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

Abstract

The application discloses a kind of photovoltaic access capacity determination method of distribution network, storage medium and electronic equipment.It relates to new energy and energy-saving technical field, the method includes: based on the current meteorological information of the region where distribution network is located, determine the multiple target operating scenarios of distribution network in prediction period, wherein, current meteorological information at least includes: the irradiance data and cloud layer moving speed data of current period, prediction period is the period of predetermined length after current period;Determine the candidate photovoltaic access capacity of distribution network under multiple target operating scenarios respectively;Based on the candidate photovoltaic access capacity of distribution network under multiple target operating scenarios respectively, determine the target photovoltaic access capacity of distribution network in prediction period.The application solves the technical problem of low prediction accuracy of photovoltaic access capacity of distribution network caused by incomplete consideration in photovoltaic access capacity determination method in the related art.

Description

Technical Field

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

[0002] As the penetration rate of distributed photovoltaic (PV) power in distribution networks continues to rise, distributed PV has evolved from a supplementary power source to a core element affecting the safe and stable operation of the system. However, related technologies have significant shortcomings in determining the PV access capacity of distribution networks, mainly in the following aspects:

[0003] Based on the load curve and photovoltaic output curve of a typical day, a single maximum connectable capacity value is output by solving the deterministic power flow equations time-by-time. This fails to reflect the inherent spatiotemporal randomness of photovoltaic output, resulting in overly conservative or aggressive photovoltaic access capacity assessment results. Furthermore, the photovoltaic access capacity determination methods in related technologies cannot reflect the dynamic evolution of the distribution network's carrying capacity at the intraday scale due to photovoltaic output fluctuations, load changes, and flexible resource status switching. This leads to significant safety blind spots in static assessment results under rapidly changing scenarios such as sudden changes in irradiance and load surges. Therefore, it can be seen that due to incomplete consideration of factors in the photovoltaic access capacity determination methods in related technologies, the prediction accuracy of photovoltaic access capacity for distribution networks is low, and the deviation from actual operation is large.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method for determining the photovoltaic (PV) access capacity of a distribution network, a storage medium, and an electronic device, to at least solve the technical problem of low accuracy in predicting the PV access capacity of a distribution network caused by incomplete consideration of factors in related technologies.

[0006] According to one aspect of the present invention, a method for determining the photovoltaic (PV) access capacity of a distribution network is provided, comprising: determining multiple target operating scenarios of the distribution network during a forecast period based on current meteorological information of the area where the distribution network is located, wherein the current meteorological information includes at least: irradiance data and cloud movement speed data for the current period, and the forecast period is a period of predetermined duration after the current period; determining candidate PV access capacities of the distribution network under the multiple target operating scenarios; and determining the target PV access capacity of the distribution network during the forecast period based on the candidate PV access capacities of the distribution network under the multiple target operating scenarios, wherein the target PV access capacity represents the PV access capacity of the distribution network under a preset confidence level.

[0007] 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 target operating scenario determination module, configured to determine multiple target operating scenarios of the distribution network during a forecast period based on current meteorological information of the area where the distribution network is located, wherein the current meteorological information includes at least: irradiance data and cloud movement speed data for the current period, and the forecast period is a period of predetermined duration after the current period; a candidate PV access capacity determination module, configured to determine candidate PV access capacities of the distribution network under the multiple target operating scenarios; and a target PV access capacity determination module, configured to determine the target PV access capacity of the distribution network during the forecast period based on the candidate PV access capacities of the distribution network under the multiple target operating scenarios, wherein the target PV access capacity represents the PV access capacity of the distribution network under a preset confidence level.

[0008] According to another aspect of the present invention, a non-volatile storage medium is also provided, which stores multiple instructions adapted for a method for determining the photovoltaic access capacity of a distribution network, any one of which is loaded and executed by a processor.

[0009] 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 any one of the photovoltaic access capacity determination methods for a distribution network.

[0010] 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 a method for determining the photovoltaic access capacity of a distribution network.

[0011] In this embodiment of the invention, multiple target operating scenarios for the distribution network during the forecast period are determined based on the current meteorological information of the area where the distribution network is located. The current meteorological information includes at least: irradiance data and cloud movement speed data for the current period. The forecast period is a predetermined duration following the current period. Candidate photovoltaic (PV) access capacities for the distribution network under each of the multiple target operating scenarios are determined. Based on these candidate PV access capacities, the target PV access capacity for the distribution network during the forecast period is determined. The target PV access capacity represents the PV access capacity of the distribution network under a pre-set confidence level. This achieves the goal of constructing multiple target operating scenarios using the current meteorological information of the area where the distribution network is located and calculating the candidate PV access capacities for each of the multiple target operating scenarios, thereby accurately determining the target PV access capacity for the distribution network during the forecast period. This improves the accuracy of PV access capacity prediction for the distribution network and solves the technical problem of low PV access capacity prediction accuracy caused by incomplete consideration of factors in PV access capacity determination methods in related technologies. Attached Figure Description

[0012] 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:

[0013] 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;

[0014] Figure 2 This is a schematic diagram of an optional three-dimensional decision-making method according to an embodiment of the present invention;

[0015] 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;

[0016] 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;

[0017] Figure 5 This 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

[0018] 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.

[0019] 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.

[0020] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0021] Photovoltaic grid connection capacity refers to the maximum active power of distributed photovoltaic systems that a distribution network can safely accept during a specific operating period, provided that all safety operation constraints of the distribution network are met.

[0022] Monte Carlo sampling is a stochastic numerical simulation method based on probability distribution. It generates a sample set that reflects the statistical characteristics of a variable by performing a large number of independent random samplings from the probability density function of the target variable, and then approximates the solution to the uncertainty problem of a complex system.

[0023] 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.

[0024] 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:

[0025] Step S102: Based on the current meteorological information of the area where the distribution network is located, determine multiple target operating scenarios of the distribution network during the forecast period. The current meteorological information includes at least: the irradiance data and cloud movement speed data of the current period. The forecast period is a period of predetermined duration after the current period.

[0026] Optionally, the process of identifying multiple target operating scenarios aims to overcome the limitations of related technologies that rely solely on typical daily or static historical curves for photovoltaic (PV) output. By real-time sensing and modeling of the spatial distribution characteristics of irradiance and the dynamic migration behavior of clouds, a multi-scenario PV output set with real spatiotemporal correlation is constructed. Ultimately, the resulting multiple target operating scenarios can simulate the complex behavior of PV output fluctuating in time series and asynchronously distributed in spatial distribution under specific meteorological conditions. This significantly improves the adaptability of PV grid connection capacity assessment to the uncertainties of real-world operation, ensuring that the assessment results no longer rely on averaged or idealized input assumptions, but rather embed the dynamic randomness driven by meteorology from the root, thus providing an input basis for subsequent PV grid connection capacity calculations.

[0027] In one optional embodiment, based on the current meteorological information of the distribution network area, multiple target operating scenarios of the distribution network during the forecast period are determined, including: determining the irradiance spatiotemporal correlation model of the distribution network during the current period based on the current meteorological information, wherein the irradiance spatiotemporal correlation model is used to indicate the correlation of irradiance intensity among multiple photovoltaic access points in the distribution network; performing Monte Carlo sampling processing on the irradiance spatiotemporal correlation model to obtain multiple initial operating scenarios of the distribution network during the forecast period; and filtering the multiple initial operating scenarios to obtain multiple target operating scenarios.

[0028] Optionally, based on the current meteorological information of the distribution network area, multiple target operating scenarios of the distribution network during the forecast period are determined. First, by analyzing the measured data of the spatial distribution of irradiance intensity and cloud movement speed of each photovoltaic access point during the current period, an irradiance spatiotemporal correlation model reflecting the non-independence of irradiance intensity among multiple photovoltaic access points in the region is constructed. This irradiance spatiotemporal correlation model can be used to quantify the complex coupling relationship between the irradiance of adjacent nodes, which has a time-delay response in time and exhibits a gradient correlation in space due to cloud cover propagation, terrain shading differences, and coordinated fluctuations of meteorological units. This breaks through the simplistic assumption in related technologies that treats the photovoltaic output of each node as an independent random variable. Next, Monte Carlo sampling was performed on the irradiance spatiotemporal correlation model to obtain multiple initial operating scenarios of the distribution network during the prediction period. This process specifically includes: First, based on the constructed irradiance spatiotemporal correlation model, a large number of independent random vectors following a standard normal distribution were randomly generated by computer. Each vector dimension corresponds to the irradiance state of all photovoltaic access points during the prediction period. Then, using the inverse function of probability integral transformation, these standard normal random variables were mapped one by one to the marginal cumulative distribution function of irradiance intensity at each node at each time, thereby generating a set of statistically rigorous structures that strictly conform to the original spatiotemporal correlation structure. A multidimensional random sample is used, with each sample completely recording the irradiance intensity evolution sequence of all photovoltaic access points within the prediction period, forming an initial operating scenario with physical consistency and statistical authenticity. This process is repeated multiple times until a preset number of iterations is reached, forming an initial scenario set covering various meteorological disturbance possibilities. This initial scenario set not only preserves the time lag effect of cloud propagation between meteorological regions and the cooperative fluctuation characteristics within the region, but also realistically reproduces the non-independence and random fluctuations of irradiance in the spatiotemporal dimension, providing a high-fidelity and reproducible input basis for subsequent photovoltaic access capacity assessment. Finally, the initial scenario set is screened and clustered using a scenario reduction method, eliminating redundant or low-probability scenarios and retaining a representative few target operating scenarios. This effectively reduces the complexity of subsequent optimization calculations while fully preserving the key probability distribution characteristics and extreme event coverage capabilities of the original scenario set. The method of determining multiple target operating scenarios through this embodiment can significantly improve the accuracy of photovoltaic access capacity prediction in depicting the uncertain environment of real operation, thereby avoiding assessment bias and safety risks caused by ignoring the spatial heterogeneity and temporal evolution of irradiance.

[0029] In an optional embodiment, given that the current meteorological information includes the current average irradiance and the current average cloud movement speed, a spatiotemporal correlation model of irradiance for the distribution network in the current time period is determined based on the current meteorological information. This includes: dividing the area where the distribution network is located into multiple meteorological regions, where each meteorological region corresponds to a variety of different irradiance characteristics; determining multiple spatial correlation coefficients of irradiance for the distribution network in the current time period based on the current average irradiance, where each spatial correlation coefficient corresponds one-to-one with multiple region pairs, each region pair comprising two corresponding meteorological regions within the multiple meteorological regions, and the spatial correlation coefficients indicating the degree of correlation of irradiance intensity between the corresponding two meteorological regions; and determining multiple cloud shading effect factors for the distribution network in the current time period based on the current average cloud movement speed. Multiple cloud shading effect factors are associated with multiple regional pairs, and the cloud shading effect factors are used to indicate the attenuation intensity of the irradiance delay between the corresponding two meteorological regions. Multiple irradiance synchronization fluctuation coefficients of the distribution network in the current time period are determined, where each of the multiple irradiance synchronization fluctuation coefficients corresponds to a different meteorological region, and the irradiance synchronization fluctuation coefficients are used to indicate the correlation of irradiance intensity between multiple photovoltaic access points in the distribution network within the corresponding meteorological region. Based on the photovoltaic output data of the distribution network in the current time period, the uniform distribution characteristics of the distribution network in the current time period are determined, where the uniform distribution characteristics are used to indicate the distribution characteristics of irradiance intensity of the distribution network after probability integral transformation. Based on multiple irradiance spatial correlation coefficients, multiple cloud shading effect factors, irradiance synchronization fluctuation coefficients, and uniform distribution characteristics, an irradiance spatiotemporal correlation model is determined.

[0030] Optionally, given that current meteorological information includes the current average irradiance and the current average cloud movement speed, a structured modeling of the spatial heterogeneity of photovoltaic output under complex geographical and meteorological conditions can be achieved by dividing the area where the distribution network is located into multiple meteorological regions with different irradiance characteristics. Specifically, firstly, for each pair of meteorological regions, the irradiance spatial correlation coefficient of the corresponding meteorological region pair is calculated based on the current average irradiance, quantifying the coordinated change trend of irradiance levels in two adjacent or neighboring meteorological regions at the same time, reflecting the irradiance synchronicity caused by regional climate consistency. Any irradiance spatial correlation coefficient can be obtained in the following way. ,in, This represents the current average irradiance of any meteorological region. This represents the current average irradiance of a specified meteorological area. Any area pair includes both any meteorological area and the specified meteorological area. An index representing any meteorological region. This represents an index for a specified meteorological region. This represents the covariance operation. This represents the standard deviation of the current average irradiance in any meteorological region. This represents the standard deviation of the current average irradiance for a specified meteorological area. Subsequently, based on the current average cloud movement speed, a cloud shading effect factor is derived for each region. This factor is used to establish a model of the time delay and attenuation intensity of the irradiance signal as it propagates from one region to another, thus characterizing the physical process of irradiance propagation time delay caused by cloud movement. Any cloud shading effect factor can be obtained as follows: ,in, Indicates the geographical distance between any meteorological region and a specified meteorological region. This indicates the current average cloud movement speed. Let represent the preset characteristic time constant of the cloud shading effect, and exp represent the exponential function. Next, for each region, the irradiance synchronization fluctuation coefficient among multiple photovoltaic access points within that region is calculated. This coefficient characterizes the high correlation fluctuation characteristics exhibited by nodes within that region due to the influence of the same cloud system, effectively distinguishing between intra-regional synergy and inter-regional differences. Any irradiance synchronization fluctuation coefficient can be obtained as follows: ,in, This represents the set of photovoltaic access points within any meteorological region. This represents the irradiance correlation coefficient between any photovoltaic access point and a designated photovoltaic access point within any meteorological region. This represents the index of any photovoltaic access point. This indicates the index of the specified photovoltaic (PV) access point. Furthermore, using PV output data, by performing a probability integral transformation on the irradiance of each PV access point in the distribution network, the statistical characteristics of the corresponding PV access point in the uniform distribution domain are obtained. This lays the foundation for subsequently constructing a unified probabilistic framework. The uniform distribution characteristics of any PV access point in the current time period can be obtained as follows: ,in, Let f(x) represent the marginal cumulative distribution function of the photovoltaic output data of any photovoltaic access point in the current time period. This represents the photovoltaic output data of any photovoltaic grid connection point in the current time period. This represents the set of multiple photovoltaic access points in the distribution network. Finally, by integrating the aforementioned irradiance spatial correlation coefficient, cloud shading effect factor, irradiance synchronization fluctuation coefficient, and uniform distribution characteristics, a high-dimensional, nonlinear, spatiotemporally coupled irradiance correlation model is constructed. This model can not only distinguish between inter-regional propagation delays and intra-regional coordinated fluctuations, but also unify the modeling logic of edge distributions and related structures, enabling the constructed joint distribution to truly reflect the dynamic evolution mechanism of irradiance. This spatiotemporal irradiance correlation model can take the following joint probability distribution form: ,in, This represents the uniform distribution vector after probability integral transformation (i.e., the uniform distribution feature is in vector form), where multiple elements of the uniform distribution vector represent the uniform distribution features of the corresponding photovoltaic access points. This represents the correlation coefficient matrix, whose elements are determined by the spatial correlation coefficient of irradiation, the cloud cover effect factor, and the irradiation synchronization fluctuation coefficient. Any off-diagonal element is... , , ,in, This refers to any photovoltaic access point in any meteorological region. Indicates any photovoltaic access point within a specified meteorological area. This represents a set of multiple photovoltaic access points in any given meteorological region. Represents a set of multiple photovoltaic access points within a specified meteorological region, where any diagonal element is... ; The cumulative normal distribution function represents the correlation coefficient matrix; This represents the inverse function of the standard normal distribution. The method used in this embodiment to determine the spatiotemporal correlation model of irradiation enables refined and physically interpretable modeling of the spatiotemporal correlation of irradiation. It overcomes the coarse processing methods in related technical approaches that rely on simple correlation coefficients or independence assumptions, enabling the initial operational scenario generated by subsequent Monte Carlo sampling to possess high-fidelity spatiotemporal correlation characteristics driven by real meteorological conditions, thereby improving the physical rationality and statistical representativeness of the initial operational scenario.

[0031] In one optional embodiment, multiple initial running scenarios are filtered to obtain multiple target running scenarios, including: initializing candidate running scenarios, filtering multiple initial running scenarios to obtain multiple target running scenarios in the following manner, wherein a candidate running scenario is any one of the multiple initial running scenarios; determining the Euclidean distances corresponding to each of the multiple remaining initial running scenarios, wherein the Euclidean distance represents the total Euclidean distance between the corresponding remaining initial running scenarios and the candidate running scenario, and the multiple remaining initial running scenarios are the initial running scenarios other than the candidate running scenarios; updating the candidate running scenarios with the smallest Euclidean distance among the multiple remaining initial running scenarios to obtain updated running scenarios; repeating the filtering operation until a preset number of iterations is reached; and obtaining multiple target running scenarios based on the updated running scenarios obtained when the preset number of iterations is reached.

[0032] Optionally, the process of filtering multiple initial running scenarios to obtain multiple target running scenarios is implemented through an iterative clustering filtering mechanism based on the principle of minimizing Euclidean distance. Specifically, firstly, any initial running scenario is selected as an initial candidate scenario, and the total Euclidean distance between each of the remaining initial scenarios and this candidate scenario is calculated. When there are multiple updated running scenarios, the Euclidean distances between each of the multiple updated scenarios and any of the remaining initial scenarios are summed to obtain the Euclidean distance of any of the remaining initial scenarios. The Euclidean distance of any of the remaining initial scenarios can be obtained in the following way. ,in, This represents the photovoltaic output of any one of multiple update scenarios. This represents the index of any update scenario. This indicates the total number of update scenarios. This represents the photovoltaic output for any other initial scenario. The index of any other initial scenario is used. Then, the scenario closest to the current candidate scenario is selected from the remaining initial scenarios and added to the candidate set to form a new updated running scenario. This process is repeated, with each iteration using the current candidate set as a benchmark to find and merge the new scenario with the smallest overall difference from the current candidate set, until a preset number of iterations is reached. The resulting updated running scenario set is the final target running scenario set. Essentially, this method is a greedy scenario reduction strategy. Its core lies in preserving the overall distribution characteristics and key variation patterns of the original initial scenario set to the greatest extent possible with the minimum number of scenarios through successive approximations. This avoids the omission of extreme events or probability distortion that may be caused by random discarding or simple clustering, achieving an optimal balance between computational efficiency and evaluation accuracy. This provides efficient and reliable scenario support for online, high-precision photovoltaic access decisions.

[0033] Step S104: Determine the candidate photovoltaic access capacity of the distribution network under multiple target operating scenarios.

[0034] Optionally, for each selected and retained target operating scenario, the initial photovoltaic access capacity of the target operating scenario is optimized to obtain the upper limit of additional photovoltaic capacity that the distribution network can still safely accept under the target operating scenario. This upper limit of additional photovoltaic capacity is the candidate photovoltaic access capacity under the target operating scenario, thereby changing the determination of photovoltaic access capacity from static determination to scenario-driven determination, so that the final output photovoltaic access capacity has physical interpretability and engineering operability.

[0035] In one optional embodiment, determining the candidate photovoltaic access capacity of the distribution network under multiple target operating scenarios includes: determining the constraints of the distribution network during the prediction period, wherein the constraints are used to indicate the safe operating boundary of the distribution network; and based on the constraints, taking the maximum photovoltaic access capacity of the distribution network under multiple target operating scenarios as the optimization objective, obtaining the candidate photovoltaic access capacity of the distribution network under multiple target operating scenarios.

[0036] Optionally, determining the candidate photovoltaic (PV) access capacity of the distribution network under multiple target operating scenarios involves constructing a multi-dimensional safety constraint system (i.e., constraint conditions) for each target operating scenario, using the real-time PV output distribution as known input. These constraints collectively characterize the limit operating boundary that the distribution network can tolerate under a specific power injection pattern. Based on this, with maximizing the PV access capacity of the distribution network as the optimization objective, the problem is transformed into an efficiently solvable convex optimization problem using mathematical methods such as second-order cone relaxation and successive linearization. This problem is solved independently under each target scenario, thereby obtaining the maximum PV capacity that the distribution network can safely accept under that target operating scenario, which is the candidate PV access capacity corresponding to that target operating scenario. This optimization process ensures that each candidate PV access capacity accurately corresponds to an operating state generated by actual weather evolution, rather than relying on rough calculations based on mean or typical curves.

[0037] In an optional embodiment, when the distribution network includes multiple electrical connection points and multiple branches, and the prediction period includes multiple prediction times, the constraints of the distribution network during the prediction period are determined, including: determining the constraints as follows: the voltage of any electrical connection point among the multiple electrical connection points at any prediction time is within a preset voltage range; the sum of the active power and reactive power of any branch among the multiple branches at any prediction time does not exceed a preset power capacity; in the event of a three-phase short circuit at any electrical connection point, the three-phase short circuit current at any electrical connection point at any prediction time does not exceed a preset breaking current; the actual photovoltaic connection capacity of any electrical connection point at any prediction time does not exceed a preset photovoltaic connection capacity; and in the event of a photovoltaic device connected to any electrical connection point, the equivalent impedance of any electrical connection point at any prediction time is equal to the product of a preset current coefficient and the actual photovoltaic connection capacity.

[0038] Optionally, in complex distribution networks with multiple electrical connection points and branches, and where the forecast period includes multiple forecast times, determining the constraints of the distribution network during the forecast period is achieved by constructing a multi-dimensional dynamic coupling constraint system. Electrical connection points refer to the physical nodes in the distribution network that are electrically connected to the main grid, such as distributed power sources, loads, and energy storage devices. These include, but are not limited to, the low-voltage side busbar of the distribution transformer, feeder branch nodes, user-side grid connection points, grid connection ports of energy storage inverters, and interconnection ports of smart soft switches, as well as photovoltaic connection points. These nodes are the boundaries of power interaction, carrying the transmission and regulation functions of voltage, current, active and reactive power. They are the core observation and control objects for distribution network operation status perception, safety constraint calculation, and carrying capacity assessment. Specifically, firstly, upper and lower limit constraints are applied to the voltage amplitude of each electrical connection point at each forecast time. ,in, This indicates the lower limit of the preset voltage range. This indicates the upper limit of the preset voltage range. This represents the voltage at any electrical connection point at any predicted time. This voltage constraint ensures that the power distribution meets power quality standards at all electrical connection points, avoiding the risk of voltage exceedances caused by photovoltaic backfeeding or sudden load drops. Secondly, a thermal stability limit constraint is applied to the sum of active and reactive power of each branch at each predicted time. ,in, Indicates the preset power capacity. This represents the active power of any branch at any predicted time. This represents the reactive power of any branch at any predicted time. This represents the voltage at any electrical connection point at any predicted time, where i represents the index of the specified electrical connection point, j represents the index of any electrical connection point, and any branch includes both any electrical connection point and the specified electrical connection point. This represents the active power load at any electrical connection point at any predicted time. This represents the reactive power load at any electrical connection point at any predicted time. This represents the active power of distributed photovoltaic power at any electrical connection point at any predicted time. This represents the actual photovoltaic (PV) grid connection capacity at any electrical connection point at any predicted time. This represents the efficiency of distributed photovoltaic systems at any electrical connection point. This represents the irradiance at any electrical connection point at any predicted time. Indicates the preset irradiation intensity. This represents the reactive power of distributed photovoltaic power at any electrical connection point at any predicted time. This represents the operating power factor of distributed photovoltaic systems at any electrical connection point. This represents the active power output of an energy storage device at any electrical connection point at any predicted time. This represents the reactive power output of an energy storage device at any electrical connection point at any predicted time. This represents the active power injected by the intelligent soft switch into any electrical connection point at any predicted time. This represents the reactive power injected by the intelligent soft switch into any electrical connection point at any predicted time. This represents the summation of multiple downstream branches over any given electrical connection point. This represents the set of multiple downstream branches of any electrical connection point. This represents the active power of any downstream branch of any electrical connection point at any predicted time. This represents the reactive power of any downstream branch of any electrical connection point at any predicted time. This represents the index of any downstream branch at any electrical connection point. This power constraint prevents insulation aging or even melting accidents caused by overload heating. Subsequently, a short-circuit current constraint is introduced to address the extreme fault scenario of a three-phase short circuit occurring at any electrical connection point at any time. ,in, This represents the three-phase short-circuit current at any electrical connection point at any predicted time. It represents the equivalent impedance from the power supply side of the distribution network to any electrical connection point at any predicted time. This represents the preset breaking current of the circuit breaker at any electrical connection point at any predicted time. This constraint ensures that the breaking capacity of the circuit breaker at that electrical connection point is sufficient to interrupt the fault current, thus truly reflecting the nonlinear lifting effect of photovoltaic grid connection on the short-circuit level of the distribution network. Furthermore, to avoid physical capacity limitations, an upper limit is set on the photovoltaic connection capacity at each electrical connection point. This constraint ensures that each electrical connection point does not exceed the physical carrying capacity of the grid connection point, transformer, or cable. Finally, the equivalent impedance after photovoltaic connection is expressed as the product of the connection capacity and the inverter short-circuit coefficient. ,in, This represents the current at any electrical connection point after a photovoltaic device is connected at any predicted time. Indicates the preset current coefficient. This represents the voltage at any electrical connection point at any predicted time. This constraint represents the actual photovoltaic (PV) capacity at any electrical connection point at any predicted time. This constraint allows the short-circuit current calculation to change in real time with the PV capacity, thus overcoming the static assumption in related technical methods that treat the short-circuit capacity as a fixed value. Through this constraint, the calculated PV capacity remains true to the actual operating mechanism, significantly improving the engineering reliability and safety margin of the evaluation results.

[0039] In an optional embodiment, when the distribution network includes multiple photovoltaic (PV) access points, based on constraints, and with the optimization objective of maximizing the PV access capacity of the distribution network under multiple target operating scenarios, candidate PV access capacities for the distribution network under multiple target operating scenarios are determined, including: determining the PV access capacity of the distribution network under multiple target operating scenarios in the following manner:

[0040] ;

[0041] in, This indicates the photovoltaic (PV) grid connection capacity of the distribution network under multiple target operating scenarios. This represents the index of any target execution scenario among multiple target execution scenarios. This represents the index of any photovoltaic access point among multiple photovoltaic access points. This represents the photovoltaic (PV) access capacity of any PV access point under any target operating scenario. This indicates the total number of photovoltaic access points.

[0042] Optionally, in the complex structure of a distribution network with multiple photovoltaic access points, in order to evaluate its maximum acceptable photovoltaic capacity in different target operating scenarios, a mathematical modeling method with the optimization objective of maximizing the total access capacity is adopted. Under the premise of satisfying the constraints, the optimization objective is solved, and the optimal photovoltaic access capacity of each photovoltaic access point is allocated. This maximizes the overall photovoltaic access capacity of the distribution network under the corresponding target operating scenario, rather than simply superimposing the limits of a single point. Thus, the global optimal configuration of photovoltaic access capacity is achieved under complex network topology and spatiotemporal coupling operating conditions.

[0043] Step S106: Based on the candidate photovoltaic access capacity of the distribution network under multiple target operating scenarios, determine the target photovoltaic access capacity of the distribution network during the prediction period, where the target photovoltaic access capacity represents the photovoltaic access capacity of the distribution network under a pre-set confidence level.

[0044] Optionally, based on the candidate photovoltaic access capacity calculated by the distribution network under multiple target operating scenarios, the probability distribution of the candidate photovoltaic access capacity under all target operating scenarios is fitted by statistical methods to finally obtain the target photovoltaic access capacity. This target photovoltaic access capacity indicates that, within the range of uncertainty in the operation of the distribution network, the distribution network has a safety margin of no less than the confidence level probability to accept the photovoltaic access of this capacity without breaking through multi-dimensional safety constraints such as voltage, thermal stability, and short circuit.

[0045] In one optional embodiment, the target photovoltaic (PV) access capacity of the distribution network during the prediction period is determined based on the candidate PV access capacities of the distribution network under multiple target operating scenarios and the weights corresponding to each of the multiple target operating scenarios. This includes: obtaining the PV access capacity distribution function of the distribution network during the prediction period based on the target PV access capacities of the distribution network under multiple target operating scenarios and the weight coefficients corresponding to each of the multiple target operating scenarios; determining the confidence interval of the PV access capacity distribution function based on a pre-set confidence level; and determining the lower quantile in the confidence interval as the target PV access capacity.

[0046] Optionally, based on the candidate photovoltaic (PV) access capacity calculated for the distribution network under multiple target operating scenarios, and combined with the weighting coefficients of each target operating scenario, a weighted fusion is used to construct a PV access capacity distribution function reflecting the distribution network's PV access capacity distribution during the forecast period. ,in, Indicates an index for any target runtime scenario. This represents the candidate photovoltaic (PV) grid connection capacity under any target operating scenario. This represents the weighting coefficient under any target operating scenario. The photovoltaic (PV) grid connection capacity distribution function comprehensively characterizes the statistical regularity of the PV capacity that the distribution network can accept under different combinations of weather, load, and flexible resource conditions, rather than a single, fixed value. Based on this PV grid connection capacity distribution function, and according to a pre-set confidence level, the confidence interval of the PV grid connection capacity distribution function is determined as follows: ,in, The quantile function representing the photovoltaic (PV) grid connection capacity distribution function (i.e., the inverse PV grid connection capacity distribution function). This indicates a pre-set confidence level; the lower quantile of this confidence interval is extracted. As the target photovoltaic access capacity, this target photovoltaic access capacity is the maximum safe photovoltaic access capacity of the distribution network in all target operating scenarios with at least this confidence probability of not exceeding the limit. It represents a conservative but reliable engineering decision threshold under the premise of controllable risk. Figure 2 This is a schematic diagram of an optional three-dimensional decision-making method according to an embodiment of the present invention. This "time period-confidence level-capacity" three-dimensional decision-making diagram is an engineering decision-making tool that presents the multi-dimensional uncertainty of the target photovoltaic (PV) grid connection capacity in a unified, visualized, and structured form. The first dimension is the time period (i.e., multiple prediction time periods), used to characterize the time-series characteristics of the PV grid connection capacity dynamically evolving with load and PV output; the second dimension is the confidence level 1- The first dimension reflects the decision-maker's tolerance for security risks; the second dimension is the corresponding carrying capacity (i.e., the target photovoltaic (PV) grid connection capacity), which is visually displayed through color depth or contour line height. The construction of this three-dimensional decision map essentially integrates the previously fragmented time period, confidence level, and carrying capacity into a dynamic, continuous, and interactive decision space. This eliminates the need for decision-makers or planners to repeatedly run numerous simulations; they can simply consult a table to obtain PV grid connection capacity guidance that matches their risk preferences, thereby greatly improving decision-making efficiency and operability.

[0047] Through the above steps S102 to S106, multiple target operating scenarios can be constructed using the current meteorological information of the distribution network area, and the candidate photovoltaic access capacity of the distribution network under each of the multiple target operating scenarios can be calculated. This allows for the accurate determination of the target photovoltaic access capacity of the distribution network during the forecast period, thereby improving the technical accuracy of photovoltaic access capacity prediction for the distribution network. This also solves the technical problem of low photovoltaic access capacity prediction accuracy for the distribution network caused by incomplete consideration of factors in the photovoltaic access capacity determination method in related technologies.

[0048] 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:

[0049] S1: Constructing the constraints of the distribution network, specifically including: establishing safety boundaries including voltage over-limit constraints, line thermal stability constraints, short-circuit constraints, photovoltaic capacity constraints, etc., to achieve dynamic coupling of state variables among multiple constraints. The specific implementation process is the same as the aforementioned embodiments, and will not be repeated here.

[0050] S2: Determine multiple target operating scenarios for the distribution network during the forecast period. Specifically, this includes: dividing the area where the distribution network is located into multiple meteorological zones based on the current irradiance intensity data and cloud movement speed data; constructing an irradiance spatiotemporal correlation model by calculating the irradiance spatial correlation coefficient, cloud shading effect factor, irradiance synchronization fluctuation coefficient within the same meteorological zone, and uniform distribution characteristics between meteorological zones; subsequently generating a large number of initial operating scenarios through Monte Carlo sampling, and reducing the scenarios based on Euclidean distance to retain a representative set of target operating scenarios. The specific implementation process is the same as in the aforementioned embodiments and will not be repeated here.

[0051] S3: Construct an optimization model for the distribution network, specifically including: establishing an optimization framework with the photovoltaic access capacity of each photovoltaic access point in the distribution network during the forecast period as the decision variable and the maximization of the total photovoltaic access capacity of the distribution network as the optimization objective. The specific implementation process is the same as the aforementioned embodiments, and will not be repeated here.

[0052] S4: Determine the candidate photovoltaic access capacity of the distribution network under multiple target operating scenarios. Specifically, for each target operating scenario generated in step S2, take the corresponding photovoltaic output time series data as known input, substitute it into the optimization model constructed in step S3, and solve the candidate photovoltaic access capacity of each target operating scenario under the given constraints in step S1. The specific implementation process is the same as the aforementioned embodiment, and will not be repeated here.

[0053] S5: Determine the target photovoltaic (PV) access capacity of the distribution network during the forecast period. Specifically, this includes: based on the candidate PV access capacities obtained in step S4 for each target operating scenario, and combined with the weight coefficients of each target operating scenario, construct a PV access capacity distribution function for the distribution network during the forecast period; based on this distribution function, extract the lower quantile of its confidence interval according to a pre-set confidence level as the target PV access capacity of the distribution network during the forecast period. This target PV access capacity represents the maximum PV capacity that the distribution network can safely accept under the pre-set confidence level. The specific implementation process is the same as in the aforementioned embodiments and will not be repeated here.

[0054] 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.

[0055] 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 (PV) grid connection capacity determination device for the aforementioned distribution network includes: a target operating scenario determination module 400, a candidate PV grid connection capacity determination module 402, and a target PV grid connection capacity determination module 404, wherein:

[0056] The target operation scenario determination module 400 is used to determine multiple target operation scenarios of the distribution network during the forecast period based on the current meteorological information of the area where the distribution network is located. The current meteorological information includes at least: the current irradiance data and cloud movement speed data, and the forecast period is a period of predetermined duration after the current period.

[0057] The candidate photovoltaic access capacity determination module 402 is connected to the target operating scenario determination module 400 and is used to determine the candidate photovoltaic access capacity of the distribution network under multiple target operating scenarios.

[0058] The target photovoltaic access capacity determination module 404 is connected to the candidate photovoltaic access capacity determination module 402. It is used to determine the target photovoltaic access capacity of the distribution network during the prediction period based on the candidate photovoltaic access capacity of the distribution network under multiple target operating scenarios. The target photovoltaic access capacity represents the photovoltaic access capacity of the distribution network under a preset confidence level.

[0059] 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.

[0060] It should be noted that the target operating scenario determination module 400, candidate photovoltaic access capacity determination module 402, and target photovoltaic access capacity determination module 404 correspond to steps S102 to S106 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 in a computer terminal.

[0061] 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.

[0062] The aforementioned photovoltaic access capacity determination device for the distribution network may also include a processor and a memory. The target operating scenario determination module 400, the candidate photovoltaic access capacity determination module 402, and the target photovoltaic access capacity determination module 404 are all stored in the memory as program modules. The processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] Optionally, during program execution, the device containing the non-volatile storage medium is controlled to perform the following functions: based on the current meteorological information of the distribution network area, determine multiple target operating scenarios of the distribution network during the forecast period, wherein the current meteorological information includes at least: irradiance data and cloud movement speed data for the current period, and the forecast period is a period of predetermined duration after the current period; determine the candidate photovoltaic access capacity of the distribution network under each of the multiple target operating scenarios; based on the candidate photovoltaic access capacity of the distribution network under each of the multiple target operating scenarios, determine the target photovoltaic access capacity of the distribution network during the forecast period, wherein the target photovoltaic access capacity represents the photovoltaic access capacity of the distribution network under a pre-set confidence level.

[0067] 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.

[0068] 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.

[0069] 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: based on the current meteorological information of the area where the distribution network is located, determine multiple target operating scenarios of the distribution network during the forecast period, wherein the current meteorological information includes at least: irradiance data and cloud movement speed data for the current period, and the forecast period is a period of predetermined duration after the current period; determine the candidate photovoltaic access capacity of the distribution network under the multiple target operating scenarios; based on the candidate photovoltaic access capacity of the distribution network under the multiple target operating scenarios, determine the target photovoltaic access capacity of the distribution network during the forecast period, wherein the target photovoltaic access capacity represents the photovoltaic access capacity of the distribution network under a preset confidence level.

[0070] like Figure 5 As 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: based on the current meteorological information of the area where the distribution network is located, it determines multiple target operating scenarios of the distribution network during the forecast period, wherein the current meteorological information includes at least: irradiance data and cloud movement speed data for the current period, and the forecast period is a period of predetermined duration after the current period; it determines the candidate photovoltaic access capacity of the distribution network under the multiple target operating scenarios; based on the candidate photovoltaic access capacity of the distribution network under the multiple target operating scenarios, it determines the target photovoltaic access capacity of the distribution network during the forecast period, wherein the target photovoltaic access capacity represents the photovoltaic access capacity of the distribution network under a preset confidence level.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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: Based on the current meteorological information of the distribution network area, multiple target operating scenarios of the distribution network during the forecast period are determined. The current meteorological information includes at least: irradiance data and cloud movement speed data for the current period. The forecast period is a period of predetermined duration after the current period. Determine the candidate photovoltaic access capacity of the distribution network under the multiple target operating scenarios; Based on the candidate photovoltaic access capacity of the distribution network under the multiple target operating scenarios, the target photovoltaic access capacity of the distribution network during the prediction period is determined, wherein the target photovoltaic access capacity represents the photovoltaic access capacity of the distribution network under a preset confidence level.

2. The method according to claim 1, characterized in that, Based on the current meteorological information of the area where the distribution network is located, multiple target operating scenarios for the distribution network during the forecast period are determined, including: Based on the current meteorological information, a spatiotemporal correlation model of irradiance for the distribution network in the current time period is determined, wherein the spatiotemporal correlation model of irradiance is used to indicate the correlation of irradiance intensity among multiple photovoltaic access points in the distribution network; Monte Carlo sampling was performed on the irradiation spatiotemporal correlation model to obtain multiple initial operating scenarios of the power distribution network during the prediction period; The multiple initial running scenarios are filtered to obtain the multiple target running scenarios.

3. The method according to claim 2, characterized in that, Given that the current meteorological information includes the current average irradiance and the current average cloud movement speed, the step of determining the spatiotemporal correlation model of irradiance for the distribution network in the current time period based on the current meteorological information includes: The area where the power distribution network is located is divided into multiple meteorological zones, wherein the multiple meteorological zones correspond to a variety of different radiation characteristics; Based on the current average irradiance intensity, multiple irradiance spatial correlation coefficients of the distribution network in the current time period are determined. The multiple irradiance spatial correlation coefficients correspond one-to-one with multiple regional pairs. Each of the multiple regional pairs includes two corresponding meteorological regions in the multiple meteorological regions. The irradiance spatial correlation coefficients are used to indicate the degree of correlation of irradiance intensity between the corresponding two meteorological regions. Based on the current average cloud movement speed, multiple cloud shading effect factors of the power distribution network in the current time period are determined, wherein the multiple cloud shading effect factors correspond one-to-one with the multiple regional pairs, and the cloud shading effect factors are used to indicate the attenuation intensity of the irradiance intensity delay between the corresponding two meteorological regions. Determine multiple irradiance synchronization fluctuation coefficients of the distribution network in the current time period, wherein the multiple irradiance synchronization fluctuation coefficients correspond one-to-one with the multiple meteorological areas, and the irradiance synchronization fluctuation coefficients are used to indicate the degree of correlation of irradiance intensity among multiple photovoltaic access points in the distribution network within the corresponding meteorological area; Based on the photovoltaic output data of the distribution network in the current time period, the uniform distribution characteristics of the distribution network in the current time period are determined, wherein the uniform distribution characteristics are used to indicate the distribution characteristics of the irradiance of the distribution network after probability integral transformation; The irradiation spatiotemporal correlation model is determined based on the multiple irradiation spatial correlation coefficients, the multiple cloud shading effect factors, the irradiation synchronization fluctuation coefficient, and the uniform distribution characteristics.

4. The method according to claim 2, characterized in that, The process of filtering the multiple initial running scenarios to obtain the multiple target running scenarios includes: Initialize candidate running scenarios, and filter the plurality of initial running scenarios in the following manner to obtain the plurality of target running scenarios, wherein the candidate running scenario is any one of the plurality of initial running scenarios: Determine the Euclidean distances corresponding to each of the remaining initial running scenarios, wherein the Euclidean distance represents the total Euclidean distance between the remaining initial running scenarios and the candidate running scenario, and the remaining initial running scenarios are the initial running scenarios other than the candidate running scenario among the multiple initial running scenarios; The candidate running scenario is updated by selecting the initial running scenario with the smallest Euclidean distance from the remaining initial running scenarios. Repeat the filtering operation until the preset number of iterations is reached; Based on the updated running scenario obtained when the preset number of iterations is reached, the multiple target running scenarios are obtained.

5. The method according to claim 1, characterized in that, The determination of the candidate photovoltaic access capacity of the distribution network under the multiple target operating scenarios includes: Determine the constraints of the distribution network during the forecast period, wherein the constraints are used to indicate the safe operating boundaries of the distribution network; Based on the constraints, with the optimization objective of maximizing the photovoltaic access capacity of the distribution network under the multiple target operating scenarios, the candidate photovoltaic access capacities of the distribution network under the multiple target operating scenarios are obtained.

6. The method according to claim 5, characterized in that, When the distribution network includes multiple electrical access points and multiple branches, and the prediction period includes multiple prediction times, the constraints for determining the distribution network during the prediction period include: The constraints are determined as follows: The voltage of any one of the plurality of electrical access points is within the preset voltage range at any of the plurality of prediction times. The sum of the active power and reactive power of any one of the multiple branches at any predicted time does not exceed the preset power capacity. In the event of a three-phase short circuit at any of the electrical connection points, the three-phase short-circuit current at any of the electrical connection points at any predicted time shall not exceed the preset breaking current. The actual photovoltaic access capacity of any electrical access point at any predicted time shall not exceed the preset photovoltaic access capacity. When a photovoltaic device is connected to any of the electrical connection points, the equivalent impedance of the electrical connection point at any predicted time is equal to the product of the preset current coefficient and the actual photovoltaic connection capacity.

7. The method according to claim 5, characterized in that, When the distribution network includes multiple photovoltaic (PV) connection points, the step of determining the candidate PV connection capacities of the distribution network under the multiple target operating scenarios based on the constraints, with the optimization objective being to maximize the PV connection capacity of the distribution network under the multiple target operating scenarios, includes: The photovoltaic access capacity of the distribution network under the multiple target operating scenarios is determined in the following manner: ； in, This indicates the photovoltaic grid connection capacity of the power distribution network under the various target operating scenarios. This represents the index of any one of the multiple target running scenarios. This represents the index of any one of the plurality of photovoltaic access points. This represents the photovoltaic access capacity of any photovoltaic access point under any target operating scenario. This indicates the total number of the multiple photovoltaic access points.

8. The method according to any one of claims 1 to 7, characterized in that, The step of determining the target photovoltaic (PV) capacity of the distribution network during the forecast period based on the candidate PV access capacities of the distribution network under multiple target operating scenarios and the weights corresponding to each of the multiple target operating scenarios includes: Based on the target photovoltaic access capacity of the distribution network under the multiple target operating scenarios, and the weight coefficients corresponding to each of the multiple target operating scenarios, the photovoltaic access capacity distribution function of the distribution network during the prediction period is obtained. Based on the preset confidence level, the confidence interval of the photovoltaic access capacity distribution function is determined; The lower quantile in the confidence interval is determined as the target photovoltaic access capacity.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the photovoltaic access capacity determination method for the distribution network according to any one of claims 1 to 8.

10. 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 photovoltaic access capacity determination method for a distribution network as described in any one of claims 1 to 8.

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