Distribution network distributed photovoltaic site selection method, equipment, medium and program product

Abstract

The invention discloses a power distribution network distributed photovoltaic site selection method and device, a medium and a program product, and relates to the technical field of power distribution networks. Wherein the historical operation data comprises node load, radiation intensity and node voltage of each node; calculating a load matching degree index, a loss sensitivity factor index and a voltage out-of-limit index of each node according to the first historical operation data; weighting the load matching degree index, the loss sensitivity factor index and the voltage out-of-limit index, and calculating a comprehensive index of each node of the power distribution network; and determining a site selection result of the power distribution network capable of installing the distributed photovoltaic devices based on the comprehensive index. According to the method, the problems that the loss of the power distribution network is aggravated and the voltage out-of-limit risk is remarkably improved due to the space-time mismatching between the distributed photovoltaic output and the load demand in a site selection result obtained based on a related technology site selection scheme are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and more particularly to a site selection method, equipment, medium and program product for distributed photovoltaic in a distribution network. Background Art

[0002] Distributed photovoltaic power generation has become a core path for achieving high penetration of renewable energy in distribution networks. However, the significant temporal and spatial fluctuations in distributed photovoltaic output, coupled with random variations in load demand, significantly increase the dynamics and uncertainty of distribution network operations. This, in turn, leads to prominent issues such as increased voltage over-limit risks, increased network losses, and degraded voltage stability.

[0003] Existing methods for site selection of distributed photovoltaic (PV) installations in distribution networks fall into two main categories: economy-oriented planning and reliability-oriented planning. Economy-oriented planning focuses on economic indicators such as return on investment and cost per kilowatt-hour (CLE), determining PV installation locations through static cost-benefit analysis. However, it ignores grid operational constraints and output-load matching. Reliability-oriented planning focuses on optimizing operational indicators such as voltage overshoot and network losses, but relies on simplified assumptions or offline simulations, making it difficult to adapt to the high-dimensional, nonlinear characteristics of large-scale distribution networks.

[0004] Therefore, when a high proportion of distributed photovoltaics is connected, it is difficult to achieve coordinated optimization of distribution network security and operation efficiency using the site selection method provided by relevant technologies. Summary of the Invention

[0005] The purpose of the present invention is to provide a site selection method, equipment, medium and program product for distributed photovoltaics in a distribution network. The present invention solves the problem that the site selection results obtained based on relevant technical site selection schemes lead to increased losses in the distribution network and a significant increase in the risk of voltage exceeding the limit due to the temporal and spatial mismatch between distributed photovoltaic output and load demand.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] A first aspect of the present invention provides a method for site selection of distributed photovoltaic power generation in a distribution network, the method comprising:

[0008] Acquire first historical operation data of a distribution network node; wherein the historical operation data includes node load, radiation intensity, and node voltage of each node;

[0009] Calculate the load matching index, loss sensitivity factor index and voltage over-limit index of each node according to the first historical operation data;

[0010] The load matching index, loss sensitivity factor index and voltage over-limit index are weighted to calculate the comprehensive index of each node in the distribution network;

[0011] Based on the comprehensive indicators, the site selection results for the distribution network where distributed photovoltaics can be installed are determined.

[0012] In one implementation, the load matching index is calculated based on the first historical operating data, specifically:

[0013] Clustering algorithm is used to cluster the node load and radiation intensity of each node in each time series, and the solar radiation intensity and load curve of each node in each time series is obtained.

[0014] The Kendall correlation coefficient is used to analyze the correlation between the solar radiation intensity and the load curve, and the matching degree of each node in each time series is obtained;

[0015] The weight of each time series is preset, and the load matching index is calculated based on the weight and matching degree.

[0016] In one implementation, the loss sensitivity factor indicator is calculated as follows:

[0017] Among them, LSFs represents the loss sensitivity factor index value, P ab,L Indicates the active power loss of branch ab, Q ab,L Represents the reactive loss of branch ab, R ab Represents the resistance of branch ab.

[0018] In one implementation, the voltage over-limit indicator is calculated as follows:

[0019] Among them, Risk(U) represents the overvoltage risk value; Pr(U i ) represents the probability of voltage exceeding the limit at the i-th node; S ev (w i ) represents the severity of overvoltage at the i-th node.

[0020] In one implementation, the calculation method of the overvoltage severity at the i-th node is specifically as follows: Among them, U i represents the per-unit voltage value at the ith node, w i Represents the voltage over-limit loss value at the i-th node.

[0021] In one implementation, the method further includes:

[0022] According to the site selection result of the distribution network where distributed photovoltaic installation is possible, obtaining the second historical operation data of the distribution network node corresponding to the site selection result;

[0023] Determining a total capacity constraint, a node capacity constraint, a power flow constraint, a node voltage constraint, and a branch current constraint of the distributed photovoltaic installation based on the second historical operating data;

[0024] Based on total capacity constraints, node capacity constraints, power flow constraints, node voltage constraints, and branch current constraints, a distributed photovoltaic capacity optimization configuration model is established with the lowest annual total cost of photovoltaic power generation as the objective function.

[0025] Solve the capacity optimization configuration model to obtain the capacity configuration of each distributed photovoltaic site selection result.

[0026] In one implementation, the annual total cost is the sum of annual investment cost, annual operation and maintenance cost, and annual network loss cost.

[0027] A second aspect of the present invention provides an electronic device including a memory and a processor;

[0028] a memory for storing a computer program, wherein the computer program includes program instructions;

[0029] The processor is configured to execute the program instructions so that the electronic device performs the steps of a site selection method for distributed photovoltaic power generation in a distribution network as provided in the first aspect of the present invention.

[0030] A third aspect of the present invention provides a computer program product comprising program instructions, which, when executed by an electronic device, enables the electronic device to perform the steps of a site selection method for distributed photovoltaics in a distribution network as provided in the first aspect of the present invention.

[0031] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a computer program. When the computer program is executed by one or more processors, it implements a site selection method for distributed photovoltaics in a distribution network as provided in the first aspect of the present invention.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] In the site selection method, equipment, medium and program product for distributed photovoltaics in a distribution network provided by the present invention, by comprehensively considering the load matching degree between photovoltaic output and node load, network loss sensitivity factor and voltage over-limit risk quantification index, combined with cluster analysis of historical data and entropy weight method weighted index, the site selection of distributed photovoltaics is achieved to reduce network losses and suppress voltage fluctuations. Distflow linearized power flow constraints and typical day clustering methods are introduced to reduce model complexity and achieve capacity optimization configuration of distributed photovoltaics, thereby improving distribution network voltage stability and reducing network losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0035] Figure 1 A schematic diagram of a process for site selection of distributed photovoltaic power generation in a distribution network according to an embodiment of the present invention;

[0036] Figure 2 A graph showing the relationship between active power output and solar intensity of a photovoltaic power station according to an embodiment of the present invention;

[0037] Figure 3 A flowchart for calculating node load matching degree provided by an embodiment of the present invention;

[0038] Figure 4 IEEE33 node diagram provided by an embodiment of the present invention;

[0039] Figure 5 A diagram showing the clustering results of radiation intensity loads provided by an embodiment of the present invention;

[0040] Figure 6 A schematic diagram of node load matching provided by an embodiment of the present invention;

[0041] Figure 7 A diagram of site selection results based on comprehensive index calculations provided by an embodiment of the present invention;

[0042] Figure 8 This is a diagram of the optimized capacity results provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0044] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the claimed function, operation or element, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0045] It should be understood that terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0046] Please refer to Figure 1 , Figure 1 A schematic diagram of a method for selecting a site for distributed photovoltaic power generation in a distribution network according to an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0047] S101, obtaining first historical operation data of a distribution network node; wherein the historical operation data includes node load, radiation intensity, and node voltage of each node.

[0048] In this embodiment, the node refers to the node constituting the network topology diagram of the distribution network, which is also common knowledge in this technical field. Secondly, the load, radiation intensity and voltage of the node are all conventional data, so no detailed explanation is given.

[0049] S102: Calculate a load matching index, a loss sensitivity factor index, and a voltage over-limit index of each node based on the first historical operation data.

[0050] In this embodiment, as far as the load matching index is concerned, the calculation process is as follows: a clustering algorithm is used to cluster the node load and radiation intensity of each node in each time series, and the daily radiation intensity and load curve of each node in each time series are obtained; the Kendall correlation coefficient is used to analyze the degree of correlation between the daily radiation intensity and the load curve, and the matching degree of each node in each time series is obtained; the weight of each time series is preset, and the load matching index is calculated based on the weight and matching degree.

[0051] Specifically, if Figure 3 As shown in the figure, the output curve of distributed photovoltaic is directly related to the local radiation intensity. Therefore, based on the historical operation data, the clustering algorithm is used to cluster the solar radiation intensity and load of the four seasons of spring, summer, autumn and winter, and the typical daily radiation intensity {X sp 、X su 、X au 、X wi}、Load curve {Y sp 、Y su 、Y au 、Y wi}.

[0052] Then use the Kendall correlation coefficient to calculate the matching degree {τ sp , τ su , τ au , τ wi}, set the weight of each season to 0.25, and obtain the matching degree τ of distributed photovoltaic and node load throughout the year, as shown in the following formula:

[0053] τ=0.25τ sp +0.25τ su +0.25τ au +0.25τ wi .

[0054] This embodiment uses the DTW-KMEANS algorithm to cluster the seasonal irradiation and load curves, and combines it with the Kendall correlation coefficient to weightedly quantify the matching degree throughout the year, thereby improving the timing fit between photovoltaic output and node load.

[0055] In terms of the loss sensitivity factor index, the network loss of the power system will become larger and larger as more power flows. After the distributed power source is connected to the distribution network, the relative size of the distributed power source's grid-connected capacity and the system load, the grid-connected location and the operating mode will change the flow direction and size of the system line, thereby affecting the network loss to varying degrees. Considering the size of the distributed power source's grid-connected capacity, the grid-connected connection of a small-capacity distributed power source will reduce the line's grid-connected loss. If the capacity of the distributed photovoltaic is large enough to be able to return power to the grid on the basis of meeting the load, the system's network loss will likely increase. In short, distributed power sources have the advantages of wide distribution, small installed capacity, and power generation that can be consumed locally. The grid-connected location and grid-connected capacity of distributed power sources can be reasonably planned according to the network topology and distribution network load requirements, thereby increasing the transmission power on the distribution network line and reducing network losses. From the perspective of grid loss, network nodes with high loss sensitivity factor indicators LSFs (loss sensitivity factors) are more likely to reduce system network losses after being connected to distributed photovoltaics. The mathematical model of LSFs is:

[0056] Based on the loss sensitivity factor index (LSFs), high-sensitivity nodes are screened and connected to photovoltaics first, optimizing the power flow distribution and reducing line losses across the entire network.

[0057] The integration of distributed photovoltaic systems has a significant impact on the voltage level of the distribution network. Specifically, the integration of distributed photovoltaic systems can easily lead to voltage increases. Therefore, integrating distributed photovoltaic systems at nodes where voltage levels are generally below the normal lower limit can effectively improve the voltage level of the entire system. Conversely, integrating distributed photovoltaic systems at nodes where voltage levels frequently exceed the normal upper limit can cause voltage overshoot and even reverse power flow.

[0058] To more accurately assess the impact of distributed PV integration on distribution network voltage levels and implement appropriate preventive measures, a set of voltage over-limit indicators can be developed based on historical power flow data and combined with a utility risk function. This set of indicators aims to quantify the voltage over-limit risks potentially caused by distributed PV integration and provide decision support for the optimized operation of distribution networks.

[0059] Specifically, the calculation method of the voltage over-limit index is as follows:

[0060] Where: Risk(U) represents the overvoltage risk value of the system; Pr(U i ) represents the probability of voltage exceeding the limit at the i-th node; S ev (w i ) represents the severity of overvoltage at the i-th node.

[0061] It is usually stipulated that when the per-unit value of voltage is between 0.95 and 1.05, it can be considered that the voltage is within the limit. The greater the voltage fluctuation, the greater the severity of the voltage risk. Therefore, the severity of voltage exceeding the limit is expressed by a risk preference utility function:

[0062] Among them, U i represents the per-unit voltage value at the ith node, w i Represents the voltage over-limit loss value at the i-th node.

[0063] This embodiment quantifies the probability and severity of voltage over-limit through a risk preference utility function, preferentially deploying photovoltaics at low-voltage nodes to raise the voltage, and avoiding the risk of reverse power flow at high-voltage nodes, thereby reducing the risk of voltage over-limit.

[0064] S103 , weighting the load matching index, the loss sensitivity factor index, and the voltage over-limit index to calculate a comprehensive index for each node in the distribution network.

[0065] In this embodiment, the loss sensitivity factor (LSF) indicator indicates that network nodes with high loss sensitivity factor (LSFs) values ​​are more likely to reduce system losses after connecting to distributed photovoltaic systems. Therefore, it is a positive indicator in the newly added distributed photovoltaic site selection comprehensive indicators. The load matching index indicates the similarity between the distributed photovoltaic output curve and the load curve. Therefore, it is a positive indicator in the newly added distributed photovoltaic site selection comprehensive indicators. The voltage lower limit indicator indicates that the distributed photovoltaic connection node is conducive to improving the system voltage. Therefore, it is a positive indicator in the newly added distributed photovoltaic site selection comprehensive indicators. The voltage upper limit indicator is a negative indicator in the newly added distributed photovoltaic site selection comprehensive indicators. The entropy weight method is used to calculate the comprehensive indicator by combining the loss sensitivity factor indicator, the load matching index, the voltage lower limit indicator, and the voltage upper limit indicator.

[0066] S104: Determine a site selection result for installing distributed photovoltaics in the distribution network based on the comprehensive indicators.

[0067] In this embodiment, since steps S101-S103 calculate the comprehensive index value of each node in the distribution network, this embodiment selects the corresponding nodes in descending order of the comprehensive index values ​​as candidate installation locations for distributed photovoltaics.

[0068] In summary, in the site selection method provided in this embodiment, by comprehensively considering the load matching degree between photovoltaic output and node load, network loss sensitivity factor and voltage over-limit risk quantification index, combined with cluster analysis of historical data and entropy weight method weighted index, the site selection of distributed photovoltaic is achieved to reduce network loss and suppress voltage fluctuations.

[0069] In some embodiments, the method further includes: obtaining second historical operating data of the distribution network node corresponding to the site selection result based on the site selection result of the distribution network where distributed photovoltaics can be installed; determining the total capacity constraint, node capacity constraint, flow constraint, node voltage constraint and branch current constraint of the distributed photovoltaic installation based on the second historical operating data; establishing a capacity optimization configuration model for distributed photovoltaics based on the total capacity constraint, node capacity constraint, flow constraint, node voltage constraint and branch current constraint, with the lowest annual total cost of photovoltaic power generation as the objective function; solving the capacity optimization configuration model to obtain the capacity configuration of each site selection result of distributed photovoltaics.

[0070] Specifically, light intensity is usually described using Beta distribution:

[0071] Where: γ max represents the maximum light intensity, γ represents the light intensity, α and β represent the shape parameters of the Beta distribution, which are generally positive and can be determined according to the specific situation, Γ(x) represents the gamma function, and

[0072] The relationship between photovoltaic output and light intensity can be approximately expressed as Figure 2 The specific expression is as follows:

[0073] Where: P PV-rated is the rated capacity of the photovoltaic power plant.

[0074] The load size is usually described by a normal distribution:

[0075] Where: P L 、μ P , σ PThe distribution is the random quantity, expectation and standard deviation of active load; Q L is the random quantity of reactive load; is the power factor angle of the load.

[0076] The objective function comprehensively considers the annual investment cost of photovoltaic power generation, annual operation and maintenance costs, and annual network loss costs, with the lowest annual total cost as the objective function. The annual total cost is the sum of the annual investment cost, annual operation and maintenance costs, and annual network loss costs. The specific mathematical expression is as follows:

[0077] minf=C+C OM +C LOSS ;

[0078]

[0079] Where: C is the annual investment cost of distributed photovoltaics, C OM is the annual operation and maintenance cost of distributed photovoltaics, C LOSS is the annual network loss cost of the distribution network, r is the annualized rate of investment cost, C PV is the unit capacity investment and construction cost of the photovoltaic power station, S PV-rated,i is the capacity of the photovoltaic power station installed at node i, d is the discount rate, y PV is the economic life of the photovoltaic power station, C loss is the unit network loss cost, I ij is the current flowing through branch ij, R ij is the resistance of branch ij.

[0080] Constraints:

[0081] The total capacity constraint means that the total capacity of the photovoltaic power generation system cannot exceed the maximum operating value:

[0082] Where: R i is the photovoltaic capacity installed at the i-th node, R total The total capacity installed for the system.

[0083] The capacity constraint of each photovoltaic installation node means that the photovoltaic capacity of each node cannot exceed its maximum allowable capacity:

[0084] 0≤R i ≤R max,i , where: R max,i The maximum installed PV capacity for the i-th node.

[0085] Since the distflow reduced equation is efficient and fast in solving the optimization model, the flow constraint is set as:

[0086] Where, P j , Qj is the active and reactive power of the net load of node j, X ij is the reactance of the line between nodes i and j, P jl , Q jl is the active and reactive power of the line between nodes j and l.

[0087] Where, P load,j , Q load,j is the active and reactive power of the load at node j, P PV,j , Q PV,j is the actual active and reactive power output of the photovoltaic node j.

[0088] The node voltage constraint is U min ≤U i ≤U max , where: U i is the voltage at node i; U min and U max are the minimum and maximum voltages allowed for the node, respectively.

[0089] Branch current constraints include: |I ij |≤I ij,max Where: I ij is the current of branch ij; I ij,max is the maximum current allowed to flow through branch ij.

[0090] The optimization problem of distributed generation (DG) configuration is inherently continuous-time, and its complexity stems from the temporal fluctuations in DG output and load. This dynamic variation significantly increases the nonlinearity of the model and the difficulty of solving it, especially in long-term scenarios, which can easily lead to convergence failure or computational resource overload. While ensuring model accuracy, the following reasonable simplification strategies are adopted to improve solution efficiency:

[0091] Typical day characterization method: Based on historical data, the DTW-KMEANS clustering algorithm is used to extract seasonal characteristics of the solar radiation intensity and load curve throughout the year, generating four typical days in spring, summer, autumn, and winter respectively to characterize the temporal fluctuation pattern within the natural year, thereby transforming the continuous time scale optimization problem into discrete modeling.

[0092] Time section discretization: Each typical day is divided into 24 equally spaced time sections t = 1, 2, ..., 24, each section corresponds to a 1-hour period, and a discrete time series is constructed.

[0093] Deterministic scenario modeling: In each time section t, the peak equivalent method is used to simplify the time series fluctuation of distributed power output and load. The photovoltaic output takes the rated power value P corresponding to the maximum irradiance in that hour. PV,t =maxP PV (T)T∈[t-1,t], the node load is the peak load of the hour, that is, P L,t =maxP L (T)T∈[t-1,t].

[0094] The site selection method provided in this embodiment adopts the following method: Figure 3 The IEEE 33-bus system topology shown in the figure is used as an example. The load and sunlight intensity data for a local distribution network over a year are selected as simulation sample data, with a 15-minute time interval. A power flow calculation is performed on 32 of these loads to obtain the time-varying voltage data for each node over the course of a year.

[0095] The DTW-KMEANS clustering method is used to perform seasonal clustering on regional light radiation intensity and node historical load data, and the clustering results of typical days in four seasons are obtained as follows: Figure 5 As shown in the figure, the time series matching degree between distributed photovoltaic output and node load curve is calculated by using the Kendell correlation coefficient. The results are as follows: Figure 6 As shown in the figure, the node with the highest annual comprehensive evaluation index calculated by the entropy weight method based on the power flow calculation will be selected as the candidate node to determine the installation location of the distributed power supply. Figure 7 The comprehensive evaluation indicators of the nodes throughout the year are shown, and the eight nodes with the highest values ​​are selected as candidate nodes, which are 24, 6, 15, 21, 17, 30, 32, and 12 from high to low.

[0096] In the specific implementation case based on the IEEE 33-bus system, the capacity configuration results of distributed photovoltaic are as follows: Figure 8 As shown in the figure, distributed PV systems are deployed at nodes {6, 12, 15, 17, 21, 24, 30, and 32}, with a total capacity of 4 MW. Nodes 6 and 24 are configured with a capacity of 1 MW, and their comprehensive index scores are the highest. Connecting distributed PV systems to these two nodes can significantly reduce system losses and voltage over-limit risks. Node 21 is configured with a capacity of 0.78 MW. Located in a densely loaded area, the Kendall correlation coefficient between PV output and the local load curve reaches 0.67. The system's daily average network losses and node voltage over-limit risk values ​​after the distributed PV system is connected are shown in Table 1 below. Compared to the system without the distributed PV system, both daily average network losses and node voltage over-limit risk values ​​have decreased.

[0097] Daily average network loss (KW) Node voltage over-limit risk average Before connecting to distributed photovoltaics 773.47 0.521 After access to distributed photovoltaic 748.55 0.436

[0098] As can be seen, the site selection method for distributed photovoltaic power generation in distribution networks provided in this embodiment integrates photovoltaic-load timing matching, loss sensitivity factors, and voltage over-limit risk indicators to construct a comprehensive evaluation system for scientific site selection and capacity configuration. The DTW-KMEANS algorithm is used to perform cluster analysis on seasonal sunlight intensity and load curves, extracting typical daily characteristics. The Kendall correlation coefficient is then weighted to quantify the year-round matching, effectively capturing temporal and spatial fluctuations. The entropy weight method dynamically weights multiple objective indicators, breaking through the limitations of traditional single-indicator site selection. The Distflow linearized power flow constraint method is introduced into the optimization model, significantly reducing the solution complexity for large-scale distribution networks. Taking an IEEE 33-node system as an example, nodes {6, 12, 15, 17, 21, 24, 30, 32} were ultimately selected for distributed photovoltaic deployment, with a total capacity of 4 MW. The implementation case demonstrates that deploying distributed photovoltaic power generation according to this method reduces network losses by 3.21% and voltage over-limit risk by 16.3%, achieving a synergistic improvement in economy, reliability, and operational efficiency. From the experimental case, this method realizes the rationality of distributed photovoltaic site selection and distributed photovoltaic capacity configuration.

[0099] The present application also provides an electronic device. The electronic device includes a processor, a memory, a communication interface, and at least one communication bus for connecting the processor, the memory, and the communication interface. The memory includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (PROM), or a compact disc read-only memory (CD-ROM), and is used for storing relevant instructions and data.

[0100] The communication interface is used to receive and send data. The processor can be one or more CPUs. When the processor is a CPU, the CPU can be a single-core CPU or a multi-core CPU. The processor in the electronic device is used to read one or more programs stored in the memory and perform the following operations: obtaining the first historical operation data of the distribution network node; wherein the historical operation data includes the node load, radiation intensity and node voltage of each node; calculating the load matching index, loss sensitivity factor index and voltage over-limit index of each node based on the first historical operation data; weighting the load matching index, loss sensitivity factor index and voltage over-limit index to calculate the comprehensive index of each node in the distribution network; and determining the site selection result of the distribution network where distributed photovoltaic can be installed based on the comprehensive index.

[0101] It should be noted that the specific implementation of each operation can be as described above. Figure 1 The corresponding description of the method embodiment shown is that the electronic device can be used to execute a site selection method for distributed photovoltaics in a distribution network according to the above method embodiment of the present application, which will not be described in detail here.

[0102] The present application also provides a computer-readable storage medium, which is a memory device in a computer device for storing programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the site selection method for distributed photovoltaic power distribution networks in the above-mentioned embodiment. Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0103] The present application also provides a computer program product including program instructions. The computer program product may be software or a program product including program instructions that can be executed on a computing device or stored on any usable medium. When executed on at least one electronic device, the computer program product causes the at least one electronic device to execute a method for selecting a site for distributed photovoltaic power generation in a power distribution network.

[0104] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for site selection of distributed photovoltaic power distribution network, characterized in that: Methods include: Acquire first historical operation data of a distribution network node; wherein the historical operation data includes node load, radiation intensity, and node voltage of each node; Calculate the load matching index, loss sensitivity factor index and voltage over-limit index of each node according to the first historical operation data; The load matching index, loss sensitivity factor index and voltage over-limit index are weighted to calculate the comprehensive index of each node in the distribution network; Based on the comprehensive indicators, the site selection results for the distribution network where distributed photovoltaics can be installed are determined.

2. The method according to claim 1, characterized in that The load matching index is calculated based on the first historical operation data, specifically: Clustering algorithm is used to cluster the node load and radiation intensity of each node in each time series, and the solar radiation intensity and load curve of each node in each time series is obtained. The Kendall correlation coefficient is used to analyze the correlation between the solar radiation intensity and the load curve, and the matching degree of each node in each time series is obtained; The weight of each time series is preset, and the load matching index is calculated based on the weight and matching degree.

3. The method according to claim 1, characterized in that The calculation formula of the loss sensitivity factor index is: Among them, LSFs represents the loss sensitivity factor index value, P ab,L Indicates the active power loss of branch ab, Q ab,L Represents the reactive loss of branch ab, R ab Represents the resistance of branch ab.

4. The method according to claim 1, wherein The calculation formula of the voltage over-limit index is: Among them, Risk(U) represents the overvoltage risk value; Pr(U i ) represents the probability of voltage exceeding the limit at the i-th node; S ev (w i ) represents the severity of overvoltage at the i-th node.

5. The method according to claim 4, characterized in that The calculation method of the overvoltage severity at the i-th node is specifically as follows: Among them, U i represents the per-unit voltage value at the ith node, w i Represents the voltage over-limit loss value at the i-th node.

6. The method according to claim 1, characterized in that The method further comprises: According to the site selection result of the distribution network where distributed photovoltaic installation is possible, obtaining the second historical operation data of the distribution network node corresponding to the site selection result; Determining a total capacity constraint, a node capacity constraint, a power flow constraint, a node voltage constraint, and a branch current constraint of the distributed photovoltaic installation based on the second historical operating data; Based on total capacity constraints, node capacity constraints, power flow constraints, node voltage constraints, and branch current constraints, a distributed photovoltaic capacity optimization configuration model is established with the lowest annual total cost of photovoltaic power generation as the objective function. Solve the capacity optimization configuration model to obtain the capacity configuration of each distributed photovoltaic site selection result.

7. The method according to claim 6, characterized in that The annual total cost is the sum of annual investment cost, annual operation and maintenance cost and annual network loss cost.

8. An electronic device, characterized in that: including memory and processor; a memory for storing a computer program, wherein the computer program includes program instructions; A processor is used to execute the program instructions so that the electronic device performs the steps of the site selection method for distributed photovoltaic power generation in a distribution network as described in any one of claims 1 to 7.

9. A computer program product comprising program instructions, characterized in that When the program instructions are executed by an electronic device, the electronic device executes the steps of the method for site selection of distributed photovoltaic power distribution network according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a computer program, and when the computer program is executed by one or more processors, it implements the site selection method for distributed photovoltaic power generation in a power distribution network according to any one of claims 1 to 8.

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