Method for determining photovoltaic access capacity of power distribution network and electronic device

Abstract

This invention discloses a method and electronic device for determining the photovoltaic (PV) access capacity of a distribution network. The method includes: obtaining the current configuration strategy of the target distribution network; determining a PV access capacity influence factor for the target distribution network under the current operating scenario based on the current configuration strategy, wherein the PV access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual PV access capacity of the target distribution network; determining the coupling coefficient of the target distribution network; and determining the actual PV access capacity of the target distribution network under the current operating scenario based on the PV access capacity influence factor and the coupling coefficient. This invention solves the technical problem in related technologies where incomplete consideration of factors leads to low accuracy in assessing the actual PV access capacity of the distribution network when facing complex distribution network operating scenarios.

Description

Technical Field

[0001] This invention relates to the fields of new energy and energy-saving technology, and more specifically, to a method and electronic device for determining the photovoltaic access capacity of a power distribution network. Background Technology

[0002] With the increasing penetration rate of distributed photovoltaic (PV) power in distribution networks, the intermittent, fluctuating, and reverse power characteristics of distributed PV output have posed severe challenges to voltage stability, equipment load, and power quality. Accurately assessing the PV capacity of distribution networks has become a critical issue in planning and operation. Existing technologies have significant shortcomings in PV capacity assessment. Methods in these technologies often consider PV output, load size, or voltage exceedance risks in isolation, resulting in assessments that fail to reflect the true impact on voltage and power flow. While these methods can achieve rough estimates within a certain range, their incomplete consideration of factors leads to low accuracy in assessing PV capacity in complex distribution network operation scenarios.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method and electronic device for determining the photovoltaic (PV) access capacity of a distribution network, which at least solves the technical problem in related technologies where incomplete consideration of factors leads to low accuracy in assessing the actual PV access capacity of the distribution network when facing complex distribution network operation scenarios.

[0005] According to one aspect of the present invention, a method for determining the photovoltaic (PV) access capacity of a distribution network is provided, comprising: obtaining a current configuration strategy of a target distribution network, wherein the current configuration strategy includes target line lengths, power, power factors, and harmonic factors corresponding to multiple load nodes in the target distribution network under the current operating scenario, preset PV access capacity and node locations corresponding to multiple PV access nodes, energy storage power and energy storage capacity of an energy storage device, and the target line length representing the line length between the power source and the corresponding load node in the target distribution network; determining a PV access capacity influence factor of the target distribution network under the current operating scenario based on the current configuration strategy, wherein the PV access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual PV access capacity of the target distribution network; determining a coupling coefficient of the target distribution network, wherein the coupling coefficient is obtained based on the historical configuration strategies corresponding to the target distribution network under multiple historical operating scenarios, and the corresponding historical PV access capacities; and determining the actual PV access capacity of the target distribution network under the current operating scenario based on the PV access capacity influence factor and the coupling coefficient.

[0006] According to another aspect of the present invention, a device for determining the photovoltaic (PV) access capacity of a distribution network is also provided, comprising: a current configuration strategy acquisition module, configured to acquire the current configuration strategy of a target distribution network, wherein the current configuration strategy includes, under the current operating scenario, the target line length, power, power factor and harmonic factor corresponding to each of multiple load nodes in the target distribution network, the preset PV access capacity and node location corresponding to each of multiple PV access nodes, the energy storage power and energy storage capacity of an energy storage device, and the target line length representing the line length between the power source and the corresponding load node in the target distribution network; an influence factor determination module, configured to determine the PV access capacity influence factor of the target distribution network under the current operating scenario based on the current configuration strategy, wherein the PV access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual PV access capacity of the target distribution network; a coupling coefficient determination module, configured to determine the coupling coefficient of the target distribution network, wherein the coupling coefficient is obtained based on the historical configuration strategies corresponding to each of the target distribution network under multiple historical operating scenarios, and the corresponding historical PV access capacity; and a PV access capacity determination module, configured to determine the actual PV access capacity of the target distribution network under the current operating scenario based on the PV access capacity influence factor and the coupling coefficient.

[0007] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium storing a plurality of instructions adapted for loading by a processor and executing any one of the photovoltaic access capacity determination methods for distribution networks described herein.

[0008] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the photovoltaic access capacity determination method for the distribution network as described in any one of the present invention.

[0009] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the method for determining the photovoltaic access capacity of a distribution network as described in any one of the present invention.

[0010] In this embodiment of the invention, the current configuration strategy of the target distribution network is obtained. This current configuration strategy includes the target line length, power, power factor, and harmonic factor corresponding to each load node in the target distribution network under the current operating scenario; the preset photovoltaic access capacity and node location corresponding to each of the multiple photovoltaic access nodes; the energy storage power and energy storage capacity of the energy storage device; and the target line length represents the line length between the power source and the corresponding load node in the target distribution network. Based on the current configuration strategy, the photovoltaic access capacity influence factor of the target distribution network under the current operating scenario is determined. This photovoltaic access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual photovoltaic access capacity of the target distribution network. Finally, the coupling coefficient of the target distribution network is determined, whereby the coupling coefficient is based on the target distribution network under multiple historical... The historical configuration strategies and corresponding historical photovoltaic (PV) access capacities for each operating scenario are obtained. Based on the PV access capacity influence factor and coupling coefficient, the actual PV access capacity of the target distribution network under the current operating scenario is determined. This achieves the goal of determining the PV access capacity influence factor based on the current configuration strategy of the target distribution network and combining it with the coupling coefficient determined based on the historical configuration strategy and historical PV access capacity, thus accurately determining the actual PV access capacity of the target distribution network under the current operating scenario. This improves the technical accuracy of the actual PV access capacity assessment of the distribution network and solves the technical problem in related technologies where incomplete consideration of factors leads to low accuracy in the assessment of the actual PV access capacity of the distribution network when facing complex distribution network operating scenarios. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0012] Figure 1 This is a flowchart of a method for determining the photovoltaic access capacity of a distribution network according to an embodiment of the present invention;

[0013] Figure 2 This is a flowchart of an optional photovoltaic access capacity impact factor determination method according to an embodiment of the present invention;

[0014] Figure 3 This is a flowchart of an optional method for determining the photovoltaic access capacity of a distribution network according to an embodiment of the present invention;

[0015] Figure 4 This is a schematic diagram of a photovoltaic access capacity determination device for a distribution network according to an embodiment of the present invention;

[0016] Figure 5This is a schematic diagram of an electronic device for determining the photovoltaic access capacity of a power distribution network according to an embodiment of the present invention. Detailed Implementation

[0017] 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.

[0018] 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.

[0019] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0020] Photovoltaic grid connection capacity refers to the maximum active power capacity of distributed photovoltaic systems that a distribution network can accommodate under a specific operating scenario, provided that the constraints of safe and stable operation of the distribution network are met.

[0021] According to an embodiment of the present invention, a method for determining the photovoltaic access capacity of a distribution network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0022] Figure 1 This is a flowchart of a method for determining the photovoltaic access capacity of a distribution network according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0023] Step S102: Obtain the current configuration strategy of the target distribution network. The current configuration strategy includes the target line length, power, power factor and harmonic factor of each load node in the target distribution network under the current operating scenario, the preset photovoltaic access capacity and node location of each photovoltaic access node, the energy storage power and energy storage capacity of the energy storage device, and the target line length represents the line length between the power source and the corresponding load node in the target distribution network.

[0024] Optionally, the current configuration strategy of the target distribution network in the current time period can be obtained. This aims to comprehensively characterize the electrical topology and operating parameter status of the target distribution network under specific operating scenarios. The current configuration strategy includes, but is not limited to, the target line length, power, power factor, and harmonic factor of the load nodes; the access capacity and spatial location of the photovoltaic access nodes; and the power and capacity configuration of the energy storage devices. Among these, the target line length is the electrical distance between the power source and the load node, which is a key physical quantity that determines the voltage drop magnitude; the load power is used to indicate the active power demand intensity of the target distribution network; the power factor is used to quantify the reactive power demand characteristics of the load, reflecting the degree of suppression of voltage drop; the harmonic factor is used to quantify the degree of disturbance of nonlinear loads to power quality; the photovoltaic access capacity and node location are used together to indicate the injection scale and spatial distribution of distributed power sources, directly affecting the power flow direction and voltage rise risk of the target distribution network; and the power and capacity of the energy storage devices are used to indicate the ability of the energy storage devices to support voltage. By synchronously collecting the aforementioned multi-dimensional parameters, the limitations of simplistic assumptions in related technologies, such as load homogenization, photovoltaic centralization, and neglect of energy storage, can be overcome. This enables refined modeling of all elements of the target distribution network, including "source-grid-load-storage," allowing the assessment of photovoltaic access capacity to be based on real, multi-scale operating parameters. Consequently, the adaptability and engineering credibility of the photovoltaic access capacity assessment results under complex distribution network operating conditions can be significantly improved.

[0025] In an optional embodiment, before obtaining the current configuration strategy of the target distribution network, the method further includes: performing cluster analysis on the historical operating data of the target distribution network and the historical meteorological data of the area where the target distribution network is located to obtain multiple clusters, wherein the historical operating data includes at least historical photovoltaic power output data and historical load data for historical time periods, and the historical meteorological data includes at least irradiance, ambient temperature and cloud cover for historical time periods, and the multiple clusters correspond to a historical operating scenario of the target distribution network, and the historical time period is a time period of a predetermined duration before the current time period; performing meteorological natural language tagging processing on the multiple clusters to obtain meteorological semantic tags corresponding to each of the multiple clusters; and obtaining multiple historical operating scenarios based on the multiple clusters and the meteorological semantic tags corresponding to each of the multiple clusters.

[0026] Optionally, before obtaining the current configuration strategy of the target distribution network, a joint cluster analysis is first performed on the historical operating data of the target distribution network for a predetermined period (e.g., the past year) prior to the current time period and the historical meteorological data of the region to reveal the operating mode of the target distribution network under the driving force of the real environment. The historical meteorological data includes meteorological indicators such as irradiance, ambient temperature, and cloud cover for historical periods, and is used to characterize the natural driving factors of photovoltaic output and load changes. The historical photovoltaic output data and historical load data can be in the form of time series curves, such as constructing daily photovoltaic output curves and load curves with hourly sampling intervals. To eliminate the differences in absolute power levels between days and highlight the temporal characteristics of the operating mode, both the photovoltaic output curve and the load curve are normalized and represented as high-dimensional time series feature vectors. The high-dimensional time series feature vector for any historical day can be obtained in the following way: ,in, This represents the normalized photovoltaic output of the photovoltaic output curve for any given historical day at any given hour. This represents the normalized load of the load curve for any given historical day at any given hour, where t represents the index of that hour, T represents the number of hours included in that historical day, and d represents the index of that historical day. A time series function representing the photovoltaic power output of any historical day. This represents the time series function of the load on any historical day. Furthermore, a time-series clustering algorithm can be used to jointly cluster historical photovoltaic output data and historical load data. This involves dividing the historical photovoltaic output data and historical load data into several clusters with similar operating states. Each cluster represents an operating mode with specific time-series characteristics, such as "high irradiance on sunny summer days, peak air conditioning load in the evening" or "low irradiance on cloudy winter days, stable heating." Based on this, for each cluster, the corresponding irradiance, temperature, and cloud cover distribution characteristics of the historical days are statistically analyzed, and meteorological natural language labeling is implemented. For example, numerical meteorological data (such as average irradiance > 700 W / m²) can be tagged with these labels. 2The weather conditions (temperature > 28℃, cloud cover < 30%) are tagged into understandable natural language descriptions. For example, sunny summer days are tagged as peak air conditioning season, cloudy and fluctuating days as intermittent photovoltaic (PV) activity, and cold and overcast winter days as stable heating. This tagging process is not a simple threshold division, but rather a multi-dimensional semantic rule base built on the experience of domain experts. It combines typical meteorological pattern classifications for semantic merging to ensure that each tag accurately reflects the meteorological causes and corresponds to the operating characteristics of the target distribution network (e.g., dust storms correspond to low irradiance, low PV output but normal load, belonging to resource-constrained scenarios). Ultimately, each cluster and its corresponding meteorological semantic tag together constitute a historical operating scenario, forming a three-in-one scenario system of "data cluster, meteorological causes, and operating characteristics." The method in this embodiment, through meteorological natural language tagging, not only retains the high-precision identification capability of temporal clustering for operating patterns, but also allows subsequent evaluation of the current configuration strategy to directly trace back to similar historical scenarios, providing a decision-making basis for the dynamic evaluation of real-time PV access capacity.

[0027] Step S104: Based on the current configuration strategy, determine the photovoltaic access capacity impact factor of the target distribution network under the current operating scenario. The photovoltaic access capacity impact factor is used to quantify the degree of impact of the current configuration strategy on the actual photovoltaic access capacity of the target distribution network.

[0028] Optionally, based on the current configuration strategy, determining the photovoltaic (PV) grid connection capacity impact factor involves extracting the multi-dimensional operating parameters (including but not limited to line length, load type, spatial distribution of PV grid connection nodes, and energy storage configuration) characterized by the configuration strategy. By quantifying the impact of each operating parameter on voltage stability and reactive power balance, the PV grid connection capacity impact factor is ultimately obtained. This factor directly reflects the target distribution network's capacity to accommodate new PV capacity under the current configuration strategy. A higher PV grid connection capacity impact factor indicates a higher degree of synergistic optimization between the target distribution network topology and operating parameters, demonstrating stronger PV carrying capacity. Conversely, a lower PV grid connection capacity impact factor indicates significant bottlenecks in the target distribution network, such as excessive line impedance leading to increased risk of voltage exceeding limits, a high proportion of inductive loads exacerbating reactive power consumption, PV grid connection points being far from load centers causing long-distance transmission losses, or harmonic superposition leading to power quality degradation. The target distribution network's capacity to accommodate PV is structurally constrained. Quantifying the PV grid connection capacity impact factor allows for a shift from single-threshold judgment to multi-dimensional mechanism-coupled evaluation, providing a calculable technical basis for the accurate quantification of PV grid connection capacity.

[0029] In one alternative embodiment, Figure 2 This is a flowchart of an optional photovoltaic access capacity impact factor determination method according to an embodiment of the present invention, such as... Figure 2As shown, given that the factors influencing photovoltaic (PV) grid connection capacity include line attenuation factor, load type factor, PV matching factor, and energy storage enhancement factor, based on the current configuration strategy, the factors influencing PV grid connection capacity of the target distribution network under the current operating scenario are determined, including:

[0030] Step S202: Based on the target line length corresponding to each of the multiple load nodes, obtain the line attenuation factor, wherein the line attenuation factor is used to quantify the degree of suppression of voltage drop during photovoltaic power transmission by the target line length of the target distribution network.

[0031] Step S204: Based on the power, power factor and harmonic factor corresponding to each of the multiple load nodes, obtain the load type factor, wherein the load type factor is used to quantify the degree of influence of the load on the voltage of the target distribution network;

[0032] Step S206: Based on the preset photovoltaic access capacity and node location of each of the multiple photovoltaic access nodes, obtain the photovoltaic matching factor, wherein the photovoltaic matching factor is used to quantify the degree of matching between the spatial distribution of the multiple photovoltaic access nodes and the load center of the target distribution network;

[0033] Step S208: Based on the energy storage power and energy storage capacity, obtain the energy storage enhancement factor, wherein the energy storage enhancement factor is used to quantify the degree of compensation of the energy storage device for the actual photovoltaic access capacity of the target distribution network.

[0034] Optionally, when the photovoltaic (PV) grid connection capacity influencing factor is composed of the line attenuation factor, load type factor, PV matching factor, and energy storage enhancement factor, firstly, the line attenuation factor is calculated based on the target line length corresponding to each load node. This line attenuation factor characterizes the cumulative effect of voltage drop during power transmission along the feeder. The longer the target line and the higher the impedance, the more significant the voltage attenuation. Essentially, it is a quantitative assessment of the target distribution network channel capacity. Secondly, based on the active power, power factor, and harmonic factor of each load node, a load type factor is constructed. This load type factor can reflect different types of loads. The cumulative impact of load on the stability of the target distribution network is considered. Furthermore, based on the capacity and spatial location of photovoltaic (PV) access nodes, a PV matching factor is calculated. This factor measures the spatial coupling strength between PV output and load centers; a higher PV matching factor indicates stronger local PV carrying capacity and smaller voltage fluctuations, thus directly increasing the upper limit of the target distribution network's capacity. Finally, based on the energy storage power and capacity of the energy storage devices, an energy storage enhancement factor is calculated. This factor quantifies the ability of energy storage to mitigate PV output fluctuations and dynamically compensate for voltage exceedances, transforming static configuration into dynamically adjustable resources. The synergistic construction of these four factors not only enables accurate characterization of the factors influencing the PV access capacity of the target distribution network but also overcomes the limitations of single-point input, single constraint, and neglect of coupling in related technologies.

[0035] In one optional embodiment, the line attenuation factor is obtained based on the target line length corresponding to each of the multiple load nodes, including: obtaining the line attenuation factor based on the target line length corresponding to each of the multiple load nodes in the following manner:

[0036] ;

[0037] in, Indicates the line attenuation factor. Indicates the preset baseline coefficient. This indicates the preset attenuation coefficient. This represents the target line length for any one of multiple load nodes. The preset baseline length is represented by , i represents the index of any load node, and Q represents the number of multiple load nodes.

[0038] Optionally, the line attenuation factor can be calculated based on the target line lengths corresponding to multiple load nodes. This is achieved by constructing a weighted average exponential attenuation model, spatially aggregating the voltage drop suppression effect of the electrical distance of each load node in the target distribution network, thereby obtaining a comprehensive attenuation index reflecting the overall transmission capacity of the entire network. In this way, the target line length of each load node undergoes a nonlinear transformation through an exponential attenuation function. When the target line length approaches or exceeds the preset baseline line length, the voltage drop will worsen exponentially. The preset attenuation coefficient can be obtained through historical experience fitting, calibrated according to different voltage levels or conductor types to ensure close alignment with actual engineering conditions. The preset baseline coefficient can be set to 1 for normalization of the reference state. Finally, the arithmetic mean of the attenuation values ​​of all load nodes can achieve a holistic characterization of the impedance distribution characteristics of the entire network, rather than relying solely on a single point or the extreme value at the farthest end. This makes the line attenuation factor a dynamic index that can sensitively respond to changes in the target distribution network structure.

[0039] In one optional embodiment, a load type factor is obtained based on the power, power factor, and harmonic factors corresponding to each of the multiple load nodes, including: obtaining the load type factor based on the power, power factor, and harmonic factors corresponding to each of the multiple load nodes in the following manner:

[0040] ;

[0041] in, Indicates the load type factor. This represents the power of any one of multiple load nodes. This represents the preset load correction factor for any load node, which is derived based on the power factor of the corresponding load node. Let represent the harmonic factor of any load node, i represent the index of any load node, and Q represent the number of load nodes.

[0042] Optionally, a load type factor can be calculated based on the active power, power factor, and harmonic factor corresponding to each of multiple load nodes. This involves integrating the differentiated impacts of different types of loads on voltage stability and power quality. Different types of loads have different demands on the voltage and reactive power of the target distribution network, thus affecting voltage stability and photovoltaic access capacity. Table 1 shows the power factor range and preset load correction coefficient values ​​for an optional linear load according to an embodiment of the present invention. Linear loads, such as inductive loads, need to absorb reactive power, which will exacerbate voltage drops; resistive loads mainly consume active power and have low reactive power demand, resulting in a relatively weak voltage impact. Inductive loads are mainly composed of inductive components, such as asynchronous motors, transformers, reactors, and industrial compressors. Their characteristic is that the current lags behind the voltage. During operation, they continuously absorb a large amount of reactive power to establish a magnetic field, thus exacerbating voltage drops along the transmission path. This is especially problematic at feeder ends or in areas with high line impedance, easily leading to voltage exceeding the lower limit and limiting the local absorption capacity of photovoltaic power. Resistive loads, on the other hand, exhibit almost purely resistive characteristics, such as incandescent lamps, electric heaters, electric kettles, and office electronic equipment. Their current and voltage are in phase, primarily consuming active power with almost no reactive power demand. Therefore, they have a smaller impact on the target distribution network voltage, and photovoltaic power is more easily absorbed smoothly, resulting in a weaker impact on the grid. Nonlinear loads, however, generate harmonics, potentially causing resonance or additional voltage distortion. Nonlinear loads are loads composed of power electronic equipment, such as frequency converters, rectifiers, switching power supplies, LED drivers, and large uninterruptible power supplies. They are characterized by severely distorted current waveforms, no longer exhibiting a sinusoidal relationship with voltage, and injecting large amounts of high-order harmonic currents into the grid, leading to voltage waveform distortion, local resonance, relay protection malfunctions, and power quality degradation. The above formula indicates that the comprehensive impact of each load node is not a simple average, but rather a weighted average of its actual active power, multiplied by a preset load correction coefficient and harmonic factor. The preset load correction coefficient quantifies the weighting factor of the impact of power factor differences on voltage stability for different load types. Its determination follows the causal relationship that a lower power factor leads to greater reactive power demand, more significant voltage drop, and stronger constraints on photovoltaic (PV) adoption. The harmonic factor quantifies the superimposed impact of harmonic currents generated by the corresponding nonlinear load node on the target distribution network impedance, resonance risk, and voltage distortion. The harmonic factor of any load node can be obtained as follows: ,in, This represents the harmonic weighting coefficient for any load node. This harmonic weighting coefficient can be set according to the equipment sensitivity. For example, the weighting of higher harmonics can be appropriately increased for motor equipment. This represents the amplitude of any harmonic current at any load node. The value represents the fundamental current amplitude, h represents the index of any harmonic, and H represents the harmonic order.

[0043] Table 1

[0044]

[0045] In an optional embodiment, a photovoltaic matching factor is obtained based on the preset photovoltaic access capacity and node location corresponding to each of the multiple photovoltaic access nodes, including: obtaining multiple target distances based on the node locations corresponding to each of the multiple photovoltaic access nodes, wherein the multiple target distances correspond one-to-one with the multiple photovoltaic access nodes, and the target distance represents the distance between the node location of the corresponding photovoltaic access node and the location of the load center; and obtaining the photovoltaic matching factor based on the multiple target distances, the preset photovoltaic access capacity and location corresponding to each of the multiple photovoltaic access nodes, in the following manner:

[0046] ;

[0047] in, Indicates the photovoltaic matching factor. This represents the preset photovoltaic access capacity of any one of multiple photovoltaic access nodes. This represents the distance to any one of multiple target distances. denoted by the preset attenuation scale, j represents the index of any photovoltaic access node, N represents the number of photovoltaic access nodes, and exp represents the exponential function.

[0048] Optionally, a photovoltaic matching factor is calculated based on the preset access capacity and spatial location of each of the multiple photovoltaic access nodes. This transforms the electrical distance relationship between photovoltaic output and the load center into a comprehensive quantitative indicator of the photovoltaic access capacity of the target distribution network. Specifically, firstly, the target distance from each photovoltaic access node to the distribution network load center is determined. This target distance is the electrical equivalent distance calculated by comprehensively considering line impedance and topology, and can truly reflect the transmission resistance of electrical energy flowing from the photovoltaic node to the load center. Any target distance can be obtained in the following way. ,in, This represents the shortest path from any photovoltaic access node to the load center. Let represent the impedance magnitude of any segment of the line on the shortest path, and m represent the index of that segment. Then, a preset attenuation scale is introduced as a key parameter to measure the effective absorption radius. The value range of the preset attenuation scale can be [amount missing] of the total length of the target distribution network feeders. This indicates that within a certain distance range, photovoltaic power can be effectively absorbed locally by the load. Beyond this preset attenuation scale, voltage fluctuations and network losses caused by long-distance power transmission will significantly suppress the carrying capacity. Based on this, the above formula uses the preset access capacity of each photovoltaic node as the weight. Nodes closer to the load center have a higher contribution weight due to their smaller target distance, while the contribution of end nodes is significantly suppressed due to their larger target distance. Finally, a photovoltaic matching factor normalized to the interval [0, 1] is obtained through weighted averaging. The larger the value of this photovoltaic matching factor, the more concentrated the photovoltaic distribution is in the dense load area, and the stronger the absorption capacity. In addition, considering the time-series correlation between historical photovoltaic output data and historical load data, the photovoltaic matching factor can be obtained based on the photovoltaic output curve and load curve of any photovoltaic access node on any day in the following way. , ,in, The Pearson correlation coefficient represents the photovoltaic output of any photovoltaic access node and its load. This represents the normalized photovoltaic output of any photovoltaic node on any historical day at any given hour. This represents the normalized load of the load curve of any photovoltaic access node on any historical day at any hour, where t represents the index of any hour and T represents the number of hours included in any historical day. This represents the average photovoltaic output of any photovoltaic access node on any historical day. Let represent the average load of any photovoltaic (PV) access node on any given historical day, and exp represent the exponential function. Compared to static matching methods in related technologies that only consider physical distance, the method for determining the PV matching factor in this embodiment can identify high-value access points that are at the end of the grid but whose output is synchronized with the evening peak load, or inefficient access points that are close to the load center but whose output is misaligned with peak electricity consumption, thereby guiding a more scientific strategy for the coordinated configuration of PV deployment and energy storage.

[0049] In one optional embodiment, the energy storage enhancement factor is obtained based on the energy storage power and energy storage capacity, including: obtaining the energy storage enhancement factor based on the energy storage power and energy storage capacity in the following manner:

[0050] ;

[0051] in, Indicates the energy storage enhancement factor. Indicates energy storage capacity, Indicates the preset baseline energy storage capacity. Indicates energy storage capacity. Indicates the preset baseline energy storage capacity. This indicates the preset efficiency coefficient.

[0052] Optionally, calculating the energy storage enhancement factor based on energy storage power and capacity expands the improvement effect of energy storage devices on the photovoltaic access capacity of the target distribution network from a single capacity or power dimension to a comprehensive compensation capability assessment of the synergistic effect of power and capacity. In the above formula, the preset benchmark energy storage capacity is used to indicate the instantaneous charge and discharge regulation capability of the energy storage device; the energy storage capacity represents the electrical energy that the energy storage device can store or release; the preset efficiency coefficient is used to indicate the comprehensive loss effect of the energy storage device in actual operation, such as charge and discharge efficiency, response delay, and loss. This preset efficiency coefficient can be obtained by constructing a multi-condition electromagnetic transient simulation model under the target distribution network operation scenario and comparing the ratio of the actual photovoltaic access capacity that the target distribution network can improve under different energy storage configurations to the product of the theoretical factor. The method for determining the energy storage enhancement factor in this embodiment is to establish a dual-parameter coupled energy storage efficiency quantification based on power and capacity in the photovoltaic access capacity assessment of the target distribution network. This method can overcome the limitations of related technologies that only use the proportion of energy storage capacity or whether energy storage is configured as a binary judgment, and ignore the effect of power on dynamic voltage regulation. This method can more realistically characterize the actual contribution of energy storage devices in suppressing voltage over-limit and increasing the upper limit of photovoltaic access.

[0053] Step S106: Determine the coupling coefficient of the target distribution network, wherein the coupling coefficient is obtained based on the historical configuration strategies of the target distribution network under multiple historical operating scenarios and the corresponding historical photovoltaic access capacity.

[0054] Optionally, determining the coupling coefficient of the target distribution network involves mining the implicit relationship between historical configuration strategies and historical photovoltaic (PV) access capacity to construct a data-driven global correction parameter. Specifically, the coupling coefficient is not a fixed value set empirically or derived theoretically. Instead, it is based on the actual PV access schemes, energy storage configurations, load structures, and line operating states adopted by the target distribution network under multiple historical operating scenarios (i.e., historical configuration strategies). This is combined with the actual PV capacity that the target distribution network can safely accommodate under the corresponding historical configuration strategies (i.e., historical PV access capacity) to perform inverse modeling. The coupling coefficients under multiple historical scenarios are then fitted to form a coupling coefficient applicable to the target distribution network. Since there are significant differences in the grid structure, load composition, and operating habits of different regional power grids (e.g., dense inductive loads and numerous end-point PV connections in industrial areas, where the voltage drop and reactive power compensation coupling effect is much stronger than in residential areas; or energy storage in a certain area often discharges during the evening peak, resulting in a significant positive enhancement effect when matched with PV), the coupling coefficient obtained through inversion of historical operating data can elevate the evaluation results from theoretical estimation to intelligent inference driven by historical experience, providing a scientific basis for accurately assessing PV access capacity.

[0055] In one optional embodiment, determining the coupling coefficient of the target distribution network includes: obtaining multiple coupling values ​​based on historical configuration strategies corresponding to multiple historical operating scenarios, wherein the coupling values ​​are used to quantify the degree of correlation between multiple operating parameters in the historical configuration strategies of the corresponding historical operating scenarios; determining coupling parameters corresponding to the multiple coupling values ​​based on the historical photovoltaic access capacity corresponding to the multiple historical operating scenarios, wherein the coupling parameters are used to indicate the influence intensity of the corresponding coupling value on the photovoltaic access capacity of the target distribution network; and determining the coupling coefficient based on the multiple coupling values ​​and the coupling parameters corresponding to the multiple coupling values.

[0056] Optionally, firstly, based on the configuration strategies corresponding to the target distribution network in multiple historical operating scenarios, multiple operating parameters are assigned, such as photovoltaic access location, load type distribution, energy storage commissioning status, and line operation mode. Multiple coupling values ​​reflecting the synergistic or restrictive effects among these operating parameters are extracted. Each coupling value is essentially a quantitative representation of a specific interactive behavior (such as long-line end-connection and highly inductive loads), used to characterize the comprehensive influence intensity when multiple operating parameters act together. Table 2 shows an optional method for determining multiple coupling values ​​according to an embodiment of the present invention. Further, combining the historical photovoltaic access capacity under each historical scenario, the actual adjustment effect of the corresponding coupling value on the photovoltaic access capacity is calculated in reverse, i.e., the coupling parameter. This coupling parameter is a weighting coefficient that measures the magnitude of the constraint influence of the coupling value on the photovoltaic access capacity of the target distribution network.

[0057] Table 2

[0058]

[0059] In one optional embodiment, based on the historical photovoltaic access capacity corresponding to each of the multiple historical operating scenarios, coupling parameters corresponding to each of the multiple coupling values ​​are determined, including: obtaining photovoltaic access capacity correction factors corresponding to each of the multiple historical operating scenarios based on the historical photovoltaic access capacity corresponding to each of the multiple historical operating scenarios and a preset benchmark photovoltaic access capacity, wherein the photovoltaic access capacity correction factors are used to indicate the degree of deviation of the historical photovoltaic access capacity in the corresponding historical operating scenario from the preset benchmark photovoltaic access capacity; and obtaining coupling parameters corresponding to each of the multiple coupling values ​​based on the photovoltaic access capacity correction factors corresponding to each of the multiple historical operating scenarios and the multiple coupling values.

[0060] Optionally, the process of determining coupling parameters based on the historical photovoltaic (PV) access capacity corresponding to multiple historical operating scenarios involves inferring the weights of the interaction effects of factors from the actual PV access capacity, thus achieving closed-loop identification from observation data to mechanism parameters. Specifically, firstly, using a preset benchmark PV access capacity as a reference, the ratio of the actual PV capacity of the target distribution network in each historical scenario to this benchmark is calculated to obtain the PV access capacity correction factor for that historical scenario. This correction factor is used to quantify the degree to which the PV capacity that the target distribution network can carry is excessive or insufficient relative to the ideal benchmark under the historical operating conditions. Subsequently, this correction factor is regressed and correlated with the coupling values ​​calculated in each operating scenario to obtain the weight coefficient corresponding to each coupling item, i.e., the coupling parameter. This coupling parameter indicates the degree of deviation of the PV access capacity of the target distribution network from the preset benchmark PV access capacity when a specific interaction (such as long lines and highly inductive loads) occurs, thereby clarifying whether the coupling mechanism enhances or inhibits PV acceptance capacity.

[0061] In one optional embodiment, determining the coupling coefficient based on multiple coupling values ​​and their respective coupling parameters includes: determining the coupling coefficient based on the multiple coupling values ​​and their respective coupling parameters in the following manner:

[0062] ;

[0063] in, Represents the coupling coefficient. This represents the coupling parameter corresponding to any one of multiple coupling values. represents any coupling value, m represents the index of any coupling value, and M represents the number of coupling values.

[0064] Optionally, a coupling coefficient is determined based on multiple coupling values ​​and their corresponding coupling parameters. In the above formula, each coupling value reflects the non-independent, non-linear interaction between multiple operating parameters. The coupling parameters are weighted coefficients obtained through historical data inversion, representing the actual impact of the coupling mechanism on the photovoltaic access capacity in the target distribution network. Positive values ​​indicate that the interaction can enhance the photovoltaic access capacity, while negative values ​​indicate that it forms a constraint. All coupling values ​​and their corresponding coupling parameters are weighted and summed, and then superimposed on the baseline value 1 to form the coupling coefficient. This coupling coefficient is a correction factor that dynamically responds to the current operating state of the target distribution network. The coupling coefficient directly reflects the net effect of the synergistic effect of multiple factors under the current configuration.

[0065] Step S108: Based on the photovoltaic access capacity influence factor and coupling coefficient, determine the actual photovoltaic access capacity of the target distribution network under the current operating scenario.

[0066] Optionally, by determining the actual photovoltaic (PV) grid connection capacity in the current operating scenario based on PV grid connection capacity influencing factors and coupling coefficients, the accuracy of PV grid connection capacity assessment can be improved. This overcomes the shortcomings of related technologies that rely solely on single-factor superposition, leading to assessment results that are either overly optimistic (ignoring negative coupling effects) or overly conservative (not considering positive coupling effects), making it difficult to truly reflect the actual acceptance capacity of the target distribution network under complex operating conditions. By introducing a coupling coefficient, the assessment gains adaptive learning capabilities. In different scenarios such as industrial areas, mixed urban and suburban areas, and old feeders, the coefficient can automatically adjust and correct weights based on historical experience, outputting capacity values ​​that conform to actual operating patterns. For example, when it is detected that the PV grid connection point is far from the load center and the load is mainly composed of motors, the coupling coefficient will automatically apply a negative correction, reducing the recommended capacity. Conversely, if the energy storage configuration is reasonable and highly matched with the load timing, the coupling coefficient will actively increase the capacity limit, releasing potential absorption space.

[0067] In an optional embodiment, when the photovoltaic (PV) grid connection capacity influencing factors include line attenuation factor, load type factor, PV matching factor, and energy storage enhancement factor, the actual PV grid connection capacity of the target distribution network under the current operating scenario is determined based on the PV grid connection capacity influencing factors and the coupling coefficient. This includes determining the actual PV grid connection capacity based on the line attenuation factor, load type factor, PV matching factor, energy storage enhancement factor, and coupling coefficient in the following manner:

[0068] ;

[0069] in, Indicates the actual photovoltaic grid connection capacity. Indicates the preset baseline photovoltaic grid connection capacity. The line attenuation factor is used to quantify the degree to which the target line length of the target distribution network suppresses voltage drop during photovoltaic power transmission. This represents the load type factor, which is used to quantify the degree of impact of the load on the voltage of the target distribution network. The photovoltaic matching factor is used to quantify the degree of matching between the spatial distribution of multiple photovoltaic access nodes in the target distribution network and the load center of the target distribution network. This represents the energy storage enhancement factor, which is used to quantify the degree to which energy storage devices in the target distribution network compensate for the actual photovoltaic access capacity of the target distribution network. This represents the coupling coefficient.

[0070] Optionally, assuming that the photovoltaic (PV) grid connection capacity influencing factors include line attenuation factors, load type factors, PV matching factors, and energy storage enhancement factors, the actual PV grid connection capacity of the target distribution network under the current operating scenario is determined using the above formula. These four PV grid connection capacity influencing factors collectively serve as the basic framework for evaluating PV grid connection capacity, but they do not yet capture the nonlinear synergistic effects between elements in actual operation. In this case, a coupling coefficient is introduced as a key correction term. Multiple known coupling mechanisms are identified and weighted through historical operating data inversion, and the interactive effects are mathematically embedded into the formula, ultimately yielding a comprehensive PV grid connection capacity that reflects the true carrying capacity of the target distribution network. The actual PV grid connection capacity obtained through the calculation method of this embodiment can significantly improve the accuracy of the evaluation and its engineering practicality, supporting the safe and intelligent coordinated operation of the target distribution network under a high proportion of distributed PV grid connection. Furthermore, based on the calculated actual PV grid connection capacity, the sensitivity of each PV grid connection capacity influencing factor to the actual PV grid connection capacity can be calculated. The sensitivity corresponding to any PV grid connection capacity influencing factor can be obtained in the following way. ,in, This represents any photovoltaic (PV) grid connection capacity influencing factor. This sensitivity reflects the response magnitude of the actual PV grid connection capacity to a small change in a particular influencing factor. For example, even if the line attenuation factor is small, its sensitivity may still be the highest if it is strongly correlated with multiple coupling terms (such as significant interaction with spatial matching degree and load type), indicating that it is exerting a limiting effect beyond its surface value under the current configuration. By comparing the sensitivity of all PV grid connection capacity influencing factors, the reasons limiting PV grid connection capacity can be identified. For example, if the line factor contributes the most, it indicates that voltage drop is the core issue, and reactive power support should be strengthened or the line upgraded; if the PV matching factor dominates, it indicates a serious mismatch between the PV grid connection location and load distribution, requiring replanning of grid connection points or deployment of distributed energy storage for local consumption; if the load type factor is prominent, it suggests excessive inductive or harmonic loads, requiring targeted installation of dynamic compensation equipment.

[0071] Through the above steps S102 to S108, the influence factors of photovoltaic access capacity can be determined based on the current configuration strategy of the target distribution network. Combined with the coupling coefficient determined based on historical configuration strategies and historical photovoltaic access capacity, the actual photovoltaic access capacity of the target distribution network under the current operating scenario can be accurately determined. This achieves the technical effect of improving the accuracy of the actual photovoltaic access capacity assessment of the distribution network, and solves the technical problem in related technologies that the photovoltaic access capacity assessment of the distribution network is not fully considered when facing complex distribution network operating scenarios, resulting in low accuracy of the actual photovoltaic access capacity assessment of the distribution network.

[0072] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 3 This is a flowchart of an optional method for determining the photovoltaic access capacity of a distribution network according to an embodiment of the present invention, such as... Figure 3 As shown, the method includes:

[0073] S1: Obtain historical operating data of the target distribution network and historical meteorological data of the area where the target distribution network is located. Specifically, this includes: comprehensively obtaining the topology, line parameters, historical load data, historical photovoltaic power output data, historical energy storage configuration information and corresponding historical meteorological data of the target distribution network through multi-source terminals such as distribution network automation systems, smart meters, and meteorological monitoring stations.

[0074] S2: Construct multiple historical operation scenarios for the target distribution network. Specifically, this includes using a joint clustering algorithm of historical load data and historical photovoltaic output data of the target distribution network to obtain multiple historical operation scenarios. Each historical operation scenario not only includes the corresponding photovoltaic and load power data, but also associates it with its corresponding meteorological characteristics and assigns natural meteorological language labels to the meteorological characteristics, thereby realizing a semantic expression from abstract data to an understandable operation mode. The specific implementation process is the same as the aforementioned embodiments, and will not be repeated here.

[0075] S3: Based on the historical configuration strategies corresponding to each of the target distribution network under multiple historical operating scenarios, and the historical photovoltaic access capacity corresponding to each of the multiple historical operating scenarios, the coupling coefficient of the target distribution network is obtained. Specifically, this includes: First, obtaining multiple coupling values ​​based on the historical configuration strategies corresponding to each of the multiple historical operating scenarios; then, determining the coupling parameters corresponding to each of the multiple coupling values ​​based on the historical photovoltaic access capacity corresponding to each of the multiple historical operating scenarios; further, obtaining the coupling coefficient based on the multiple coupling values ​​and the coupling parameters corresponding to each of the multiple coupling values. The specific implementation process is the same as in the aforementioned embodiments, and will not be repeated here.

[0076] S4: Based on the current configuration strategy of the target distribution network, determine the photovoltaic access capacity impact factors of the target distribution network under the current operating scenario. Specifically, this includes: calculating the road attenuation factor, load type factor, photovoltaic matching factor, and energy storage enhancement factor based on the current configuration strategy. The road attenuation factor reflects the aggravated voltage drop effect caused by the increase in line length; the load type factor is used to indicate the differentiated impact of different loads on voltage stability and reactive power demand; the photovoltaic matching factor is used to assess the geographical and electrical proximity of the photovoltaic access point spatial distribution to the load center; and the energy storage enhancement factor is used to measure the ability of energy storage devices to compensate for and smooth voltage fluctuations during charging and discharging. The specific implementation process is the same as in the aforementioned embodiments and will not be repeated here.

[0077] S5: Based on the photovoltaic access capacity influence factor and coupling coefficient, determine the actual photovoltaic access capacity of the target distribution network under the current operating scenario. The specific implementation process is the same as the aforementioned embodiment, and will not be repeated here.

[0078] S6: Calculate the sensitivity of photovoltaic access capacity influencing factors to actual photovoltaic access capacity. Specifically, this includes: quantifying the relative contribution of each photovoltaic access capacity influencing factor to the actual photovoltaic access capacity through sensitivity analysis, identifying influencing factors that limit the improvement of photovoltaic access capacity, and generating targeted improvement suggestions accordingly. The specific implementation process is the same as the aforementioned embodiments and will not be repeated here.

[0079] This embodiment can achieve at least one of the following effects: (1) In the photovoltaic access capacity assessment, photovoltaic data and load data are jointly clustered to retain the time-series characteristics of each, and can automatically distinguish between "resource-constrained" scenarios (such as dust storms and cloudy weather leading to insufficient photovoltaic output) and "grid-constrained" scenarios (such as peak loads leading to voltage drops), avoiding information loss caused by net load clustering; (2) After clustering, weather data is used to interpret the results, which not only retains the causal logic of weather as an independent variable, but also ensures the effectiveness of the clustering results for photovoltaic access capacity calculation, realizing a complete technical chain from weather to operation mode to photovoltaic access capacity; (3) The line, load, photovoltaic, and energy storage are evaluated in the same framework, and the "single element" one-sided conclusions are avoided through coupling coefficient correction.

[0080] This embodiment also provides a photovoltaic access capacity determination device for a distribution network. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0081] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described method for determining the photovoltaic access capacity of a distribution network is also provided. Figure 4 This is a schematic diagram of a photovoltaic access capacity determination device for a distribution network according to an embodiment of the present invention, as shown below. Figure 4 As shown, the photovoltaic access capacity determination device for the aforementioned distribution network includes: a current configuration strategy acquisition module 400, an influencing factor determination module 402, a coupling coefficient determination module 404, and a photovoltaic access capacity determination module 406, wherein:

[0082] The current configuration strategy acquisition module 400 is used to acquire the current configuration strategy of the target distribution network. The current configuration strategy includes the target line length, power, power factor and harmonic factor of each load node in the target distribution network under the current operating scenario, the preset photovoltaic access capacity and node location of each photovoltaic access node, the energy storage power and energy storage capacity of the energy storage device, and the target line length represents the line length between the power source and the corresponding load node in the target distribution network.

[0083] The influence factor determination module 402 is connected to the current configuration strategy acquisition module 400. It is used to determine the photovoltaic access capacity influence factor of the target distribution network under the current operating scenario based on the current configuration strategy. The photovoltaic access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual photovoltaic access capacity of the target distribution network.

[0084] The coupling coefficient determination module 404 is connected to the influence factor determination module 402 and is used to determine the coupling coefficient of the target distribution network. The coupling coefficient is obtained based on the historical configuration strategies of the target distribution network under multiple historical operating scenarios and the corresponding historical photovoltaic access capacity.

[0085] The photovoltaic access capacity determination module 406 is connected to the coupling coefficient determination module 404 and is used to determine the actual photovoltaic access capacity of the target distribution network under the current operating scenario based on the photovoltaic access capacity influence factor and the coupling coefficient.

[0086] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0087] It should be noted that the aforementioned current configuration strategy acquisition module 400, influence factor determination module 402, coupling coefficient determination module 404, and photovoltaic access capacity determination module 406 correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run on a computer terminal.

[0088] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0089] The aforementioned photovoltaic access capacity determination device for the distribution network may also include a processor and a memory. The aforementioned current configuration strategy acquisition module 400, influence factor determination module 402, coupling coefficient determination module 404, photovoltaic access capacity determination module 406, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0090] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0091] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device containing the non-volatile storage medium to execute any of the photovoltaic access capacity determination methods for the power distribution network.

[0092] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0093] Optionally, during program execution, the device containing the non-volatile storage medium is controlled to perform the following functions: Obtain the current configuration strategy of the target distribution network, wherein the current configuration strategy includes the target line length, power, power factor, and harmonic factor corresponding to each of the multiple load nodes in the target distribution network under the current operating scenario; the preset photovoltaic access capacity and node location corresponding to each of the multiple photovoltaic access nodes; the energy storage power and energy storage capacity of the energy storage device; and the target line length represents the line length between the power source and the corresponding load node in the target distribution network. Based on the current configuration strategy, determine the photovoltaic access capacity influence factor of the target distribution network under the current operating scenario, wherein the photovoltaic access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual photovoltaic access capacity of the target distribution network. Determine the coupling coefficient of the target distribution network, wherein the coupling coefficient is obtained based on the historical configuration strategies corresponding to each of the target distribution network under multiple historical operating scenarios, and the corresponding historical photovoltaic access capacity. Based on the photovoltaic access capacity influence factor and the coupling coefficient, determine the actual photovoltaic access capacity of the target distribution network under the current operating scenario.

[0094] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described methods for determining the photovoltaic access capacity of a distribution network.

[0095] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of the method for determining the photovoltaic access capacity of a distribution network as described above.

[0096] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: obtaining the current configuration strategy of the target distribution network, wherein the current configuration strategy includes the target line length, power, power factor and harmonic factor corresponding to each of the multiple load nodes in the target distribution network under the current operating scenario, the preset photovoltaic access capacity and node location corresponding to each of the multiple photovoltaic access nodes, the energy storage power and energy storage capacity of the energy storage device, and the target line length representing the line length between the power source and the corresponding load node in the target distribution network; determining the photovoltaic access capacity influence factor of the target distribution network under the current operating scenario based on the current configuration strategy, wherein the photovoltaic access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual photovoltaic access capacity of the target distribution network; determining the coupling coefficient of the target distribution network, wherein the coupling coefficient is obtained based on the historical configuration strategies corresponding to each of the target distribution network under multiple historical operating scenarios, and the corresponding historical photovoltaic access capacity; and determining the actual photovoltaic access capacity of the target distribution network under the current operating scenario based on the photovoltaic access capacity influence factor and the coupling coefficient.

[0097] like Figure 5As shown, this embodiment of the invention provides an electronic device 10, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: obtaining the current configuration strategy of the target distribution network, wherein the current configuration strategy includes the target line length, power, power factor, and harmonic factor corresponding to each of the multiple load nodes in the target distribution network under the current operating scenario, the preset photovoltaic access capacity and node location corresponding to each of the multiple photovoltaic access nodes, the energy storage power and energy storage capacity of the energy storage device, and the target line length representing the line length between the power source and the corresponding load node in the target distribution network; determining the photovoltaic access capacity influence factor of the target distribution network under the current operating scenario based on the current configuration strategy, wherein the photovoltaic access capacity influence factor is used to quantify the degree of influence of the current configuration strategy on the actual photovoltaic access capacity of the target distribution network; determining the coupling coefficient of the target distribution network, wherein the coupling coefficient is obtained based on the historical configuration strategies corresponding to each of the target distribution network under multiple historical operating scenarios, and the corresponding historical photovoltaic access capacity; and determining the actual photovoltaic access capacity of the target distribution network under the current operating scenario based on the photovoltaic access capacity influence factor and the coupling coefficient.

[0098] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0099] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0101] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0102] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0103] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this 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. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0104] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the photovoltaic (PV) grid connection capacity of a distribution network, characterized in that, include: Obtain the current configuration strategy of the target distribution network, wherein the current configuration strategy includes the target line length, power, power factor and harmonic factor corresponding to each of the multiple load nodes in the target distribution network under the current operating scenario, the preset photovoltaic access capacity and node location corresponding to each of the multiple photovoltaic access nodes, the energy storage power and energy storage capacity of the energy storage device, and the target line length represents the line length between the power source and the corresponding load node in the target distribution network; Based on the current configuration strategy, the photovoltaic access capacity impact factor of the target distribution network under the current operating scenario is determined, wherein the photovoltaic access capacity impact factor is used to quantify the degree of impact of the current configuration strategy on the actual photovoltaic access capacity of the target distribution network; The coupling coefficient of the target distribution network is determined, wherein the coupling coefficient is obtained based on the historical configuration strategies of the target distribution network under multiple historical operating scenarios and the corresponding historical photovoltaic access capacity. Based on the photovoltaic access capacity influence factor and the coupling coefficient, the actual photovoltaic access capacity of the target distribution network under the current operating scenario is determined.

2. The method according to claim 1, characterized in that, Before obtaining the current configuration strategy of the target distribution network, the method further includes: Cluster analysis is performed on the historical operating data of the target distribution network and the historical meteorological data of the area where the target distribution network is located to obtain multiple clusters. The historical operating data includes at least historical photovoltaic power output data and historical load data for historical periods. The historical meteorological data includes at least irradiance, ambient temperature and cloud cover for the historical periods. The multiple clusters correspond to a historical operating scenario of the target distribution network. The historical periods are periods of a predetermined duration prior to the current period. Meteorological natural language tagging is performed on the multiple clusters respectively to obtain the meteorological semantic tags corresponding to each of the multiple clusters; Based on the multiple clusters and their respective meteorological semantic tags, the multiple historical operating scenarios are obtained.

3. The method according to claim 1, characterized in that, When the photovoltaic (PV) grid connection capacity influencing factors include line attenuation factors, load type factors, PV matching factors, and energy storage enhancement factors, determining the PV grid connection capacity influencing factors of the target distribution network under the current operating scenario based on the current configuration strategy includes: Based on the target line length corresponding to each of the multiple load nodes, the line attenuation factor is obtained, wherein the line attenuation factor is used to quantify the degree to which the target line length of the target distribution network suppresses voltage drop during photovoltaic power transmission. Based on the power, power factor and harmonic factor corresponding to each of the multiple load nodes, the load type factor is obtained, wherein the load type factor is used to quantify the degree of influence of the load on the voltage of the target distribution network; Based on the preset photovoltaic access capacity and the node location corresponding to each of the plurality of photovoltaic access nodes, the photovoltaic matching factor is obtained, wherein the photovoltaic matching factor is used to quantify the degree of matching between the spatial distribution of the plurality of photovoltaic access nodes and the load center of the target distribution network; Based on the energy storage power and the energy storage capacity, the energy storage enhancement factor is obtained, wherein the energy storage enhancement factor is used to quantify the degree of compensation of the energy storage device for the actual photovoltaic access capacity of the target distribution network.

4. The method according to claim 3, characterized in that, The process of obtaining the line attenuation factor based on the target line length corresponding to each of the multiple load nodes includes: Based on the target line length corresponding to each of the multiple load nodes, the line attenuation factor is obtained in the following manner: ； in, This represents the line attenuation factor. Indicates the preset baseline coefficient. Indicates the preset attenuation coefficient. This represents the target line length for any one of the plurality of load nodes. The preset baseline length is represented by , i represents the index of any load node, and Q represents the number of load nodes.

5. The method according to claim 3, characterized in that, The load type factor is obtained based on the power, power factor, and harmonic factor corresponding to each of the plurality of load nodes, including: Based on the power, power factor, and harmonic factor corresponding to each of the plurality of load nodes, the load type factor is obtained in the following manner: ； in, This represents the load type factor. This represents the power of any one of the plurality of load nodes. This represents a preset load correction factor for any given load node, which is obtained based on the power factor of the corresponding load node. The harmonic factor represents any one of the load nodes, i represents the index of any one load node, and Q represents the number of the plurality of load nodes.

6. The method according to claim 3, characterized in that, The process of obtaining the photovoltaic matching factor based on the preset photovoltaic access capacity and the node location corresponding to each of the plurality of photovoltaic access nodes includes: Based on the node positions corresponding to each of the plurality of photovoltaic access nodes, a plurality of target distances are obtained, wherein the plurality of target distances correspond one-to-one with the plurality of photovoltaic access nodes, and the target distance represents the distance between the node position of the corresponding photovoltaic access node and the position of the load center; Based on the multiple target distances, the preset photovoltaic access capacity corresponding to each of the multiple photovoltaic access nodes, and the location, the photovoltaic matching factor is obtained in the following manner: ； in, This represents the photovoltaic matching factor. This refers to the preset photovoltaic access capacity of any one of the plurality of photovoltaic access nodes. This represents any one of the plurality of target distances. The preset attenuation scale is represented by j, which represents the index of any photovoltaic access node, and N represents the number of photovoltaic access nodes. This represents an exponential function.

7. The method according to claim 3, characterized in that, The process of obtaining the energy storage enhancement factor based on the energy storage power and the energy storage capacity includes: Based on the energy storage power and the energy storage capacity, the energy storage enhancement factor is obtained in the following manner: ； in, This represents the energy storage enhancement factor. This indicates the energy storage capacity. Indicates the preset baseline energy storage capacity. Indicates the energy storage power, Indicates the preset baseline energy storage capacity. This indicates the preset efficiency coefficient.

8. The method according to claim 1, characterized in that, Determining the coupling coefficient of the target distribution network includes: Based on the historical configuration strategies corresponding to each of the multiple historical operation scenarios, multiple coupling values ​​are obtained, wherein the coupling values ​​are used to quantify the degree of correlation between multiple operation parameters in the historical configuration strategies of the corresponding historical operation scenarios; Based on the historical photovoltaic access capacity corresponding to each of the multiple historical operating scenarios, the coupling parameters corresponding to each of the multiple coupling values ​​are determined, wherein the coupling parameters are used to indicate the intensity of the influence of the corresponding coupling value on the photovoltaic access capacity of the target distribution network; The coupling coefficient is determined based on the plurality of coupling values ​​and the coupling parameters corresponding to each of the plurality of coupling values.

9. The method according to claim 8, characterized in that, The step of determining the coupling parameters corresponding to each of the multiple coupling values ​​based on the historical photovoltaic access capacity corresponding to each of the multiple historical operating scenarios includes: Based on the historical photovoltaic access capacity and preset benchmark photovoltaic access capacity corresponding to each of the multiple historical operating scenarios, a photovoltaic access capacity correction factor corresponding to each of the multiple historical operating scenarios is obtained. The photovoltaic access capacity correction factor is used to indicate the degree of deviation of the historical photovoltaic access capacity relative to the preset benchmark photovoltaic access capacity under the corresponding historical operating scenario. Based on the photovoltaic access capacity correction factors corresponding to multiple historical operating scenarios and the multiple coupling values, the coupling parameters corresponding to each of the multiple coupling values ​​are obtained.

10. The method according to claim 8, characterized in that, The step of determining the coupling coefficient based on the plurality of coupling values ​​and the coupling parameters corresponding to each of the plurality of coupling values ​​includes: Based on the plurality of coupling values ​​and the coupling parameters corresponding to each of the plurality of coupling values, the coupling coefficient is determined in the following manner: ； in, This represents the coupling coefficient. This represents the coupling parameter corresponding to any one of the plurality of coupling values. Let m represent any of the coupling values, m represent the index of any of the coupling values, and M represent the number of the plurality of coupling values.

11. The method according to any one of claims 1 to 10, characterized in that, When the photovoltaic (PV) grid connection capacity influencing factors include line attenuation factor, load type factor, PV matching factor, and energy storage enhancement factor, determining the actual PV grid connection capacity of the target distribution network under the current operating scenario based on the PV grid connection capacity influencing factors and the coupling coefficient includes: Based on the line attenuation factor, the load type factor, the photovoltaic matching factor, the energy storage enhancement factor, and the coupling coefficient, the actual photovoltaic grid connection capacity is determined as follows: ； in, This indicates the actual photovoltaic grid connection capacity. Indicates the preset baseline photovoltaic grid connection capacity. The line attenuation factor is used to quantify the degree to which the target line length of the target distribution network suppresses voltage drop during photovoltaic power transmission. This refers to the load type factor, which is used to quantify the degree of influence of the load on the voltage of the target distribution network; The photovoltaic matching factor is used to quantify the degree of matching between the spatial distribution of multiple photovoltaic access nodes in the target distribution network and the load center of the target distribution network. The energy storage enhancement factor is used to quantify the degree of compensation of the energy storage devices in the target distribution network to the actual photovoltaic access capacity of the target distribution network. This represents the coupling coefficient.

12. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining the photovoltaic access capacity of the distribution network according to any one of claims 1 to 11.

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