of the power generation component according to any embodiment of the present invention.

[0009] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the configuration method of the power generation component according to any embodiment of the present invention.

[0010] The technical solution of this invention can obtain first resource data and distribution network information data within a target installation area, then determine first installation data that the target installation area can accommodate for the power generation component based on the first resource data, and determine second installation data that the distribution network can accommodate for the power generation component based on the distribution network information data and pre-established correction data. Next, based on the first installation data and the second installation data, the target installation data for the target installation area can be determined. Finally, based on the target installation data, the target configuration result within the target installation area can be determined. This solves the problem of low accuracy in determining installation data in the prior art, and the low security of the distribution network caused by the mismatch between the installation data and the actual installation data in the area. It improves the accuracy of determining the installation data of the power generation component in the area, as well as the security of the distribution network.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for configuring a power generation component according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of a method for configuring a power generation component according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the configuration device for a power generation component provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the configuration method of the power generation component according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1This is a flowchart of a configuration method for a power generation component according to Embodiment 1 of the present invention. This embodiment is applicable to configuring power generation components in an area where they are to be installed. The method can be executed by a configuration device for the power generation component, which can be implemented in hardware and / or software and can be configured in an electronic device or cloud server used for configuring the power generation component. Figure 1 As shown, the method includes: S101. Obtain the first resource data and distribution network information data within the target installation area.

[0017] The target installation area can refer to the planned area where power generation components (such as distributed photovoltaics) are to be installed, such as a park, street, or power distribution network area.

[0018] The first resource data can be the site resources and corresponding site parameters available for the installation of power generation components within the target installation area, which may include physical space parameters and attribute parameters.

[0019] Distribution network information data can refer to relevant information about the distribution network to which the target installation area belongs. For example, it can be the power grid topology data (such as the connection relationship of substations, lines, and distribution transformers), power grid equipment parameters (such as transformer capacity and impedance, line type and impedance), and power grid operation information (such as hourly load curves, voltage curves, and power factors over a period of time).

[0020] For example, this embodiment can use pre-sensing and mapping to collect all available site resources and corresponding site parameters for installing power generation components within the target installation area into a database, which can then be directly retrieved from the pre-set database for subsequent use. For instance, high-resolution satellite remote sensing imagery can be used to identify building outlines within the target installation area, and oblique photogrammetry can be used to obtain three-dimensional roof information, establishing a roof resource database. Then, based on the building function, the buildings are classified into industrial plants, commercial buildings, public buildings, residential buildings, etc., and the location coordinates, area, orientation, slope, height, and other parameters of each building's roof are determined. Subsequently, the building category and roof parameters are added to the corresponding storage location in the roof resource database.

[0021] Alternatively, this embodiment can also obtain the site resources and corresponding site parameters available for the installation of power generation components within the target installation area by real-time acquisition of the geographic information system or building archives of the target installation area.

[0022] In addition, this embodiment can also obtain information such as power grid topology data, power grid equipment parameters, and historical load records of the distribution network from the power grid company's data platform or through direct on-site surveys as distribution network information data.

[0023] S102. Based on the first resource data, determine the first installation data that the target installation area can accommodate for the power generation component.

[0024] The first installation data can be an assessment of the maximum capacity of power generation components that can theoretically be installed in the target installation area from the perspective of the resources of the target installation area.

[0025] For example, after determining the site resources and corresponding site parameters available for installing power generation components within the target installation area, this embodiment can obtain the maximum capacity value of power generation components that can theoretically be installed in the target installation area by multiplying the total area of ​​site resources within the target area with the corresponding unit installation coefficient (representing the capacity of power generation components that can be installed per unit area).

[0026] S103. Based on the power distribution network information data and the pre-established correction data, determine the second installation data that the power distribution network can accommodate for the power generation components.

[0027] The second installation data can be the maximum capacity value that the distribution network corresponding to the determined target installation area can safely accommodate power generation components, from the perspective of safe operation of the distribution network (the carrying capacity of the distribution network).

[0028] Correction data can be coefficients or parameters used to adjust data characterizing the carrying capacity of the distribution network. It should be noted that the upper limit of the distribution network's carrying capacity depends not only on the static parameters of the grid equipment within the distribution network but also on the dynamic influence of the power output of the generating units and the power consumption characteristics of the distribution network. Therefore, correction data that reflects the dynamic influence of the power generation characteristics of the generating units and the grid operation characteristics of the distribution network can further improve the accuracy of determining the second installation data that the distribution network can accommodate for the generating units.

[0029] For example, in this embodiment, the maximum capacity of the power distribution network to the power generation components can be determined by electrical calculations using electrical parameters (such as transformer capacity and line specifications) in the power distribution network information data, and this value can be used as the static carrying capacity data value of the power distribution network.

[0030] Next, by using the correction data constructed through the pre-analysis of the matching degree between the power generation characteristics of the power generation components and the power grid operation characteristics of the distribution network in time, the static carrying capacity data value of the distribution network foundation is corrected, thereby transforming the static capacity into a second installation data that better reflects the actual operation of the distribution network.

[0031] S104. Based on the first installation data and the second installation data, determine the target installation data for the target installation area.

[0032] The target installation data can be the final installed capacity of the power generation components in the target installation area, which is determined after comprehensively considering the upper limit of the installed power generation components in the target installation area and the upper limit of the safe acceptance of the distribution network.

[0033] For example, in order to determine the capacity of the optimal installed power generation components in the target installation area as the globally optimal target installation data while ensuring the safety of the distribution network, this embodiment can use the first installation data and the second installation data together as the capacity constraint condition for determining the target installation data, thereby determining the smaller value of the two as the target installation data.

[0034] S105. Based on the target installation data, determine the target configuration result within the target installation area.

[0035] The target configuration result can be the capacity allocation result for the target installation area, corresponding to the target installation data. It should be noted that the target installation area can include multiple sub-areas, and each sub-area can have its own corresponding capacity allocation result.

[0036] For example, after determining the target installation data representing the final installed capacity of the power generation components within the target installation area, the target installation data can be evenly distributed according to the number of sub-regions within the target installation area so that each sub-region is configured with a corresponding capacity allocation result, and finally the target configuration result within the target installation area can be obtained by summarizing.

[0037] The technical solution of this embodiment can obtain first resource data and distribution network information data within the target installation area, then determine first installation data that the target installation area can accommodate for the power generation component based on the first resource data, and determine second installation data that the distribution network can accommodate for the power generation component based on the distribution network information data and pre-established correction data. Next, the target installation data of the target installation area can be determined based on the first installation data and the second installation data. Finally, the target configuration result within the target installation area can be determined based on the target installation data. This solves the problem of low accuracy in determining installation data in the prior art, and the low security of the distribution network caused by the mismatch between the installation data and the actual installation data in the area. It improves the accuracy of determining the installation data of the power generation component in the area, as well as the security of the distribution network.

[0038] Example 2 Figure 2 This is a flowchart illustrating a configuration method for a power generation component according to Embodiment 2 of the present invention. This embodiment is a further optimization and extension based on the above-described embodiments, and can be combined with various optional technical solutions in the above embodiments. For example... Figure 2 As shown, the method includes: S201. Obtain the first resource data and distribution network information data within the target installation area.

[0039] S202. Based on the preset multi-dimensional filtering indicators, the first resource data is filtered to obtain the second resource data.

[0040] The multi-dimensional screening criteria can be a set of conditions or thresholds set from multiple dimensions to determine whether a target installation area is suitable for installing power generation modules. The second resource data can refer to the valid site resources and corresponding site parameters that meet the actual installation requirements of the power generation modules and are selected based on the multi-dimensional screening criteria.

[0041] It should be noted that not all site resources in the initial resource data within the target installation area are suitable for installing power generation components, which leads to lower accuracy in the final installation data. To further improve the accuracy of the installation data, this embodiment can perform a refined evaluation of the initially acquired resource data to exclude unsuitable site resources.

[0042] For example, in this embodiment, after determining the site resources and corresponding site parameters available for the installation of power generation components within the target installation area, the first resource data can be filtered according to preset screening conditions covering dimensions such as structural safety, orientation, tilt angle, shading, minimum area, and property rights policy, and the effective site area after evaluation can be identified as the second resource data.

[0043] The preset multi-dimensional screening indicators cover structural safety, orientation, tilt angle, shading, minimum area, and property rights policy. For example, a screening indicator based on structural safety could be: requiring a roof load-bearing capacity ≥ 30 kg / m². 2 The following criteria were used to select suitable roofs: 1) Based on orientation suitability: South-facing, southeast-facing, and southwest-facing roofs are preferred; 2) Based on tilt angle suitability: Suitable range is [0°, 45°]; 3) Based on shading: Annual shadow distribution is calculated based on the solar trajectory model, excluding areas with an annual average shading rate >20%; 4) Based on area threshold: Minimum usable area is set at 50 square meters.

[0044] S203. Based on the area of ​​each type of installation area in the second resource data and the unit installation coefficient corresponding to each type of installation area, determine the first installation data that the target installation area can accommodate.

[0045] The unit installation factor can be considered as the capacity of power generation components that can be installed per unit area. It is understood that installation areas can be classified as flat roofs or pitched roofs, and the unit installation factor differs for each category. The unit installation factor for a flat roof should be less than that for a pitched roof.

[0046] For example, in this embodiment, the valid installation sites in the second resource data can be classified according to a preset type, and the total area of ​​each category can be counted. Then, the corresponding unit installation coefficient can be determined for each type of site, and the maximum capacity value of the power generation components that can be installed in the target installation area can be calculated by determining the sum of the product of the area of ​​each type of site and the corresponding unit installation coefficient, which is used as the first installation data.

[0047] S204. Based on the power distribution network information data, calculate the basic capacity of the power distribution network using preset electrical constraints.

[0048] Electrical constraints can be used to quantify and assess the physical limits of distribution network equipment, such as transformer capacity constraints, line thermal stability constraints, and voltage quality constraints. Specifically, a transformer capacity constraint can be the maximum capacity of the generating units it can support under the transformer's maximum capacity. A line thermal stability constraint can be the maximum capacity of the generating units it can support while ensuring line thermal stability. A voltage quality constraint can be the maximum capacity of the generating units it can support while ensuring stable voltage at each node in the distribution network. It should be noted that these electrical constraints can be set in advance by technicians based on the operating conditions of common equipment in the distribution network.

[0049] The basic capacity of the distribution network can be the preliminary capacity value that the distribution network can accommodate, calculated under the condition of preliminarily considering the physical parameter constraints of the power distribution equipment in the distribution network.

[0050] For example, this step can use the safe operation of power grid equipment as a constraint to preliminarily assess the theoretical carrying capacity of the distribution network. Specifically, based on the power grid topology and equipment parameters in the distribution network information data, transformer capacity constraints, line thermal stability constraints, and voltage quality constraints within the target area can be determined respectively. Then, the minimum value among the various capacities calculated from the above constraints can be taken as the basic capacity of the distribution network. This basic capacity of the distribution network can represent the maximum carrying capacity of the distribution network under ideal steady state.

[0051] S205. Based on the matching correction data and the carrying capacity correction data, the basic capacity of the power distribution network is corrected to determine the second installation data.

[0052] In this embodiment, the pre-established correction data may include matching correction data and carrying capacity correction data.

[0053] Matching correction data is a coefficient characterizing the temporal matching degree between the output characteristics of power generation components and the regional electricity consumption characteristics, and is used to correct the basic capacity of the distribution network. It is understood that if there is a temporal mismatch between the output characteristics of power generation components and the regional electricity consumption characteristics, it means that the actual capacity of the distribution network in that area will be lower than its theoretical capacity, and the actual configurable capacity will be less. Therefore, matching correction data can be used to adjust the basic capacity of the distribution network to determine a capacity that better reflects the actual capacity.

[0054] Carrying capacity correction data can be a coefficient used to characterize the comprehensive carrying capacity, including equipment carrying capacity, power quality assurance capability, and load matching adaptability. Similarly, if the distribution network's capabilities in equipment carrying capacity, power quality assurance capability, and load matching adaptability are insufficient, the actual acceptance capacity of the distribution network in that area will be lower than its theoretical acceptance capacity. Therefore, carrying capacity correction data can also be used to adjust the basic capacity of the distribution network.

[0055] For example, in order to make the static distribution network basic capacity closer to the actual operating scenario, this embodiment can generate matching correction data by analyzing the output data of the power generation components and the historical operating data of the region in advance. At the same time, it can generate carrying capacity correction data by comprehensively evaluating the equipment carrying capacity, power quality assurance capability and load matching adaptability of the distribution network in advance. Then, the distribution network basic capacity can be multiplied with the matching correction data and the carrying capacity correction data in sequence to determine the second installation data that is more in line with the actual operating scenario.

[0056] S206. Determine the target constraints based on the first installation data and the second installation data.

[0057] It should be noted that existing methods often lack a comprehensive assessment of the target installation area and the distribution network when determining target installation data, leading to contradictions such as having rooftops but not being able to install them (insufficient grid capacity) or having capacity but no rooftops (insufficient rooftop resources), thus failing to achieve efficient configuration of power generation components.

[0058] Therefore, in order to solve the above problems, this embodiment can determine the target constraint conditions characterizing the capacity boundary by comprehensively judging the first installation data on the target installation area side and the second installation data on the distribution network side.

[0059] The target constraint can be that the target installation data is less than or equal to the minimum of the first installation data and the second installation data. It represents the maximum capacity of the power generation components that can be configured within the target installation area, provided that both site installation conditions and power distribution network safety conditions are met.

[0060] For example, the target constraint conditions can be determined based on the first installation data and the second installation data as follows: ; in, It can represent the target installation data; This can represent the initial installation data that the target installation area can accommodate for the power generation components; This can represent the second installation data that the power distribution network can accommodate for the power generation components.

[0061] Understandably, when < At that time, the target installation data is limited by the roof resources in the target installation area. > At that time, the target installation data is limited by the capacity that the distribution network can accommodate for the power generation components.

[0062] S207. Based on the pre-established target optimization function and combined with the target constraints, solve to determine the target installation data; wherein, the target optimization function includes: economic target optimization function, environmental target optimization function, and power grid friendliness optimization function.

[0063] The objective optimization function can include multiple parallel optimization objective dimensions, such as economic benefit dimension, environmental benefit dimension, and grid operation benefit dimension. Accordingly, the objective optimization function can include: economic objective optimization function, environmental objective optimization function, and grid-friendliness optimization function.

[0064] Furthermore, the economic objective optimization function can be: ; Wherein, NPV can represent the net present value of a power generation module project over its entire life cycle, and R can represent the long-term economic benefits of the power generation module project; i can represent the year number; T can represent the total number of years in the entire life cycle of the power generation module project; R i C can represent the revenue in year i; i can represent the cost in year i; r can represent the discount rate (usually 5%-8%).

[0065] The environmental objective optimization function can be: ; Among them, E CO2 It can represent the total carbon emission reduction over the entire life cycle and can be used to quantify the environmental targets of power generation module projects; P can represent the capacity of the power generation modules ultimately configured within the target installation area; H can represent the annual equivalent utilization hours (which can be determined in advance based on local solar resources); δ CO2It can represent the carbon emission reduction coefficient per unit of electricity generated; T can represent the total number of years in the project's entire life cycle.

[0066] The grid-friendly optimization function can be: ; Among them, Loss total This value represents the overall grid loss index, used to characterize the merits of a solution in terms of grid friendliness. It's worth noting that a smaller value indicates a lighter additional burden on the distribution network caused by the integration of power generation components, resulting in better grid operation security and economy. active It can represent active power loss (the annual active power loss of the distribution network after the generation components are connected); Loss voltage It can represent voltage quality loss, reflecting the impact of power generation components on power quality; W v It can represent the voltage quality loss weighting coefficient.

[0067] It should be noted that, in order to determine the optimal construction capacity of the power generation components in the target installation area, this embodiment can also use a pre-established target optimization function to comprehensively solve the problem under multiple optimization dimensions, so that the final determined target installation data not only meets the site resources on the target area side and the safety requirements on the distribution network side, but also achieves comprehensive optimization under economic, environmental, and technical dimensions.

[0068] For example, using the target constraints as the feasible domain boundary, a multi-objective evolutionary algorithm or a weighted summation method is employed to solve the multi-dimensional target optimization function, generating comprehensively optimized target installation data.

[0069] S208. Based on the comprehensive score of each sub-region within the predetermined target installation area, determine the installation allocation weight of each sub-region.

[0070] The overall score represents the comprehensive suitability of installing power generation components in each sub-region (such as each distribution substation) within the target installation area. The installation allocation weight can be used to determine the capacity ratio of the corresponding sub-region.

[0071] For example, in order to reasonably decompose the total capacity of installed power generation components within the target installation area represented by the target installation data into various sub-regions within the target installation area, this embodiment can pre-calculate the comprehensive score of each sub-region using evaluation methods such as the analytic hierarchy process (AHP) based on factors such as resource conditions, grid conditions, and load matching degree. Then, the installation allocation weight that each sub-region should occupy is determined based on its comprehensive score. It can be understood that the higher the comprehensive score of a sub-region, the greater its corresponding installation allocation weight.

[0072] S209. Generate the target configuration result based on the target installation data and the installation allocation weight of each sub-region.

[0073] The target configuration result can be used to represent the installation capacity configuration of each sub-region within the target installation area.

[0074] For example, the determined target installation data (representing the total capacity of the power generation components installed in the target installation area) can be multiplied by the installation allocation weight of each sub-region to calculate the specific configuration installation capacity of each sub-region. Then, the configuration results of all sub-regions can be summarized to generate the target configuration result.

[0075] The technical solution of this embodiment acquires first resource data and distribution network information data within the target installation area. Then, based on preset multi-dimensional screening indicators, the first resource data is filtered to obtain second resource data. Based on the area of ​​various installation areas in the second resource data and the unit installation coefficient corresponding to each type of installation area, the first installation data that the target installation area can accommodate is determined. Simultaneously, based on the distribution network information data and preset electrical constraints, the basic capacity of the distribution network is calculated. Based on matching correction data and carrying capacity correction data, the basic capacity of the distribution network is corrected to determine the second installation data. Finally, based on the first and second installation data, target constraints are determined. Based on a pre-established target optimization function and the target constraints, the target installation data is solved and determined. Based on the comprehensive score of each sub-region within the target installation area, the installation allocation weight of each sub-region is determined. Based on the target installation data and the installation allocation weights of each sub-region, a target configuration result is generated. This allows for a more accurate determination of the actual impact of power generation component access on the distribution network and further determines installation data for power generation components in the target installation area that better matches the actual operating scenario, ensuring the safe and stable operation of the distribution network.

[0076] Based on the above embodiments, the present invention also provides an optional embodiment, which can further illustrate the above-described method for establishing matching correction data. The method for establishing matching correction data may include: Acquire historical meteorological and historical load data for the target installation area; Based on the historical meteorological data, a daily power output curve for the power generation components is established; Based on the historical load data, a daily load curve is established; Based on the daily power output curve and the daily load curve, the load matching coefficient is calculated, and the matching correction data is determined based on the load matching coefficient.

[0077] Among them, historical meteorological data can be environmental parameters related to power generation of photovoltaic modules recorded in the target installation area during a historical period, including at least solar irradiance, ambient temperature, cloud cover, etc., which can be used to characterize the solar resources and their temporal distribution characteristics of the target installation area.

[0078] Historical load data can be the power consumption data recorded at various times within a historical period by the distribution network to which the target installation area belongs. It can be used to characterize the power consumption behavior patterns and temporal distribution characteristics of power users within the target installation area.

[0079] The daily power generation curve can be a function curve constructed with time as the horizontal axis and power generation as the vertical axis, which can be used to reflect the intermittent and fluctuating characteristics of power generation from power generation components. The daily load curve can also be a function curve constructed with time as the horizontal axis and power consumption as the vertical axis, used to illustrate the changing pattern of electricity load intensity over time. The load matching coefficient can be a dimensionless index used to quantify the degree of overlap between the daily power generation curve and the daily load curve of power generation components in the time dimension.

[0080] For example, the present invention can obtain historical meteorological parameters such as irradiance and temperature of the target installation area from a meteorological database or a third-party service platform, and summarize them to form historical meteorological data. Simultaneously, it obtains historical load data from the power distribution network system, which shares the same time dimension as the historical meteorological data. Furthermore, based on the historical meteorological data, a daily power output curve for the power generation components can be established, and based on the historical load data, a daily load curve can be established.

[0081] Next, the load matching factor λ can be calculated based on the daily power generation curve and the daily load curve, reflecting the proportion of power generated by the power generation components that is directly absorbed by the local load: ; Where λ can represent the load matching coefficient; t can be a time index, used to represent a time within a day; P pv (t) can represent the output power of the power generation component at time t; P load (t) can represent the load power of the target installation area at time t.

[0082] Understandably, the load matching coefficient λ quantifies the degree of matching between the power generation of the power generation components and the power consumption of the load in terms of time distribution. The higher the value of λ, the stronger the absorption capacity and the smaller the impact on the distribution network.

[0083] Furthermore, based on different ranges of λ values, corresponding matching correction data K can be generated using preset mapping rules. match This is used to dynamically adjust the basic capacity of the distribution network. For example, it can be set that if λ≥0.7, then K... match =1.0 (indicating no reduction in the basic capacity of the distribution network), if 0.5≤λ<0.7, then Kmatch =0.9, if λ<0.5, then K match =0.8.

[0084] Based on the above embodiments, the present invention also provides another optional embodiment, which can further illustrate the method for establishing the above-mentioned carrying capacity correction data. The method for establishing the carrying capacity correction data may include: Target evaluation indicators are determined based on the aforementioned power distribution network information data; The target installation area is comprehensively evaluated based on the target evaluation indicators to obtain the corresponding load-bearing capacity evaluation score; Based on the load-bearing capacity evaluation score, the load-bearing capacity correction data is determined.

[0085] Among them, the target evaluation indicators can be used to quantify the quality of the distribution network’s ability to support the connection of power generation components in multiple dimensions, including at least the dimensions of equipment hardware conditions, power quality level, and operational flexibility.

[0086] The carrying capacity evaluation score is a comprehensive score obtained by quantitatively assessing the overall carrying capacity health of the distribution network within the target installation area. It can be understood that a higher carrying capacity evaluation score indicates a stronger adaptability of the distribution network to the connection of power generation components, and a less likely impact on the acceptable capacity.

[0087] For example, target evaluation indicators that can characterize the carrying capacity of the distribution network can be selected based on distribution network information data. For instance, indicators such as transformer load margin, line transmission margin, and equipment utilization rate can be selected from the equipment carrying capacity dimension; indicators such as voltage qualification rate, voltage fluctuation rate, and harmonic distortion rate can be selected from the power quality dimension; and indicators such as load matching coefficient, net load fluctuation, and self-consumption ratio can be selected from the load matching dimension.

[0088] Furthermore, the Top-Optimal Solution Ranking Method (TOPSIS) can be used for comprehensive evaluation to determine the load-bearing capacity evaluation score of each sub-region or the entire target installation area, and the load-bearing capacity correction data can be determined from the pre-established mapping rules from the load-bearing capacity evaluation score to the load-bearing capacity correction data.

[0089] Example 3 Figure 3 This is a schematic diagram of a configuration device for a power generation component provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The acquisition module 31 is used to acquire the first resource data and distribution network information data within the target installation area; The first determining module 32 is used to determine, based on the first resource data, the first installation data that the target installation area can accommodate for the power generation component; The second determining module 33 is used to determine the second installation data that the power distribution network can accommodate for the power generation components based on the power distribution network information data and the pre-established correction data. The third determining module 34 is used to determine the target installation data of the target installation area based on the first installation data and the second installation data; Configuration module 35 is used to determine the target configuration result within the target installation area based on the target installation data.

[0090] The technical solution of this embodiment can obtain first resource data and distribution network information data within the target installation area, then determine first installation data that the target installation area can accommodate for the power generation component based on the first resource data, and determine second installation data that the distribution network can accommodate for the power generation component based on the distribution network information data and pre-established correction data. Next, the target installation data of the target installation area can be determined based on the first installation data and the second installation data. Finally, the target configuration result within the target installation area can be determined based on the target installation data. This solves the problem of low accuracy in determining installation data in the prior art, and the low security of the distribution network caused by the mismatch between the installation data and the actual installation data in the area. It improves the accuracy of determining the installation data of the power generation component in the area, as well as the security of the distribution network.

[0091] Optionally, the first determining module 32 can be specifically used to filter the first resource data according to preset multi-dimensional filtering indicators to obtain the second resource data; Based on the area of ​​each type of installation area in the second resource data and the unit installation coefficient corresponding to each type of installation area, the first installation data that the target installation area can accommodate is determined.

[0092] Optionally, the correction data may include: matching correction data and carrying capacity correction data.

[0093] The second determining module 33 can be specifically used to calculate the basic capacity of the distribution network based on the distribution network information data and through preset electrical constraints. Based on the matching correction data and the carrying capacity correction data, the basic capacity of the distribution network is corrected to determine the second installation data.

[0094] Optionally, the method for establishing the matching correction data may include: Acquire historical meteorological and historical load data for the target installation area; Based on the historical meteorological data, a daily power output curve for the power generation components is established; Based on the historical load data, a daily load curve is established; Based on the daily power output curve and the daily load curve, the load matching coefficient is calculated, and the matching correction data is determined based on the load matching coefficient.

[0095] Optionally, the method for constructing the carrying capacity correction data may include: Target evaluation indicators are determined based on the aforementioned power distribution network information data; The target installation area is comprehensively evaluated based on the target evaluation indicators to obtain the corresponding load-bearing capacity evaluation score; Based on the load-bearing capacity evaluation score, the load-bearing capacity correction data is determined.

[0096] Optionally, the third determining module 34 can be specifically used to determine the target constraint conditions based on the first installation data and the second installation data; Based on the pre-established target optimization function and the target constraints, the target installation data is solved and determined. The objective optimization functions include: an economic objective optimization function, an environmental objective optimization function, and a power grid friendliness optimization function.

[0097] Optionally, the configuration module 35 can be specifically used to determine the installation allocation weight of each sub-region based on the comprehensive score of each sub-region within the predetermined target installation area; The target configuration result is generated based on the target installation data and the installation allocation weights of each sub-region.

[0098] The configuration device for the power generation component provided in this embodiment of the invention can execute the configuration method for the power generation component provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0099] Example 4 Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0100] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0101] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0102] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the configuration methods of power generation components.

[0103] In some embodiments, the configuration method for the power generation component may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the configuration method for the power generation component described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the configuration method for the power generation component by any other suitable means (e.g., by means of firmware).

[0104] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0106] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0108] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0109] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0110] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the configuration method of the power generation component of any embodiment of the present invention.

[0111] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0112] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0113] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for configuring a power generation component, characterized in that, include: Acquire primary resource data and power distribution network information data within the target installation area; Based on the first resource data, determine the first installation data that the target installation area can accommodate for the power generation component; Based on the power distribution network information data and the pre-established correction data, determine the second installation data that the power distribution network can accommodate for the power generation components; Based on the first installation data and the second installation data, the target installation data for the target installation area is determined; Based on the target installation data, determine the target configuration result within the target installation area.

2. The method according to claim 1, characterized in that, The step of determining the first installation data that the target installation area can accommodate based on the first resource data includes: The first resource data is filtered according to the preset multidimensional filtering indicators to obtain the second resource data; Based on the area of ​​each type of installation area in the second resource data and the unit installation coefficient corresponding to each type of installation area, the first installation data that the target installation area can accommodate is determined.

3. The method according to claim 1, characterized in that, The correction data includes: matching correction data and carrying capacity correction data; The step of determining the second installation data that the power distribution network can accommodate for the power generation components based on the power distribution network information data and pre-established correction data includes: Based on the power distribution network information data, the basic capacity of the power distribution network is calculated using preset electrical constraints. Based on the matching correction data and the carrying capacity correction data, the basic capacity of the distribution network is corrected to determine the second installation data.

4. The method according to claim 3, characterized in that, The method for establishing the matching correction data includes: Acquire historical meteorological and historical load data for the target installation area; Based on the historical meteorological data, a daily power output curve for the power generation components is established; Based on the historical load data, a daily load curve is established; Based on the daily power output curve and the daily load curve, the load matching coefficient is calculated, and the matching correction data is determined based on the load matching coefficient.

5. The method according to claim 3, characterized in that, The method for constructing the carrying capacity correction data includes: Target evaluation indicators are determined based on the aforementioned power distribution network information data; The target installation area is comprehensively evaluated based on the target evaluation indicators to obtain the corresponding load-bearing capacity evaluation score; Based on the load-bearing capacity evaluation score, the load-bearing capacity correction data is determined.

6. The method according to claim 1, characterized in that, The step of determining the target installation data for the target installation area based on the first installation data and the second installation data includes: Based on the first installation data and the second installation data, determine the target constraints; Based on the pre-established target optimization function and the target constraints, the target installation data is solved and determined. The objective optimization functions include: an economic objective optimization function, an environmental objective optimization function, and a power grid friendliness optimization function.

7. The method according to claim 1, characterized in that, The step of determining the target configuration result within the target installation area based on the target installation data includes: Based on the comprehensive score of each sub-region within the predetermined target installation area, the installation allocation weight of each sub-region is determined; The target configuration result is generated based on the target installation data and the installation allocation weights of each sub-region.

8. A device for configuring a power generation component, characterized in that, include: The acquisition module is used to acquire the first resource data and distribution network information data within the target installation area; The first determining module is used to determine, based on the first resource data, the first installation data that the target installation area can accommodate for the power generation component; The second determining module is used to determine the second installation data that the power distribution network can accommodate for the power generation components based on the power distribution network information data and the pre-established correction data. The third determining module is used to determine the target installation data of the target installation area based on the first installation data and the second installation data; The configuration module is used to determine the target configuration result within the target installation area based on the target installation data.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the configuration method of the power generation component according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the configuration method of the power generation component according to any one of claims 1-7.

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