Distributed photovoltaic configuration optimization method and device, equipment and storage medium

Abstract

The invention discloses a distributed photovoltaic configuration optimization method, device and equipment and a storage medium, which are applied to the technical field of photovoltaic configuration optimization, and the method comprises the steps: obtaining the data of a target power grid; obtaining a power flow model of the target power grid based on the topological structure data, the line impedance data and the load power data; performing power flow analysis processing on the power flow model to obtain a first system network loss value; partial derivative analysis is carried out on the plurality of load power data to obtain a sensitivity coefficient, and then a second system network loss value is obtained; obtaining total loss data based on the first system network loss value and the second system network loss value; processing the distributed photovoltaic capacity data by using a gradient analysis technology to obtain critical capacity permeability; the total loss data and the critical capacity permeability are processed, and a distributed photovoltaic configuration optimization scheme is generated based on the mapping relation obtained through processing. According to the method provided by the invention, the distributed photovoltaic configuration is optimized, so that the safe operation of the power grid is realized.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic configuration optimization technology, and in particular to a method, apparatus, equipment and storage medium for distributed photovoltaic configuration optimization. Background Technology

[0002] In the power system sector, distributed photovoltaics has seen rapid development due to its cleanliness, flexibility, and proximity to the load side.

[0003] In existing technologies, the planning of distributed photovoltaic (PV) grid connection in distribution networks generally follows the classic principle of end-point grid connection and local consumption, which means that connecting PV power to the end of the feeder is most conducive to reducing power loss of the line.

[0004] However, with the continuous expansion of the scale of distributed photovoltaic grid connection, during periods of high photovoltaic output and low load, large-scale end-grid connection is very likely to cause power backflow, which in turn generates power flow backflow. This reverse power flow will not only significantly raise the voltage level at the end of the feeder and frequently cause safety accidents where the voltage exceeds the operating limit, but also seriously restrict the grid's ability to accept distributed photovoltaic.

[0005] Therefore, optimizing the configuration of distributed photovoltaic systems has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] This invention provides a method, apparatus, device, and storage medium for optimizing distributed photovoltaic (PV) configurations to solve the technical problem of power flow backflow in the prior art. By optimizing the configuration of distributed PV, the safe operation of the power grid can be achieved.

[0007] To address the aforementioned technical problems, this invention provides a method for optimizing distributed photovoltaic configuration, the method comprising: Acquire the topology data, line impedance data, distributed photovoltaic capacity data, and load power data of several nodes in the target power grid; Based on the topology data, the line impedance data, and several load power data, the power flow model of the target power grid is obtained; The power flow model is subjected to power flow analysis to obtain the first system network loss value of the target power grid; Partial derivative analysis is performed on several load power data to obtain sensitivity coefficients of several nodes in the target power grid; the several load power data and the corresponding sensitivity coefficients are weighted to obtain the second system network loss value of the target power grid. Based on the first system network loss value and the second system network loss value, the total loss data of the target power grid is obtained; The distributed photovoltaic capacity data is processed using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid; The total loss data and the critical capacity penetration rate are processed, and a distributed photovoltaic configuration optimization scheme for the target power grid is generated based on the mapping relationship obtained from the processing.

[0008] As one preferred embodiment, obtaining the power flow model of the target power grid based on the topology data, the line impedance data, and several load power data includes: The topology data is parsed to obtain the node-branch association network; The line impedance data is converted to obtain the branch admittance parameters; Construct a node admittance matrix based on the node-branch association network and the branch admittance parameters; The node admittance matrix and several load power data are subjected to load allocation processing to obtain the power flow model of the target power grid.

[0009] As one preferred embodiment, the step of performing power flow analysis on the power flow model to obtain the first system network loss value of the target power grid includes: The power flow model is analyzed and processed using numerical iteration techniques to obtain full-node voltage phasor data; Power flow analysis is performed on the full node voltage phasor data to obtain the first system network loss value of the target power grid.

[0010] As one preferred embodiment, obtaining the total loss data of the target power grid based on the first system network loss value and the second system network loss value includes: The network loss value of the first system is normalized to obtain the static network loss index; The network loss value of the second system is standardized to obtain the dynamic sensitivity index; The total loss data is obtained by processing the static network loss index and the dynamic sensitivity index using a weighted fusion technique. As one preferred embodiment, the step of processing the distributed photovoltaic capacity data using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid includes: The distributed photovoltaic capacity data is continuously and incrementally scanned to obtain the capacity safety boundary; The critical capacity penetration rate of the target power grid is obtained by processing the capacity safety boundary using gradient analysis technology.

[0011] As one preferred embodiment, the process of processing the total loss data and the critical capacity penetration rate, and generating a distributed photovoltaic configuration optimization scheme for the target power grid based on the processed mapping relationship, includes: The total loss data and the critical capacity permeability are processed using multi-objective optimization techniques to obtain the mapping relationship; The optimal compromise solution is selected from the mapping relationship to obtain the optimized distributed photovoltaic configuration scheme.

[0012] As one preferred embodiment, after obtaining the distributed photovoltaic configuration optimization scheme, the distributed photovoltaic configuration optimization method further includes: Power flow processing is performed on the distributed photovoltaic configuration optimization scheme to obtain the power flow return data of the target power grid; The power flow feedback data is evaluated, and the distributed photovoltaic configuration optimization scheme is assessed based on the evaluation results.

[0013] The present invention also provides a distributed photovoltaic configuration optimization device, comprising: The acquisition module is used to acquire the topology data, line impedance data, distributed photovoltaic capacity data, and load power data of several nodes in the target power grid. A construction module is used to obtain the power flow model of the target power grid based on the topology data, the line impedance data, and several load power data. The power flow analysis module is used to perform power flow analysis on the power flow model to obtain the first system network loss value of the target power grid; The processing module is used to perform partial derivative analysis on several load power data to obtain the sensitivity coefficients of several nodes in the target power grid; and to perform weighted processing on several load power data and corresponding sensitivity coefficients to obtain the second system network loss value of the target power grid. The loss processing module is used to obtain the total loss data of the target power grid based on the first system network loss value and the second system network loss value; The penetration module is used to process the distributed photovoltaic capacity data using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid. The generation module is used to process the total loss data and the critical capacity penetration rate, and generate a distributed photovoltaic configuration optimization scheme for the target power grid based on the processed mapping relationship.

[0014] The present invention also provides a distributed photovoltaic configuration optimization device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the distributed photovoltaic configuration optimization method as described above.

[0015] The present invention further provides a computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the distributed photovoltaic configuration optimization method as described above.

[0016] Compared with the prior art, the beneficial effects of the present invention are at least one of the following: This invention acquires topology data, line impedance data, distributed photovoltaic (PV) capacity data, and load power data of several nodes in a target power grid. Based on the topology data, line impedance data, and load power data, a power flow model of the target power grid is obtained. Power flow analysis is performed on the power flow model to obtain a first system network loss value for the target power grid. Partial derivative analysis is performed on the load power data to obtain sensitivity coefficients of several nodes in the target power grid. Weighted processing is applied to the load power data and the corresponding sensitivity coefficients to obtain a second system network loss value for the target power grid. Based on the first and second system network loss values, the total loss data of the target power grid is obtained. Gradient analysis is used to process the distributed PV capacity data to obtain the critical capacity penetration rate of the target power grid. The total loss data and the critical capacity penetration rate are processed, and a distributed PV configuration optimization scheme for the target power grid is generated based on the resulting mapping relationship.

[0017] Compared with existing technologies, this invention optimizes the configuration of distributed photovoltaic (PV) systems by constructing a power grid flow model and incorporating load sensitivity analysis and critical capacity penetration rate calculation. First, it comprehensively evaluates the total loss characteristics of the power grid under static and load fluctuation conditions. Then, it uses gradient analysis to accurately calculate the safe penetration rate boundary that ensures voltage does not exceed the limit. Finally, based on the mapping relationship between loss characteristics and the safe boundary, it generates an optimized scheme, which prioritizes the placement of PV systems at key nodes that most effectively reduce grid losses without triggering reverse power flow voltage exceedances. This achieves the dual goals of reducing system losses and improving PV integration capacity while ensuring grid safety. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a distributed photovoltaic configuration optimization method in one embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a distributed photovoltaic configuration optimization device in one embodiment of the present invention; Figure 3 This is a schematic diagram of the distributed photovoltaic configuration optimization structure in one embodiment of the present invention; Figure label: The module consists of: 11. Acquisition module; 12. Construction module; 13. Power flow analysis module; 14. Processing module; 15. Loss processing module; 16. Penetration module; 17. Generation module; 21. Processor; 22. Memory. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] One embodiment of the present invention provides a method for optimizing distributed photovoltaic configuration. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shown is a flowchart illustrating a distributed photovoltaic configuration optimization method according to one embodiment of the present invention. The method includes: S1: Obtain the topology data, line impedance data, distributed photovoltaic capacity data, and load power data of several nodes in the target power grid; S2: Based on the topology data, the line impedance data, and several load power data, the power flow model of the target power grid is obtained; S3: Perform power flow analysis on the power flow model to obtain the first system network loss value of the target power grid; S4: Perform partial derivative analysis on the load power data to obtain the sensitivity coefficients of several nodes in the target power grid; perform weighted processing on the load power data and the corresponding sensitivity coefficients to obtain the second system network loss value of the target power grid; S5: Based on the first system network loss value and the second system network loss value, obtain the total loss data of the target power grid; S6: The distributed photovoltaic capacity data is processed using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid; S7: Process the total loss data and the critical capacity penetration rate, and generate a distributed photovoltaic configuration optimization scheme for the target power grid based on the mapping relationship obtained from the processing.

[0023] Specifically, topology data mainly includes substations, lines, load nodes, photovoltaic access points in the target power grid, and the connection relationships between these points; line impedance data mainly includes the resistance and reactance parameters of the lines; distributed photovoltaic capacity data is the current or planned photovoltaic installed capacity; node load power data refers to the electricity demand of each node, which directly affects network losses and photovoltaic absorption.

[0024] Next, based on the topology data, the line impedance data, and several load power data, a power flow model of the target power grid is obtained, specifically including: parsing the topology data to obtain a node-branch association network; transforming the line impedance data to obtain branch admittance parameters; constructing a node admittance matrix based on the node-branch association network and the branch admittance parameters; and performing load allocation processing on the node admittance matrix and several load power data to obtain the power flow model of the target power grid.

[0025] First, extract the basic information of all nodes and branches from the topology data, clarify the type of each node and the type of each branch, then determine the nodes connected at both ends of each branch, and present this connection relationship in a structured way, such as by using a list to clarify the correspondence between branches and nodes, to ensure that all connection relationships are accurately sorted out.

[0026] In the above analysis process, the structural information of the power grid is transformed into a clear relationship between nodes and branches.

[0027] Impedance data such as resistance and reactance for each line are extracted, and these impedance parameters are converted into corresponding conductance and susceptance through calculation. For lines with parallel capacitors, the resulting parallel susceptance is also processed. Ultimately, each branch has a corresponding admittance parameter, and these parameters correspond one-to-one with the previously determined branches. This converts commonly used impedance parameters in engineering into admittance parameters that are easier to use in power flow calculations, adapting to the subsequent logic for constructing the nodal admittance matrix.

[0028] The electrical connections between nodes in the entire power grid are quantified using matrix form. Specifically, the dimension of the matrix is ​​determined based on the number of nodes in the power grid, and the order of the matrix is ​​consistent with the total number of nodes. The diagonal elements of the matrix are calculated as self-admittance, where the self-admittance of each node is the sum of the admittances of all branches connected to that node. The off-diagonal elements are calculated as mutual admittance, where the mutual admittance between two nodes is the negative of the admittance of the branches connecting them. If two nodes are not directly connected, the mutual admittance is 0. After completing the matrix filling, its accuracy is verified.

[0029] By organizing the load power data of each node, clarifying the active and reactive power of each load node, and then allocating these load powers to the corresponding nodes, the injected power of each node is determined, which is the power source power minus the load power. Combined with the node admittance matrix, the power balance relationship of each node is established, forming a complete mathematical model that includes unknown quantities such as node voltage, which is the power flow model. This model can reflect the actual operating state of the power grid.

[0030] In step S3, power flow analysis is performed on the power flow model to obtain the first system network loss value of the target power grid. Specifically, this includes: analyzing the power flow model using numerical iteration techniques to obtain full node voltage phasor data; and performing power flow analysis on the full node voltage phasor data to obtain the first system network loss value of the target power grid.

[0031] The core logic of numerical iteration is to gradually approximate the true value through a cycle of guessing, calculating, and correcting. Commonly used methods include the Newton-Raphson method. Specifically, in operation, an initial voltage guess value is first set for all nodes, for example, assuming that the load node voltage is the rated voltage and the power supply node voltage remains constant. Then, the initial voltage is substituted into the power balance relationship of the power flow model to calculate the power deviation of each node, which is the difference between the injected power that the node should have in the model and the actual calculated power. Then, according to the magnitude of the power deviation, the voltage values ​​of each node are adjusted according to a specific correction rule to obtain new voltage data. The process of substituting and calculating deviations and correcting voltages is repeated until the power deviation of all nodes is less than the industry-specified accuracy standard. The voltage data obtained at this point is the full-node voltage phasor data, which includes the voltage amplitude and phase angle of each node.

[0032] Then, based on the full node voltage phasor data and existing line admittance parameters, the power flow of each branch is calculated; then, the power loss of the branch is calculated based on the power flow data of the branch, because power will be lost due to line resistance and reactance when it is transmitted in the branch. The magnitude of the loss is related to the power difference between the two ends of the branch, that is, the power input at one end of the branch minus the power output at the other end. The difference is the loss of the branch. Finally, the power losses of all branches in the target grid are added together, and the sum is the first system network loss value, which reflects the basic loss level of the grid when transmitting power under the current load and grid structure.

[0033] Then, partial derivative analysis is performed on several load power data to obtain the sensitivity coefficients of several nodes in the target power grid; the several load power data and the corresponding sensitivity coefficients are weighted and processed to obtain the second system network loss value of the target power grid.

[0034] In this process, while keeping the power grid topology, line impedance, and other conditions unchanged, only a small change is applied to the load power of a single node, and the change in the total network loss of the system is observed. By calculating the ratio between these two changes, the sensitivity coefficient of that node is obtained.

[0035] By analyzing all load nodes one by one in this manner, a sensitivity coefficient is obtained for each node. The larger the coefficient, the more significant the impact of load power changes on network loss. By quantifying the impact of load power changes at each node on the total network loss, it is possible to identify which nodes' load fluctuations will more significantly alter network loss, providing a basis for subsequent accurate calculation of dynamic network loss.

[0036] Multiply the actual load power data of each node by the sensitivity coefficient of that node to obtain the weighted contribution value of each node's load to network loss. Then, add up the weighted contribution values ​​of all nodes, and the sum is the second system network loss value.

[0037] Based on the network loss values ​​of the first system and the second system, the total loss data of the target power grid is obtained, specifically including: normalizing the network loss value of the first system to obtain a static network loss index; standardizing the network loss value of the second system to obtain a dynamic sensitivity index; and processing the static network loss index and the dynamic sensitivity index using weighted fusion technology to obtain the total loss data.

[0038] The first system network loss value is an absolute value based on static power flow calculation. It is affected by the scale of the power grid. The absolute values ​​of different power grids or different time periods of the same power grid lack direct comparability. The purpose of normalization is to eliminate the differences in scale and dimensions and transform it into a standardized proportional indicator, laying the foundation for subsequent integration with dynamic indicators.

[0039] Specifically, key operating parameters of the target power grid are selected as reference values, such as the total load, total generating capacity, or rated power of the power grid. The first system network loss value is divided by this reference benchmark to obtain a dimensionless ratio value, which is the static network loss index.

[0040] The second system network loss value is a dynamically corrected value calculated based on load sensitivity, and its numerical range may differ significantly from the static network loss index. The purpose of standardization is to map the dynamically corrected network loss value to a range consistent with the static network loss index, avoiding the over-amplification or weakening of a certain index during subsequent fusion due to differences in numerical values, and ensuring the effectiveness of the weights of the two during the fusion process.

[0041] First, collect the second system network loss data from the historical operation of the target power grid, and determine the maximum and minimum values ​​in these data to construct the numerical range of dynamic network loss. Then, subtract the historical minimum value from the current second system network loss value, and divide by the difference between the historical maximum and the historical minimum value. Through this calculation, the current second system network loss value is mapped to a fixed interval of 0 to 1, and the result is the dynamic sensitivity index.

[0042] Static network loss indicators reflect the static loss level of the power grid foundation, while dynamic sensitivity indicators reflect the dynamic loss impact caused by load fluctuations. The purpose of weighted fusion is to assign different importance weights to the two indicators according to the actual operation needs of the power grid, and to combine the information of the two to obtain total loss data that can reflect both the foundation loss and the dynamic changes, so that the final loss result is more comprehensive.

[0043] Specifically, the weight allocation rules are determined based on the power grid's operating scenarios and needs. For example, during periods of low load fluctuation, static losses have a greater impact on total losses, so the static network loss index is given a higher weight and the dynamic sensitivity index a lower weight. During periods of high load fluctuation, the weight of the dynamic sensitivity index is increased and the weight of the static network loss index is decreased. Then, the static network loss index is multiplied by its corresponding weight, and the dynamic sensitivity index is multiplied by its corresponding weight. The two products are then added together to obtain the total loss data.

[0044] The distributed photovoltaic capacity data is processed using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid. This process includes: performing continuous incremental scanning processing on the distributed photovoltaic capacity data to obtain a capacity safety boundary; and processing the capacity safety boundary using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid.

[0045] Determine the initial capacity and increment for the scan. The initial capacity is usually started from 0 or the current photovoltaic capacity already connected to the grid. The increment is chosen as a small fixed value to ensure that critical safety thresholds are not skipped.

[0046] Then, the capacity of distributed photovoltaic power is gradually increased according to the set range. Each time the capacity is increased, the capacity data is substituted into the existing power grid flow model to simulate the current operating state of the power grid.

[0047] At the same time, the core safety indicators of the power grid are monitored in real time, including whether node voltage deviation, line power exceeding limits, frequency fluctuations, and short-circuit current meet national standards.

[0048] The process of continuously increasing capacity, simulating operation, and monitoring safety indicators continues until, after a certain capacity increase, at least one safety indicator of the power grid exceeds the standard range for the first time, at which point the scanning stops.

[0049] The last safe capacity value before exceeding the standard, together with the first capacity value exceeding the standard, is determined as the capacity safety boundary, which is the upper limit of photovoltaic capacity for safe grid operation within this range.

[0050] Within the capacity safety boundary range determined in the first step, the capacity increment is reduced, and a more refined secondary scan is performed. Each time a small increment of capacity is added, the current photovoltaic capacity value and the corresponding grid safety index value are recorded.

[0051] Then, calculate the ratio of the change in safety index to the change in photovoltaic capacity in two adjacent scans, i.e., the gradient. Observe the trend of the gradient. Outside the safety boundary range, the gradient is usually relatively gentle; but when approaching the critical point, the gradient will suddenly increase. The capacity point where the gradient changes abruptly is the maximum safe photovoltaic capacity for safe grid operation.

[0052] Finally, dividing this maximum safe capacity by the total load capacity or total installed capacity of the target power grid yields the critical capacity penetration rate, which represents the upper limit of the safe ratio that photovoltaic grid connection cannot exceed.

[0053] The total loss data and the critical capacity penetration rate are processed, and a distributed photovoltaic configuration optimization scheme for the target power grid is generated based on the processed mapping relationship. This includes: processing the total loss data and the critical capacity penetration rate using multi-objective optimization technology to obtain the mapping relationship; and performing optimal compromise solution selection processing on the mapping relationship to obtain the distributed photovoltaic configuration optimization scheme.

[0054] With the objectives of minimizing total losses and ensuring that the total photovoltaic (PV) capacity does not exceed the critical capacity penetration rate, a suitable multi-objective optimization algorithm, such as a non-dominated sorting genetic algorithm or a particle swarm optimization algorithm, is used. The total losses, critical capacity penetration rate, and PV configuration variables are input into the algorithm. Through multiple iterations, the algorithm filters out all non-dominated solutions—meaning no solution can simultaneously achieve lower total losses and a PV capacity closer to the critical value. Each non-dominated solution corresponds to a set of PV configuration parameters, total losses, and PV capacity. Organizing these correspondences into structured relational data yields the mapping relationship between total losses, PV configuration, and the safety boundary.

[0055] Based on the obtained mapping relationship, and combined with the actual decision-making criteria of the power grid, the non-dominated solutions in the mapping relationship are ranked. For example, if the power load in the area where the power grid is located fluctuates greatly and the requirements for power supply stability are high, the decision-making criteria will be more inclined to prioritize safety, that is, the photovoltaic capacity and critical penetration rate will maintain a certain safety margin; if the area is more concerned about cost reduction and efficiency improvement and the power grid has strong regulation capabilities, the decision-making criteria will be more inclined to prioritize economy.

[0056] The non-dominated solutions in the mapping relationship are sorted according to the decision criteria, and a compromise solution is selected. The selected compromise solution is then verified to ensure that it meets other actual constraints of the power grid, such as the voltage carrying capacity of the access node, the power transmission limit of the line, and the spatiotemporal matching degree between photovoltaic output and load.

[0057] Finally, the photovoltaic configuration parameters corresponding to the verified compromise solution are compiled into a specific scheme, including the total photovoltaic installed capacity, the optimal access node list, and the photovoltaic capacity allocation ratio of each access node, forming a distributed photovoltaic configuration optimization scheme that can directly guide the implementation of the project.

[0058] After obtaining the distributed photovoltaic configuration optimization scheme, the distributed photovoltaic configuration optimization method further includes: performing power flow processing on the distributed photovoltaic configuration optimization scheme to obtain power flow feedback data of the target power grid; judging the power flow feedback data; and evaluating the distributed photovoltaic configuration optimization scheme based on the judgment result.

[0059] Extract key parameters from the distributed photovoltaic configuration optimization scheme, including the determined total photovoltaic installed capacity, the photovoltaic capacity allocation of each access node, and the specific branch location of the photovoltaic access.

[0060] Next, these parameters are updated into the power flow model of the target power grid. The updated power flow model is then calculated using the previous numerical iteration technique to solve for data such as the voltage of all nodes in the power grid after the implementation of the scheme, the direction and magnitude of power flow in each branch, the total network loss of the system, and the power balance of each node. These data, which reflect the actual power flow state of the power grid and are obtained through the simulation of the scheme, are the power flow feedback data.

[0061] The power flow feedback data is compared with the power grid standards one by one. If all safety indicators meet the standards and the economic indicators meet the expectations, the judgment result is qualified. The evaluation conclusion is that the optimization scheme is feasible and can be directly used to guide the implementation of the project.

[0062] If one or more indicators fail to meet the standards, the result will be deemed unqualified. The evaluation conclusion must clearly point out the problem and propose preliminary adjustment directions to provide a basis for iterative revision of the optimization plan.

[0063] Another embodiment of the present invention provides a distributed photovoltaic configuration optimization device. For details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The diagram shown illustrates the structure of a distributed photovoltaic configuration optimization device according to one embodiment of the present invention. The device includes: The acquisition module 11 is used to acquire the topology data, line impedance data, distributed photovoltaic capacity data, and load power data of several nodes in the target power grid. The construction module 12 is used to obtain the power flow model of the target power grid based on the topology data, the line impedance data and several load power data. The power flow analysis module 13 is used to perform power flow analysis on the power flow model to obtain the first system network loss value of the target power grid; Processing module 14 is used to perform partial derivative analysis on several load power data to obtain sensitivity coefficients of several nodes in the target power grid; and to perform weighted processing on several load power data and corresponding sensitivity coefficients to obtain the second system network loss value of the target power grid. The loss processing module 15 is used to obtain the total loss data of the target power grid based on the first system network loss value and the second system network loss value; The penetration module 16 is used to process the distributed photovoltaic capacity data using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid; The generation module 17 is used to process the total loss data and the critical capacity penetration rate, and generate a distributed photovoltaic configuration optimization scheme for the target power grid based on the processed mapping relationship.

[0064] See Figure 3This is a schematic diagram of the structure of a distributed photovoltaic configuration optimization device provided in an embodiment of the present invention. The distributed photovoltaic configuration optimization device provided in this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, it implements the steps as described in the above-described distributed photovoltaic configuration optimization method embodiment, for example... Figure 1 The steps S1 to S7 described above; or, when the processor 21 executes the computer program, it implements the functions of each module in the above-described device embodiments, such as the acquisition module 11.

[0065] For example, the computer program can be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the distributed photovoltaic configuration optimization device. For example, the computer program can be divided into an acquisition module 11, a construction module 12, a power flow analysis module 13, etc., with the specific functions of each module as follows: The acquisition module 11 is used to acquire the topology data, line impedance data, distributed photovoltaic capacity data, and load power data of several nodes in the target power grid. The construction module 12 is used to obtain the power flow model of the target power grid based on the topology data, the line impedance data and several load power data. The power flow analysis module 13 is used to perform power flow analysis on the power flow model to obtain the first system network loss value of the target power grid; Processing module 14 is used to perform partial derivative analysis on several load power data to obtain sensitivity coefficients of several nodes in the target power grid; and to perform weighted processing on several load power data and corresponding sensitivity coefficients to obtain the second system network loss value of the target power grid. The loss processing module 15 is used to obtain the total loss data of the target power grid based on the first system network loss value and the second system network loss value; The penetration module 16 is used to process the distributed photovoltaic capacity data using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid; The generation module 17 is used to process the total loss data and the critical capacity penetration rate, and generate a distributed photovoltaic configuration optimization scheme for the target power grid based on the processed mapping relationship.

[0066] The distributed photovoltaic (PV) configuration optimization device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of a distributed PV configuration optimization device and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the distributed PV configuration optimization device may also include input / output devices, network access devices, buses, etc.

[0067] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the distributed photovoltaic configuration optimization equipment, connecting various parts of the equipment via various interfaces and lines.

[0068] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the distributed photovoltaic configuration optimization device by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0069] If the modules integrated into the distributed photovoltaic configuration optimization equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0071] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform steps in the distributed photovoltaic configuration optimization method of the above embodiments, for example... Figure 1 Steps S1 to S7 as described above.

[0072] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for optimizing distributed photovoltaic configuration, characterized in that, The method comprises the following steps: obtaining topology data, line impedance data, distributed photovoltaic capacity data and load power data of a plurality of nodes in the target power grid; based on the topology data, the line impedance data and a plurality of load power data, obtaining a power flow model of the target power grid; performing power flow analysis on the power flow model to obtain a first system loss value of the target power grid; performing partial derivative analysis on a plurality of load power data to obtain sensitivity coefficients of a plurality of nodes in the target power grid; and performing weighted processing on a plurality of load power data and corresponding sensitivity coefficients to obtain a second system loss value of the target power grid; based on the first system loss value and the second system loss value, obtaining total loss data of the target power grid; processing the distributed photovoltaic capacity data using gradient analysis technology to obtain a critical capacity penetration rate of the target power grid; processing the total loss data and the critical capacity penetration rate, and generating a distributed photovoltaic configuration optimization scheme for the target power grid based on the mapping relationship obtained by processing.

2. The method of claim 1, wherein, The power flow model of the target power grid is obtained based on the topology data, the line impedance data and a plurality of load power data, which comprises the following steps: performing analysis processing on the topology data to obtain a node-branch associated network; performing conversion processing on the line impedance data to obtain branch admittance parameters; constructing a node admittance matrix based on the node-branch associated network and the branch admittance parameters; performing load distribution processing on the node admittance matrix and a plurality of load power data to obtain the power flow model of the target power grid.

3. The method of claim 1, wherein, The first system loss value of the target power grid is obtained by performing power flow analysis on the power flow model, which comprises the following steps: performing analysis processing on the power flow model using numerical iteration technology to obtain full-node voltage phasor data; performing power flow analysis on the full-node voltage phasor data to obtain the first system loss value of the target power grid.

4. The method of claim 1, wherein, The total loss data of the target power grid is obtained based on the first system loss value and the second system loss value, which comprises the following steps: performing normalization processing on the first system loss value to obtain a static loss index; performing standardization processing on the second system loss value to obtain a dynamic sensitivity index; processing the static loss index and the dynamic sensitivity index using weighted fusion technology to obtain the total loss data.

5. The method of claim 1, wherein, The critical capacity penetration rate of the target power grid is obtained by processing the distributed photovoltaic capacity data using gradient analysis technology, which comprises the following steps: performing continuous incremental scanning processing on the distributed photovoltaic capacity data to obtain a capacity safety boundary; processing the capacity safety boundary using gradient analysis technology to obtain the critical capacity penetration rate of the target power grid.

6. The method of claim 1, wherein, The distributed photovoltaic configuration optimization scheme for the target power grid is generated based on the mapping relationship obtained by processing the total loss data and the critical capacity penetration rate, which comprises the following steps: processing the total loss data and the critical capacity penetration rate using multi-objective optimization technology to obtain the mapping relationship; The mapping relationship is subjected to optimal compromise solution selection processing to obtain the distributed photovoltaic configuration optimization scheme.

7. The distributed photovoltaic configuration optimization method of claim 1, wherein, After the distributed photovoltaic configuration optimization scheme is obtained, the distributed photovoltaic configuration optimization method further comprises: The distributed photovoltaic configuration optimization scheme is subjected to power flow processing to obtain power flow return data of the target power grid. The power flow return data is subjected to judgment, and the distributed photovoltaic configuration optimization scheme is evaluated based on the obtained judgment result.

8. A distributed photovoltaic configuration optimization apparatus, characterized by, Comprise: An acquisition module is configured to acquire topological structure data, line impedance data, distributed photovoltaic capacity data, and load power data of a plurality of nodes in a target power grid; A construction module is configured to obtain a power flow model of the target power grid based on the topological structure data, the line impedance data, and the load power data of the plurality of nodes; A power flow analysis module is configured to perform power flow analysis processing on the power flow model to obtain a first system loss value of the target power grid; A processing module is configured to perform partial derivative analysis on the load power data of the plurality of nodes to obtain sensitivity coefficients of the plurality of nodes in the target power grid; and perform weighting processing on the load power data of the plurality of nodes and the corresponding sensitivity coefficients to obtain a second system loss value of the target power grid; A loss processing module is configured to obtain total loss data of the target power grid based on the first system loss value and the second system loss value; A penetration module is configured to perform processing on the distributed photovoltaic capacity data by using a gradient analysis technique to obtain a critical capacity penetration rate of the target power grid; A generation module is configured to perform processing on the total loss data and the critical capacity penetration rate, and generate a distributed photovoltaic configuration optimization scheme of the target power grid based on a mapping relationship obtained by the processing.

9. A distributed photovoltaic configuration optimization device, comprising: The computer readable storage medium stores a computer program, wherein when a device in which the computer readable storage medium is located executes the computer program, the distributed photovoltaic configuration optimization method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein when a device in which the computer readable storage medium is located executes the computer program, the distributed photovoltaic configuration optimization method according to any one of claims 1 to 7 is implemented.

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