Distribution network distributed photovoltaic parallel optimization method, system, equipment and medium

Abstract

The invention belongs to the technical field of power systems, and discloses a distributed photovoltaic parallel optimization method, system and device for a power distribution network and a medium. The method comprises the following steps: acquiring the number of distributed photovoltaic units of a distributed photovoltaic system in a power distribution network and real-time operation data of each distributed photovoltaic unit; constructing an optimization task of each distributed photovoltaic unit according to the number of the distributed photovoltaic units and the real-time operation data, configuring a distributed computing node for each optimization task, and generating an optimization strategy; performing a simulation test on the distributed photovoltaic system, and evaluating the stability of the distributed photovoltaic system; under the condition of the unstable state, analyzing the distributed photovoltaic system, and determining fault factors causing the unstable state; adjusting an optimization strategy of each distributed photovoltaic unit according to the fault factors; and the distributed photovoltaic system is optimized based on the adjusted optimization strategy. According to the invention, equipment wear and fault risks are reduced, and stable operation of a power grid is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, device and medium for parallel optimization of distributed photovoltaic power generation in a power distribution network. Background Art

[0002] Distributed photovoltaic power generation refers to the installation of multiple photovoltaic power generation units distributed in the power distribution network. These photovoltaic units convert solar energy into electrical energy through solar panels and directly connect to the distribution network to supply power to local users and the power grid. The advantages of distributed photovoltaic systems include reducing transmission losses, improving energy efficiency, reducing grid pressure, and promoting the application of renewable energy. They are usually installed on the roofs or idle land of residential buildings, commercial facilities and public buildings, and achieve efficient power management and scheduling through smart grid technology.

[0003] Parallel optimization refers to the use of parallel computing technology to execute multiple optimization tasks or algorithms simultaneously to solve complex problems more efficiently. By breaking down large-scale computing tasks into smaller subtasks and assigning them to multiple processors or computing nodes for parallel processing, computing time and resource consumption can be significantly reduced, and the speed and effect of overall optimization can be improved. This method is often used in fields such as big data analysis, artificial intelligence, engineering design, and power system management to achieve faster and more accurate optimization results.

[0004] When optimizing distributed photovoltaics in existing distribution networks in parallel, the distributed computing architecture is used to decompose large-scale computing tasks and process them in parallel. Since each node works independently and runs simultaneously, the total computing time is greatly reduced. However, when multiple nodes perform optimization calculations at the same time, resonance occurs, that is, the optimization decisions of each node interfere with each other, causing the photovoltaic system to enter an unstable oscillation state. At the same time, if the photovoltaic system continues to be in an unstable state, it will cause frequent fluctuations in the power grid, increase the wear and failure risks of equipment, and may even trigger the protection mechanism to disconnect some photovoltaic systems, further exacerbating the instability of the power grid. Summary of the invention

[0005] The embodiments of the present invention provide a method, system, device and medium for parallel optimization of distributed photovoltaic power generation in a power distribution network to solve the above-mentioned technical problems in the prior art.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a method for parallel optimization of distributed photovoltaics in a power distribution network is provided.

[0008] In one embodiment, the distributed photovoltaic parallel optimization method for a power distribution network includes:

[0009] Obtain the number of distributed photovoltaic units of the distributed photovoltaic system in the distribution network and the real-time operation data of each distributed photovoltaic unit;

[0010] According to the number of distributed photovoltaic units and the real-time operation data, an optimization task for each distributed photovoltaic unit is constructed, and a distributed computing node is configured for each optimization task to generate an optimization strategy for each distributed photovoltaic unit;

[0011] Summarize the various optimization strategies, and based on the summarized optimization strategies, conduct simulation tests on the distributed photovoltaic system, and evaluate the stability of the distributed photovoltaic system based on the simulation test results;

[0012] When the evaluation result shows that the distributed photovoltaic system is in an unstable state, the distributed photovoltaic system is analyzed to determine the fault factors that cause the distributed photovoltaic system to be in an unstable state;

[0013] According to the fault factors, the optimization strategy of each distributed photovoltaic unit is adjusted; and based on the adjusted optimization strategy, the distributed photovoltaic system is optimized.

[0014] In one embodiment, when acquiring the real-time operating data of each distributed photovoltaic unit, the voltage sensor, current sensor, power sensor, temperature sensor, irradiance sensor and smart meter pre-deployed on each distributed photovoltaic unit are used to acquire the real-time operating data of each distributed photovoltaic unit.

[0015] In one embodiment, configuring a distributed computing node for each optimization task to generate an optimization strategy for each distributed photovoltaic unit includes:

[0016] A distributed computing node is configured for each of the optimization tasks, and based on the distributed computing nodes and according to the corresponding optimization tasks, the distributed photovoltaic system is locally optimized through a genetic algorithm to generate an optimization strategy for each distributed photovoltaic unit.

[0017] In one embodiment, based on the simulation test results, evaluating the stability of the distributed photovoltaic system includes:

[0018] Based on the simulation test results, extract the voltage fluctuation amplitude data of each distributed computing node, and generate a node voltage fluctuation index according to the voltage fluctuation amplitude data;

[0019] Based on the simulation test results, extract the load distribution change data of each distributed computing node, and generate a node load distribution anomaly index according to the load distribution change data;

[0020] Calculating a stability coefficient of a distributed photovoltaic system according to the node voltage fluctuation index and the node load distribution abnormality index;

[0021] The stability of the distributed photovoltaic system is determined according to the stability coefficient of the distributed photovoltaic system.

[0022] In one embodiment, generating a node voltage fluctuation index according to the voltage fluctuation amplitude data includes:

[0023] The voltage fluctuation amplitude data extracted from each distributed computing node is preprocessed (the preprocessing includes: calculating the mean and standard deviation of all the load data, and using the mean and standard deviation of the load data for standardization to ensure that the mean of each column of load data in the data matrix is ​​0 and the variance is 1), and the preprocessed voltage data is subjected to wavelet decomposition to obtain signal features at different scales;

[0024] Perform wavelet transform on the obtained signal features at different scales to obtain the wavelet coefficients at each position at each scale;

[0025] All wavelet coefficients at each scale are counted, and the wavelet energy at each scale is calculated; and based on the wavelet energy at each scale, the node voltage fluctuation index is calculated.

[0026] In one embodiment, the calculation formula of the wavelet decomposition is:

[0027]

[0028] Where X(t) is the original voltage fluctuation amplitude data in period t; φ J,k (t) and ψ J,k (t) are scaling function and wavelet function respectively; c J,k and d J,k are the scale coefficient and wavelet coefficient; J is the number of decomposition levels; k is the decomposition position; j is the decomposition scale.

[0029] In one embodiment, the calculation formula of the wavelet energy is:

[0030]

[0031] Where Ej is the wavelet energy of each scale; d J,k is the wavelet coefficient.

[0032] In one embodiment, the calculation formula of the node voltage fluctuation index is:

[0033]

[0034] Where Vs is the node voltage fluctuation index; Ej is the wavelet energy of each scale.

[0035] In one embodiment, generating a node load distribution anomaly index according to the load distribution change data includes:

[0036] Determining load data of distributed computing nodes at different time points according to the load distribution change data;

[0037] Performing standardization processing on the load data, and forming a data matrix according to the load data after the standardization processing;

[0038] Calculating the covariance matrix corresponding to the data matrix, and performing eigenvalue analysis on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0039] Sorting the eigenvalues, selecting eigenvalues ​​within a predetermined range as main components, and selecting corresponding eigenvectors based on the main components;

[0040] The filtered eigenvectors are projected onto the data matrix to obtain the principal component scores; and the node load distribution anomaly index is calculated based on the principal component scores.

[0041] The standardization process includes: calculating the mean and standard deviation of all the load data, and using the mean and standard deviation of the load data for standardization to ensure that the mean of each column of the load data in the data matrix is ​​0 and the variance is 1.

[0042] In one embodiment, the calculation formula of the covariance matrix is:

[0043]

[0044] Where Σ is the covariance matrix of the data matrix; x i is the i-th sample, i.e., the node load vector; μ is the mean vector; N is the total number of samples; and T is the matrix transpose.

[0045] In one embodiment, the calculation formula of the main component score is:

[0046] Y=XV

[0047] Where Y is the main component score; X is the data matrix; V is the selected feature vector.

[0048] In one embodiment, the calculation formula of the node load distribution anomaly index is:

[0049]

[0050] Where DM is the node load distribution anomaly index, i.e., the node load vector; μ is the mean vector; and T is the matrix transpose.

[0051] In one embodiment, the calculation formula of the stability coefficient of the distributed photovoltaic system is:

[0052]

[0053] Where CM is the stability coefficient of the distributed photovoltaic system; DM is the node load distribution abnormality index; Vs is the node voltage fluctuation index; a1 and a2 decibels are the proportional coefficients of the node voltage fluctuation index and the node load distribution abnormality index, and a2>a1>0.

[0054] In one embodiment, according to the stability coefficient of the distributed photovoltaic system, determining the stability of the distributed photovoltaic system includes:

[0055] Comparing the stability coefficient of the distributed photovoltaic system with a preset reference threshold value of the stability coefficient of the distributed photovoltaic system;

[0056] When the comparison result shows that the stability coefficient of the distributed photovoltaic system is greater than or equal to the stability coefficient reference threshold, it is determined that the distributed photovoltaic system is in a stable state;

[0057] When the comparison result is that the stability coefficient of the distributed photovoltaic system is less than the stability coefficient reference threshold, it is determined that the distributed photovoltaic system is in an unstable state.

[0058] In one embodiment, according to the fault factor, adjusting the optimization strategy of each distributed photovoltaic unit includes:

[0059] According to the fault factors, the optimization algorithm parameters, communication protocol, data synchronization algorithm and node coordination algorithm of each distributed photovoltaic unit optimization strategy are adjusted.

[0060] In one embodiment, the distributed photovoltaic parallel optimization method for the distribution network further includes:

[0061] After optimizing the distributed photovoltaic system based on the adjusted optimization strategy, a stability coefficient of the distributed photovoltaic system within a predetermined time period is calculated and a data set is formed;

[0062] Calculating a mean and a standard deviation of a data set, and calculating a fault risk threshold value based on the mean and the standard deviation of the data set;

[0063] Compare and calculate the stability coefficient in the data set with the fault risk threshold, and obtain the abnormal time proportion value that the stability coefficient of the distributed photovoltaic system is less than the fault risk threshold within a predetermined time;

[0064] Compare the abnormal time ratio value with a reference threshold of a preset abnormal time ratio value, and when the comparison result is that the abnormal time ratio value is greater than or equal to the reference threshold, determine that the risk of failure of the distributed photovoltaic system is high and generate a warning signal; otherwise, determine that the risk of failure of the distributed photovoltaic system is low and do not generate a warning signal.

[0065] In one embodiment, the calculation formula for the failure risk threshold is:

[0066] TF = μ + kσ

[0067] In the formula, TF is the failure risk threshold; μ is the data set mean; σ is the data set standard deviation; k is an adjustment coefficient.

[0068] In one embodiment, the calculation formula for the abnormal time ratio value is:

[0069]

[0070] In the formula, PF is the normal time ratio value; x is the number of the stability coefficient in the data set, CM x is the stability coefficient in the data set, M is the total number of stability coefficients in the data set, L is an indicator function, when CM x < TF, L = 1; otherwise L = 0; TF is the failure risk threshold.

[0071] According to the second aspect of the embodiments of the present invention, a distribution network distributed photovoltaic parallel optimization system is provided.

[0072] In one embodiment, the distribution network distributed photovoltaic parallel optimization system includes:

[0073] A data acquisition module, configured to acquire the number of distributed photovoltaic units in the distributed photovoltaic system in the distribution network and the real-time operation data of each distributed photovoltaic unit;

[0074] A strategy generation module, configured to construct an optimization task for each distributed photovoltaic unit according to the number of distributed photovoltaic units and the real-time operation data, and configure a distributed computing node for each optimization task to generate an optimization strategy for each distributed photovoltaic unit;

[0075] A stability evaluation module, configured to summarize each optimization strategy, and based on the summarized optimization strategy, perform a simulation test on the distributed photovoltaic system, and evaluate the stability of the distributed photovoltaic system based on the simulation test result;

[0076] A fault analysis module is used to analyze the distributed photovoltaic system and determine the fault factors that cause the distributed photovoltaic system to be in an unstable state when the evaluation result shows that the distributed photovoltaic system is in an unstable state;

[0077] The system optimization module is used to adjust the optimization strategy of each distributed photovoltaic unit according to the fault factor; and optimize the distributed photovoltaic system based on the adjusted optimization strategy.

[0078] In one embodiment, when acquiring the real-time operating data of each distributed photovoltaic unit, the data acquisition module uses the voltage sensor, current sensor, power sensor, temperature sensor, irradiance sensor and smart meter pre-deployed on each distributed photovoltaic unit to acquire the real-time operating data of each distributed photovoltaic unit.

[0079] In one embodiment, when the strategy generation module configures a distributed computing node for each of the optimization tasks and generates an optimization strategy for each distributed photovoltaic unit, the distributed computing node is configured for each of the optimization tasks, and based on the distributed computing nodes and according to the corresponding optimization tasks, the distributed photovoltaic system is locally optimized through a genetic algorithm to generate an optimization strategy for each distributed photovoltaic unit.

[0080] In one embodiment, when the stability assessment module assesses the stability of the distributed photovoltaic system based on the simulation test results, the voltage fluctuation amplitude data of each distributed computing node is extracted based on the simulation test results, and a node voltage fluctuation index is generated based on the voltage fluctuation amplitude data; the load distribution change data of each distributed computing node is extracted based on the simulation test results, and a node load distribution anomaly index is generated based on the load distribution change data; the stability coefficient of the distributed photovoltaic system is calculated based on the node voltage fluctuation index and the node load distribution anomaly index; and the stability of the distributed photovoltaic system is determined based on the stability coefficient of the distributed photovoltaic system.

[0081] In one embodiment, when the stability assessment module generates a node voltage fluctuation index based on the voltage fluctuation amplitude data, it preprocesses the voltage fluctuation amplitude data extracted from each distributed computing node, and performs wavelet decomposition on the preprocessed voltage data to obtain signal features at different scales; performs wavelet transform on the obtained signal features at different scales to obtain wavelet coefficients at each position at each scale; performs statistics on all wavelet coefficients at each scale to calculate the wavelet energy at each scale; and calculates the node voltage fluctuation index based on the wavelet energy at each scale.

[0082] In one embodiment, the calculation formula of the wavelet decomposition is:

[0083]

[0084] Where X(t) is the original voltage fluctuation amplitude data in period t; φ J,k (t) and ψ J,k (t) are scaling function and wavelet function respectively; c J,k and d J,k are the scale coefficient and wavelet coefficient; J is the number of decomposition levels; k is the decomposition position; j is the decomposition scale.

[0085] In one embodiment, the calculation formula of the wavelet energy is:

[0086]

[0087] Where Ej is the wavelet energy of each scale; d J,k is the wavelet coefficient.

[0088] In one embodiment, the calculation formula of the node voltage fluctuation index is:

[0089]

[0090] Where Vs is the node voltage fluctuation index; Ej is the wavelet energy of each scale.

[0091] In one embodiment, when the stability assessment module generates the node load distribution anomaly index according to the load distribution change data, the module determines the load data of the distributed computing nodes at different time points according to the load distribution change data; standardizes the load data, and forms a data matrix according to the load data after the standardization; calculates the covariance matrix corresponding to the data matrix, and performs eigenvalue analysis on the covariance matrix to obtain eigenvalues ​​and eigenvectors; sorts the eigenvalues, and selects the eigenvalues ​​within a predetermined range as the main components, and selects the corresponding eigenvectors based on the main components; projects the selected eigenvectors onto the data matrix to obtain the main component scores; and calculates the node load distribution anomaly index according to the main component scores.

[0092] The standardization process includes: calculating the mean and standard deviation of all the load data, and using the mean and standard deviation of the load data for standardization to ensure that the mean of each column of the load data in the data matrix is ​​0 and the variance is 1.

[0093] In one embodiment, the calculation formula of the covariance matrix is:

[0094]

[0095] Where Σ is the covariance matrix of the data matrix; x iis the i-th sample, i.e., the node load vector; μ is the mean vector; N is the total number of samples; and T is the matrix transpose.

[0096] In one embodiment, the calculation formula of the main component score is:

[0097] Y=XV

[0098] Where Y is the main component score; X is the data matrix; V is the selected feature vector.

[0099] In one embodiment, the calculation formula of the node load distribution anomaly index is:

[0100]

[0101] Where DM is the node load distribution abnormality index; x i is the i-th sample, i.e., the node load vector; μ is the mean vector; T is the matrix transpose.

[0102] In one embodiment, the calculation formula of the stability coefficient of the distributed photovoltaic system is:

[0103]

[0104] Where CM is the stability coefficient of the distributed photovoltaic system; DM is the node load distribution abnormality index; Vs is the node voltage fluctuation index; a1 and a2 decibels are the proportional coefficients of the node voltage fluctuation index and the node load distribution abnormality index, and a2>a1>0.

[0105] In one embodiment, when the stability assessment module determines the stability of the distributed photovoltaic system based on the stability coefficient of the distributed photovoltaic system, the stability coefficient of the distributed photovoltaic system is compared with a preset reference threshold of the stability coefficient of the distributed photovoltaic system; when the comparison result is that the stability coefficient of the distributed photovoltaic system is greater than or equal to the reference threshold of the stability coefficient, the distributed photovoltaic system is judged to be in a stable state; when the comparison result is that the stability coefficient of the distributed photovoltaic system is less than the reference threshold of the stability coefficient, the distributed photovoltaic system is judged to be in an unstable state.

[0106] In one embodiment, when the system optimization module adjusts the optimization strategy of each distributed photovoltaic unit according to the fault factor, it adjusts the optimization algorithm parameters, communication protocol, data synchronization algorithm and node coordination algorithm of the optimization strategy of each distributed photovoltaic unit according to the fault factor.

[0107] In one embodiment, the distribution network distributed photovoltaic parallel optimization system further includes:

[0108] A continuous monitoring module is used to calculate the stability coefficient of a distributed photovoltaic system within a predetermined time period after optimizing the distributed photovoltaic system based on an adjusted optimization strategy, and form a data set; calculate the mean and standard deviation of the data set, and calculate a fault risk threshold according to the mean and standard deviation of the data set; compare and calculate the stability coefficient in the data set with the fault risk threshold to obtain the abnormal time proportion value that the stability coefficient of the distributed photovoltaic system within a predetermined time is less than the fault risk threshold; compare the abnormal time proportion value with a reference threshold of the preset abnormal time proportion value, and determine that the risk of the distributed photovoltaic system having a fault is high and generate a warning signal when the comparison result is that the abnormal time proportion value is greater than or equal to the reference threshold; otherwise, determine that the risk of the distributed photovoltaic system having a fault is low and do not generate a warning signal.

[0109] In one embodiment, the calculation formula of the fault risk threshold is:

[0110] TF = μ + kσ

[0111] In the formula, TF is the fault risk threshold; μ is the mean of the data set; σ is the standard deviation of the data set; k is an adjustment coefficient.

[0112] In one embodiment, the calculation formula of the abnormal time proportion value is:

[0113]

[0114] In the formula, PF is the normal time proportion value; x is the number of the stability coefficient in the data set, CM x is the stability coefficient in the data set, M is the total number of stability coefficients in the data set, L is an indicator function, when CM x < TF, L = 1; otherwise L = 0; TF is the fault risk threshold.

[0115] According to the third aspect of the embodiments of the present invention, a computer device is provided.

[0116] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0117] According to the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided.

[0118] In one embodiment, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0119] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0120] 1. The present invention ensures the efficient operation of each node and generates high-quality optimization decisions through real-time data collection, building optimization tasks for each distributed photovoltaic unit, configuring distributed computing nodes, and applying genetic algorithms. Through simulation testing and comprehensive analysis and evaluation of system stability, the system operation status is clarified, and optimization decisions are adjusted in time to ensure that the system can recover quickly in an unstable state, thereby reducing equipment wear and failure risks and maintaining the smooth operation of the power grid.

[0121] 2. The present invention continuously monitors and analyzes system operation data, continuously improves the optimization strategy according to the actual operation situation, realizes long-term stable monitoring and optimization of the distributed photovoltaic system, and effectively reduces the risk of system failure. The whole method not only improves the operation performance and reliability of the photovoltaic system, but also ensures the coordination and consistency of the optimization decision of each node. Through flexible adjustment mechanism and efficient algorithm, the reliable operation and efficient management of the photovoltaic system in a complex environment are realized.

[0122] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0124] Figure 1 is a flow chart of a method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to an exemplary embodiment;

[0125] Figure 2 is a structural block diagram of a distributed photovoltaic parallel optimization system for a distribution network according to an exemplary embodiment;

[0126] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0127] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0128] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0129] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0130] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0131] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0132] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0133] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0134] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0135] Figure 1 An embodiment of the distributed photovoltaic parallel optimization method for a power distribution network of the present invention is shown.

[0136] In this optional embodiment, the distribution network distributed photovoltaic parallel optimization method includes:

[0137] Step S101, obtaining the number of distributed photovoltaic units of a distributed photovoltaic system in a distribution network and real-time operation data of each distributed photovoltaic unit;

[0138] Step S103, constructing an optimization task for each distributed photovoltaic unit according to the number of distributed photovoltaic units and the real-time operation data, configuring a distributed computing node for each optimization task, and generating an optimization strategy for each distributed photovoltaic unit;

[0139] Step S105, summarizing the various optimization strategies, and based on the summarized optimization strategies, performing simulation tests on the distributed photovoltaic system, and evaluating the stability of the distributed photovoltaic system based on the simulation test results;

[0140] Step S107, when the evaluation result shows that the distributed photovoltaic system is in an unstable state, analyzing the distributed photovoltaic system to determine the fault factor causing the distributed photovoltaic system to be in an unstable state;

[0141] Step S109, adjusting the optimization strategy of each distributed photovoltaic unit according to the fault factor; and optimizing the distributed photovoltaic system based on the adjusted optimization strategy.

[0142] Figure 2 An embodiment of the distributed photovoltaic parallel optimization system for power distribution network of the present invention is shown.

[0143] In this optional embodiment, the distribution network distributed photovoltaic parallel optimization system includes:

[0144] The data acquisition module 201 is used to obtain the number of distributed photovoltaic units of the distributed photovoltaic system in the distribution network and the real-time operation data of each distributed photovoltaic unit;

[0145] A strategy generation module 203 is used to construct an optimization task for each distributed photovoltaic unit according to the number of distributed photovoltaic units and the real-time operation data, and configure a distributed computing node for each optimization task to generate an optimization strategy for each distributed photovoltaic unit;

[0146] The stability evaluation module 205 is used to summarize the various optimization strategies, and based on the summarized optimization strategies, perform simulation tests on the distributed photovoltaic system, and evaluate the stability of the distributed photovoltaic system based on the simulation test results;

[0147] The fault analysis module 207 is used to analyze the distributed photovoltaic system and determine the fault factors that cause the distributed photovoltaic system to be in an unstable state when the evaluation result shows that the distributed photovoltaic system is in an unstable state;

[0148] The system optimization module 209 is used to adjust the optimization strategy of each distributed photovoltaic unit according to the fault factor; and optimize the distributed photovoltaic system based on the adjusted optimization strategy.

[0149] In the above optional embodiment, when obtaining the real-time operating data of each distributed photovoltaic unit, the voltage sensor, current sensor, power sensor, temperature sensor, irradiance sensor and smart meter pre-deployed on each distributed photovoltaic unit are used to obtain the real-time operating data of each distributed photovoltaic unit.

[0150] Specifically, real-time data from each distributed photovoltaic unit is obtained through sensors and smart meters, and the optimization task is decomposed into several subtasks according to the number of distributed photovoltaic units. Each subtask contains the real-time data and computing requirements of some distributed photovoltaic units.

[0151] Design and deploy a network of sensors and smart meters to monitor and collect data in real time in distributed PV systems, including: Determine the distribution and number of distributed PV units. Install voltage, current, power, temperature, and irradiance sensors on each distributed PV unit. Deploy smart meters to record and transmit data. Ensure that sensors and smart meters are connected to a central data collection system (such as a SCADA system) to achieve real-time data transmission. Collect real-time data through sensors and smart meters, and pre-process the data to ensure data accuracy and consistency. Set the time interval for data collection (such as every minute, every second). Delete outliers and noise data, and fill in missing data. Convert data into a unified format for subsequent processing and analysis. Store the pre-processed data in a central database to ensure data accessibility and security.

[0152] According to the number and geographical distribution of distributed photovoltaic units, the optimization task is decomposed into several subtasks, each of which contains the real-time data and computing requirements of some photovoltaic units. The number of subtasks is determined based on the computing resources and the number of photovoltaic units. Distributed photovoltaic units are assigned to different subtasks to balance the computing load of each subtask as much as possible. Corresponding real-time data and specific computing requirements (such as power optimization, load balancing, voltage regulation, etc.) are assigned to each subtask.

[0153] In the above optional embodiment, when configuring a distributed computing node for each of the optimization tasks and generating an optimization strategy for each distributed photovoltaic unit, a distributed computing node is configured for each of the optimization tasks, and based on the distributed computing nodes and according to the corresponding optimization tasks, a genetic algorithm is used to perform local optimization on the distributed photovoltaic system to generate an optimization strategy for each distributed photovoltaic unit.

[0154] Specifically, configure distributed computing nodes to ensure that each computing node runs simultaneously and independently processes the assigned subtasks, and locally optimize the PV system through genetic algorithms to generate optimization decisions. Select appropriate servers or computers as computing nodes, considering their CPU, memory, and storage capabilities. Install the operating system, distributed computing framework (such as Hadoop, Spark), and required optimization algorithm software on each node. Ensure that all computing nodes are connected through a high-speed network to ensure the stability and efficiency of data transmission. Initialize the computing nodes and perform connection tests to ensure smooth communication between nodes. Start the computing nodes and load the necessary initialization parameters and configuration files. Use network testing tools (such as ping, traceroute) to test the connection between nodes to ensure that the network latency and bandwidth meet the requirements. Decompose large-scale optimization tasks into several subtasks. Divide the overall optimization task into multiple subtasks based on the number of PV units and their geographical locations. Each subtask contains the data and computing requirements of some PV units. Assign subtasks to each computing node. Assign specific subtasks to each computing node to ensure load balancing. Send the corresponding real-time data and computing requirements to each computing node.

[0155] The PV system is locally optimized by genetic algorithm, and the optimization decision is generated. Specifically, the genetic algorithm is initialized on each computing node. A set of initial solutions is generated for each subtask, namely the initial population. The genetic algorithm parameters are set, including population size, crossover rate, mutation rate and number of iterations. The fitness of each individual solution is calculated as the criterion for judging its quality. According to the optimization goal of the PV system (such as maximizing power output and minimizing voltage fluctuation), the fitness function is defined. The fitness value of each individual solution in the initial population is calculated. Excellent individuals are selected to enter the next generation according to the fitness value. Individuals are selected from the population using methods such as roulette selection and tournament selection. The selected individuals have high fitness and ensure the transmission of excellent genes. New individuals are generated through crossover operation. According to the set crossover rate, a pair of parent individuals are selected. Some genes of the parent individuals are exchanged using methods such as single-point crossover and two-point crossover to generate new offspring individuals. New genes are introduced through mutation operation to increase population diversity. According to the set mutation rate, the genes of individuals are randomly selected for mutation. New individuals are generated using simple mutation methods, such as randomly changing the value of a certain site. Repeat fitness calculation, selection, crossover and mutation operations until the termination condition is met. Run multiple iteration cycles to gradually optimize the individual solutions in the population. When the preset number of iterations is reached or the fitness of the population is no longer significantly improved, the algorithm is terminated. Generate local optimization decisions at each computing node. Select the individual with the highest fitness from the final population as the local optimization result. Send the local optimization decision back to the central server or coordination node. Aggregate the optimization results of each computing node to the central server. Collect the local optimization decisions sent back by each node. Integrate these decisions to form a global optimization plan,

[0156] In the above optional embodiments, when summarizing the various optimization strategies, and based on the summarized optimization strategies, performing simulation tests on the distributed photovoltaic system, and based on the simulation test results, evaluating the stability of the distributed photovoltaic system, the various optimization strategies are summarized, and based on the summarized optimization strategies, performing simulation tests on the distributed photovoltaic system, based on the simulation test results, extracting the voltage fluctuation amplitude data of each distributed computing node, and generating a node voltage fluctuation index based on the voltage fluctuation amplitude data; based on the simulation test results, extracting the load distribution change data of each distributed computing node, and generating a node load distribution anomaly index based on the load distribution change data; calculating the stability coefficient of the distributed photovoltaic system based on the node voltage fluctuation index and the node load distribution anomaly index; and determining the stability of the distributed photovoltaic system based on the stability coefficient of the distributed photovoltaic system.

[0157] Specifically, the optimization decision data generated by each computing node is collected. After completing the local optimization calculation, each computing node packages the optimization decision (such as voltage set point, output power, reactive power compensation and other parameters) and transmits it to the central server.

[0158] Use reliable data transmission protocols to ensure that data is not lost or tampered with during transmission. Integrate the optimization decisions of all nodes on the central server. Integrate the optimization decisions received from each node and unify the data format to facilitate subsequent processing. Resolve possible decision conflicts and duplications to ensure data consistency and integrity.

[0159] Use simulation software to build detailed distribution network and photovoltaic system simulation models. Use tools such as MATLAB / Simulink, PSCAD, etc. to create the topology of the power system, including power lines, transformers, loads, and photovoltaic power generation units. Input actual grid parameters and photovoltaic system parameters to ensure that the model can accurately reflect the behavior of the actual system.

[0160] Configure the simulation model parameters, including the optimization decisions for each node. Input the optimization decision parameters (such as voltage set point, output power, etc.) for each computing node into the simulation model. Check and verify the correctness of the input parameters to ensure that the simulation model can accurately reflect the optimization decisions.

[0161] Start the simulation model to simulate the operation of the distribution network and photovoltaic system under different conditions. Set the simulation time step and total simulation time to ensure that the system response can be fully observed. Record the system status data at each time point, such as voltage, current, and load distribution.

[0162] Record the voltage fluctuation and load changes of each node during the simulation process. Use the recording function of the simulation software to save the voltage and load data of each node at each time during the simulation process. Extract the voltage fluctuation amplitude and load distribution data of each node from the simulation test results.

[0163] When generating the node voltage fluctuation index based on the voltage fluctuation amplitude data, the voltage fluctuation amplitude data extracted from each distributed computing node is preprocessed, and the preprocessed voltage data is subjected to wavelet decomposition to obtain signal characteristics at different scales; the obtained signal characteristics at different scales are subjected to wavelet transform to obtain wavelet coefficients at each position at each scale; all wavelet coefficients at each scale are counted to calculate the wavelet energy at each scale; and the node voltage fluctuation index is calculated based on the wavelet energy at each scale.

[0164] Specifically, in practical applications, before performing wavelet decomposition on the voltage fluctuation amplitude data, the voltage fluctuation amplitude data is preprocessed to ensure that the length of the signal is a power of 2 to facilitate the calculation of the wavelet transform (if not, zero padding can be performed).

[0165] The pre-processed voltage fluctuation amplitude data is decomposed by wavelet to extract signal features at different scales (frequencies). When applying, it is necessary to select a suitable wavelet basis function (such as Daubechies wavelet, symbol db) and determine the number of decomposition layers J. The more layers, the higher the frequency resolution of the decomposition. The expression of wavelet decomposition is:

[0166]

[0167] Where X(t) is the original voltage fluctuation amplitude data for the period t; J,k (t) and ψ J,k (t) are scaling function and wavelet function respectively, c J,k and d J,k are the scale coefficient and wavelet coefficient, J is the number of decomposition levels, k is the decomposition position, and j is the decomposition scale.

[0168] The wavelet coefficients at each scale are extracted. These coefficients contain the information of the signal at different frequencies. The signal is transformed by wavelet to obtain the wavelet coefficients d at each scale j and position k. J,k ; Count all wavelet coefficients of each layer j and calculate the wavelet energy Ej of the jth layer. The calculation formula is:

[0169]

[0170] Where Ej is the wavelet energy of each scale; d J,k is the wavelet coefficient, k is the decomposition position, and j is the decomposition scale.

[0171] The voltage fluctuation index is calculated based on the wavelet energy at each scale. The specific calculation expression is:

[0172]

[0173] Where Vs is the node voltage fluctuation index; Ej is the wavelet energy of each scale; J is the number of decomposition levels, and j is the decomposition scale.

[0174] The larger the node voltage fluctuation index, the worse the stability of the distributed photovoltaic system. A higher voltage fluctuation index indicates that the voltage at the node varies greatly at different time points, which usually reflects the instability and frequent fluctuations of the voltage. In the distribution network, a large voltage fluctuation will affect the power quality, resulting in reduced operating efficiency or even damage to electrical equipment.

[0175] In distributed photovoltaic systems, high voltage fluctuation index may be caused by the large fluctuation of photovoltaic power output power due to factors such as weather changes and cloud cover. Such fluctuations will bring challenges to the voltage regulation and load balancing of the power grid, and increase the uncertainty and complexity of system operation. In addition, frequent voltage fluctuations may also cause inverters and other power electronic equipment to frequently switch states, increasing equipment wear and failure rate.

[0176] In order to ensure the stability of distributed photovoltaic systems, measures need to be taken to reduce the voltage fluctuation index. This includes optimizing the control strategy of distributed photovoltaic systems, enhancing the regulation capability of energy storage systems, and improving the dispatching and management of power grids to smooth the output fluctuations of photovoltaic power generation and ensure voltage stability and reliable operation of the power system.

[0177] The node load distribution anomaly index is generated according to the load distribution changes of each node extracted from the simulation test results. The method for obtaining the node load distribution anomaly index is as follows:

[0178] When generating a node load distribution anomaly index according to the load distribution change data, determine the load data of the distributed computing nodes at different time points according to the load distribution change data; perform standardization on the load data (the standardization includes: calculating the mean and standard deviation of all the load data, and using the mean and standard deviation of the load data for standardization to ensure that the mean of each column of load data in the data matrix is ​​0 and the variance is 1), and form a data matrix according to the load data after standardization; calculate the covariance matrix corresponding to the data matrix, and perform eigenvalue analysis on the covariance matrix to obtain eigenvalues ​​and eigenvectors; sort the eigenvalues, and select the eigenvalues ​​within a predetermined range as the main components, and select the corresponding eigenvectors based on the main components; project the selected eigenvectors onto the data matrix to obtain the main component scores; and calculate the node load distribution anomaly index according to the main component scores.

[0179] The calculation formula of the covariance matrix is:

[0180]

[0181] Where Σ is the covariance matrix of the data matrix; x i is the i-th sample, i.e., the node load vector, μ is the mean vector, N is the total number of samples, and T is the matrix transpose.

[0182] The calculation formula for the principal component score is:

[0183] Y=XV

[0184] Where Y is the main component score; X is the data matrix; V is the selected feature vector.

[0185] The calculation formula of the node load distribution abnormality index is:

[0186]

[0187] Where DM is the node load distribution abnormality index; x i is the i-th sample, i.e., the node load vector, μ is the mean vector, and T is the matrix transpose.

[0188] The larger the node load distribution anomaly index is, the worse the stability of the distributed photovoltaic system is. This is because a higher load distribution anomaly index reflects the uneven distribution and drastic changes of the load among the nodes, which may cause some nodes to be overloaded while other nodes are underloaded. This imbalance will increase the risk and instability of system operation. Abnormal load distribution may cause voltage fluctuations, frequency deviations and power quality degradation, which in turn affect the normal operation of the equipment, shorten its service life, and may cause serious consequences such as power outages.

[0189] In addition, the high load distribution anomaly index also means that the distributed photovoltaic system cannot effectively cope with the dynamic adjustment of load changes and distribution, indicating that there are problems with the current control strategy and scheduling scheme, and it is impossible to fully utilize the potential of photovoltaic power generation to maintain the stability of the power grid. In order to improve the stability of the system, it is necessary to optimize the load distribution strategy, improve the system's responsiveness and regulation capabilities, and ensure the reasonable distribution and stable operation of the load among the nodes.

[0190] The node voltage fluctuation index and the node load distribution anomaly index are normalized, and the stability coefficient of the distributed photovoltaic system is calculated based on the normalized node voltage fluctuation index and the node load distribution anomaly index.

[0191] For example, the present invention can use the following formula to calculate the stability coefficient of the distributed photovoltaic system, and the calculation expression is:

[0192]

[0193] Where CM is the stability coefficient of the distributed photovoltaic system; DM is the node load distribution abnormality index; Vs is the node voltage fluctuation index; a1 and a2 decibels are the proportional coefficients of the node voltage fluctuation index and the node load distribution abnormality index, and a2>a1>0.

[0194] In the above optional embodiment, when determining the stability of the distributed photovoltaic system according to the stability coefficient of the distributed photovoltaic system, the stability coefficient of the distributed photovoltaic system is compared with a preset reference threshold value of the stability coefficient of the distributed photovoltaic system; when the comparison result is that the stability coefficient of the distributed photovoltaic system is greater than or equal to the reference threshold value of the stability coefficient, it means that when the nodes of the distributed photovoltaic system are simultaneously performing optimization calculations, the optimization decisions of each node have low mutual interference, and it is judged that the distributed photovoltaic system is in a stable state, and a photovoltaic system stability signal is generated at this time; when the comparison result is that the stability coefficient of the distributed photovoltaic system is less than the reference threshold value of the stability coefficient, it means that when the nodes of the photovoltaic system are simultaneously performing optimization calculations, the optimization decisions of each node have high mutual interference, and it is judged that the distributed photovoltaic system is in an unstable state, and a photovoltaic system instability signal is generated at this time.

[0195] In the above optional embodiment, when adjusting the optimization strategy of each distributed photovoltaic unit according to the fault factor, the optimization algorithm parameters, communication protocol, data synchronization algorithm and node coordination algorithm of the optimization strategy of each distributed photovoltaic unit are adjusted according to the fault factor.

[0196] Specifically, collect and analyze system operation data to determine the specific manifestations of unstable states (such as voltage fluctuations, frequency instability, etc.) and identify the main causes of unstable states, such as unbalanced load distribution and interference in optimization decisions between nodes.

[0197] Adjust the optimization algorithm parameters of each node to improve the robustness of the system. Re-evaluate the optimization algorithm parameters currently used (such as the crossover rate and mutation rate of the genetic algorithm). Adjust the parameter settings according to the actual situation to improve the convergence speed and stability of the algorithm. For example, the crossover rate α→α+Δα; the mutation rate β→β+Δβ; according to the system requirements and actual operation, evaluate the applicability of other optimization algorithms (such as particle swarm optimization, simulated annealing, etc.). If necessary, replace or combine multiple algorithms to improve the optimization effect.

[0198] Optimize the communication mechanism between nodes to ensure the timeliness and accuracy of data transmission. Check and improve the network connection between nodes to reduce communication delays and data loss. Use efficient communication protocols (such as MQTT, HTTP / 2) to ensure the stability of data transmission.

[0199] Ensure data synchronization of each node to avoid decision conflicts caused by data asynchrony. Implement data synchronization mechanism to regularly synchronize the operation data of each node and optimize decision making. Use distributed databases (such as Apache Cassandra, HBase) or real-time data synchronization tools (such as Apache Kafka) to ensure data consistency.

[0200] Strengthen the coordination mechanism between nodes to ensure that the optimization decisions of each node are coordinated. Implement a hierarchical control architecture and set up a master control node to coordinate the optimization decisions of each sub-node. Use collaborative optimization algorithms, such as the Distributed Coordination Algorithm (DCA), to coordinate the optimization process of each node.

[0201] Implement the adjusted optimization algorithm parameters and control strategy into the system. Configure the new algorithm parameters to each node and restart the optimization algorithm. Update the configuration of the communication and coordination mechanism to ensure the smooth operation of the new mechanism.

[0202] Monitor the operating status of the adjusted system and provide real-time feedback and improvements. Use monitoring tools (such as Prometheus and Grafana) to monitor the operating status of the system in real time and record key performance indicators (KPIs). Make further adjustments based on the monitoring results to optimize system performance.

[0203] In addition, in actual application, after adjusting the optimization strategy, the operating data of the photovoltaic system within a fixed period of time is monitored, and the stability performance of the system is evaluated by calculating and analyzing these data. Then, the optimization strategy is further improved based on the analysis results to reduce the risk of failure of the photovoltaic system and improve its overall operating stability.

[0204] Specifically, after optimizing the distributed photovoltaic system based on the adjusted optimization strategy, the stability coefficient of the distributed photovoltaic system within a predetermined time period is calculated and a data set is formed; the mean and standard deviation of the data set are calculated, and the fault risk threshold is calculated based on the mean and standard deviation of the data set; the stability coefficient in the data set is compared with the fault risk threshold to obtain an abnormal time proportion value in which the stability coefficient of the distributed photovoltaic system within the predetermined time is less than the fault risk threshold; the abnormal time proportion value is compared with a preset reference threshold of the abnormal time proportion value, and when the comparison result is that the abnormal time proportion value is greater than or equal to the reference threshold, it is determined that the risk of failure of the distributed photovoltaic system is high and a warning signal is generated; otherwise, it is determined that the risk of failure of the distributed photovoltaic system is low and no warning signal is generated.

[0205] The calculation formula of the fault risk threshold is:

[0206] TF=μ+kσ

[0207] Where TF is the fault risk threshold; μ is the mean of the data set; σ is the standard deviation of the data set; and k is the adjustment coefficient.

[0208] The calculation formula of the abnormal time ratio value is:

[0209]

[0210] In the formula, PF is the constant time proportion value; x is the serial number of the stability coefficient in the data set, CM x is the stability coefficient in the data set, M is the total number of stability coefficients in the data set, L is the indicator function, when CM x < TF, L = 1; otherwise L = 0; TF is the failure risk threshold.

[0211] After generating the warning signal, the reasons for the system instability can also be analyzed according to the stability evaluation results. Analyze the operation data during the abnormal time period to identify the main factors leading to instability, such as excessive load fluctuations or voltage fluctuations. Identify the deficiencies in the optimization algorithm and control strategy. Then, according to the analysis results, adjust the parameters of the optimization algorithm to improve the system stability. According to the cause of the fault, adjust the parameters of the optimization algorithm, such as the crossover rate and mutation rate of the genetic algorithm: α → α - Δα; β → β - Δβ. Strengthen the communication and coordination between nodes to ensure the consistency of the optimization decisions of each node. Optimize the communication mechanism, reduce the data transmission delay, and improve the data synchronization. Implement a distributed coordination algorithm, such as DCA (Distributed Coordination Algorithm), to improve the coordination between each node. Implement the adjusted optimization strategy into the system and monitor it. Apply the new optimization algorithm parameters and coordination mechanism. Monitor the system operation status again within a fixed time and record the new stability coefficient. Evaluate the effect of the improved strategy to ensure the improvement of the system stability. Calculate the mean and standard deviation of the new stability coefficient, and compare the abnormal time proportion values before and after the improvement. According to the evaluation results, further optimize the strategy to ensure the long-term stable operation of the system.

[0212] Figure 3 An embodiment of a computer device according to the present invention is shown. The computer device may be a server, and the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0213] Those skilled in the art can understand, Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0214] In addition, the above-mentioned computer device provided by the present invention includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiment when executing the computer program.

[0215] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0216] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0217] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A distributed photovoltaic parallel optimization method for a distribution network, characterized in that: include: Obtain the number of distributed photovoltaic units of the distributed photovoltaic system in the distribution network and the real-time operation data of each distributed photovoltaic unit; According to the number of distributed photovoltaic units and the real-time operation data, an optimization task for each distributed photovoltaic unit is constructed, and a distributed computing node is configured for each optimization task to generate an optimization strategy for each distributed photovoltaic unit; Summarize the various optimization strategies, and based on the summarized optimization strategies, conduct simulation tests on the distributed photovoltaic system, and evaluate the stability of the distributed photovoltaic system based on the simulation test results; When the evaluation result shows that the distributed photovoltaic system is in an unstable state, the distributed photovoltaic system is analyzed to determine the fault factors that cause the distributed photovoltaic system to be in an unstable state; According to the fault factors, the optimization strategy of each distributed photovoltaic unit is adjusted; and based on the adjusted optimization strategy, the distributed photovoltaic system is optimized.

2. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: When acquiring the real-time operating data of each distributed photovoltaic unit, the voltage sensor, current sensor, power sensor, temperature sensor, irradiance sensor and smart meter pre-deployed on each distributed photovoltaic unit are used to acquire the real-time operating data of each distributed photovoltaic unit.

3. The method for parallel optimization of distributed photovoltaic power generation in distribution network according to claim 1, characterized in that: Configuring a distributed computing node for each of the optimization tasks to generate an optimization strategy for each distributed photovoltaic unit includes: A distributed computing node is configured for each of the optimization tasks, and based on the distributed computing nodes and according to the corresponding optimization tasks, the distributed photovoltaic system is locally optimized through a genetic algorithm to generate an optimization strategy for each distributed photovoltaic unit.

4. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: Based on the simulation test results, the stability of the distributed photovoltaic system is evaluated including: Based on the simulation test results, extract the voltage fluctuation amplitude data of each distributed computing node, and generate a node voltage fluctuation index according to the voltage fluctuation amplitude data; Based on the simulation test results, extract the load distribution change data of each distributed computing node, and generate a node load distribution anomaly index according to the load distribution change data; Calculating a stability coefficient of a distributed photovoltaic system according to the node voltage fluctuation index and the node load distribution abnormality index; The stability of the distributed photovoltaic system is determined according to the stability coefficient of the distributed photovoltaic system.

5. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 4, characterized in that: Generating a node voltage fluctuation index according to the voltage fluctuation amplitude data includes: The voltage fluctuation amplitude data extracted from each distributed computing node is preprocessed, and the preprocessed voltage data is subjected to wavelet decomposition to obtain signal features at different scales; Perform wavelet transform on the obtained signal features at different scales to obtain the wavelet coefficients at each position at each scale; All wavelet coefficients at each scale are counted, and the wavelet energy at each scale is calculated; and based on the wavelet energy at each scale, the node voltage fluctuation index is calculated.

6. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 5, characterized in that: The calculation formula of the wavelet decomposition is: Where X(t) is the original voltage fluctuation amplitude data in period t; φ J,k (t) and ψ J,k (t) are scaling function and wavelet function respectively; c J,k and d J,k are scale coefficients and wavelet coefficients; J is the number of decomposition layers; k is the decomposition position; j is the decomposition scale; the calculation formula of the wavelet energy is: Ej = ∑ k d J,k 2 ; Where Ej is the wavelet energy of each scale; d J,k is the wavelet coefficient; the calculation formula of the node voltage fluctuation index is: Where Vs is the node voltage fluctuation index; Ej is the wavelet energy of each scale.

7. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 4, characterized in that: Generating a node load distribution anomaly index according to the load distribution change data includes: Determining load data of distributed computing nodes at different time points according to the load distribution change data; Performing standardization processing on the load data, and forming a data matrix according to the load data after the standardization processing; Calculating the covariance matrix corresponding to the data matrix, and performing eigenvalue analysis on the covariance matrix to obtain eigenvalues ​​and eigenvectors; Sorting the eigenvalues, selecting eigenvalues ​​within a predetermined range as main components, and selecting corresponding eigenvectors based on the main components; The filtered eigenvectors are projected onto the data matrix to obtain the principal component scores; and the node load distribution anomaly index is calculated based on the principal component scores.

8. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 7, characterized in that: The calculation formula of the covariance matrix is: Where Σ is the covariance matrix of the data matrix; x i is the i-th sample, i.e., the node load vector; μ is the mean vector; N is the total number of samples; T is the matrix transpose; the calculation formula of the main component score is: Y=XV; where Y is the main component score; X is the data matrix; V is the selected feature vector; the calculation formula of the node load distribution anomaly index is: Where DM is the node load distribution abnormality index.

9. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 4, characterized in that: The calculation formula of the stability coefficient of the distributed photovoltaic system is: Where CM is the stability coefficient of the distributed photovoltaic system; DM is the node load distribution abnormality index; Vs is the node voltage fluctuation index; a1 and a2 decibels are the proportional coefficients of the node voltage fluctuation index and the node load distribution abnormality index, and a2>a1>0.

10. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 4, characterized in that: According to the stability coefficient of the distributed photovoltaic system, determining the stability of the distributed photovoltaic system includes: Comparing the stability coefficient of the distributed photovoltaic system with a preset reference threshold value of the stability coefficient of the distributed photovoltaic system; When the comparison result shows that the stability coefficient of the distributed photovoltaic system is greater than or equal to the stability coefficient reference threshold, it is determined that the distributed photovoltaic system is in a stable state; When the comparison result is that the stability coefficient of the distributed photovoltaic system is less than the stability coefficient reference threshold, it is determined that the distributed photovoltaic system is in an unstable state.

11. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: According to the fault factors, the optimization strategy for adjusting each distributed photovoltaic unit includes: According to the fault factors, the optimization algorithm parameters, communication protocol, data synchronization algorithm and node coordination algorithm of each distributed photovoltaic unit optimization strategy are adjusted.

12. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: Also includes: After optimizing the distributed photovoltaic system based on the adjusted optimization strategy, a stability coefficient of the distributed photovoltaic system within a predetermined time period is calculated and a data set is formed; Calculating a mean and a standard deviation of a data set, and calculating a fault risk threshold value based on the mean and the standard deviation of the data set; Compare and calculate the stability coefficient in the data set with the fault risk threshold, and obtain the abnormal time proportion value that the stability coefficient of the distributed photovoltaic system is less than the fault risk threshold within a predetermined time; Compare the abnormal time ratio value with a preset reference threshold of the abnormal time ratio value, and if the comparison result shows that the abnormal time ratio value is greater than or equal to the reference threshold, determine that the risk of failure of the distributed photovoltaic system is high and generate a warning signal; On the contrary, it is determined that the risk of failure of the distributed photovoltaic system is low and no warning signal is generated.

13. The method for parallel optimization of distributed photovoltaic power generation in a power distribution network according to claim 12, characterized in that: The calculation formula for the failure risk threshold is: TF = μ + kσ; where TF is the failure risk threshold; μ is the mean of the data set; σ is the standard deviation of the data set; k is the adjustment coefficient; the calculation formula for the abnormal time proportion value is: where PF is the normal time proportion value; x is the number of the stability coefficient in the data set, CM x is the stability coefficient in the data set, M is the total number of stability coefficients in the data set, L is the indicator function, when CM x < TF, L = 1; otherwise L = 0; TF is the failure risk threshold.

14. A distributed photovoltaic parallel optimization system for distribution network, characterized in that: include: A data acquisition module is used to obtain the number of distributed photovoltaic units of the distributed photovoltaic system in the distribution network and the real-time operation data of each distributed photovoltaic unit; A strategy generation module, used to construct an optimization task for each distributed photovoltaic unit according to the number of distributed photovoltaic units and the real-time operation data, and configure a distributed computing node for each optimization task to generate an optimization strategy for each distributed photovoltaic unit; The stability evaluation module is used to summarize various optimization strategies, and based on the summarized optimization strategies, perform simulation tests on the distributed photovoltaic system, and evaluate the stability of the distributed photovoltaic system based on the simulation test results; A fault analysis module is used to analyze the distributed photovoltaic system and determine the fault factors that cause the distributed photovoltaic system to be in an unstable state when the evaluation result shows that the distributed photovoltaic system is in an unstable state; The system optimization module is used to adjust the optimization strategy of each distributed photovoltaic unit according to the fault factor; and optimize the distributed photovoltaic system based on the adjusted optimization strategy.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 13 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

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