Comprehensive Load Characteristic Analysis Method and System for Distribution Networks with Distributed Generators

Abstract

This invention discloses a comprehensive load characteristic analysis method and system for distribution networks containing distributed generation (DG) sources, relating to the field of distribution network analysis technology. The method involves periodically performing stability analysis on each connected DG source. When the distribution network load exceeds a preset load threshold, the system first determines whether the stored capacity of the DG source meets the network's needs. DG sources that do not meet the threshold are removed from the power source list, completing the first deletion process. The system then performs a backtesting analysis on the stability of the remaining DG sources. DG sources that fail the backtesting analysis are removed from the power source list, completing the second deletion process. Based on the stability backtesting analysis results, the system dynamically adjusts the output capacity of each DG source. The analysis system dynamically adjusts the output of the DG sources according to the backtesting analysis results to match the distribution network load demand, ensuring the stability and safety of the distribution network under heavy load conditions.

Description

Technical Field

[0001] This invention relates to the field of distribution network analysis technology, specifically to a method and system for comprehensive load characteristic analysis of distribution networks containing distributed generation sources. Background Technology

[0002] The analysis system is mainly used to study and analyze the changes in the comprehensive load characteristics of the distribution network after the integration of distributed power sources. With the rapid development of renewable energy (such as photovoltaic and wind power) and distributed generation technology, a large number of distributed power sources have been integrated into the distribution network, changing the traditional operation mode of the distribution network with centralized power sources as the core. In order to improve the operating efficiency and safety and stability of the power grid, it is particularly important to conduct in-depth analysis of the load characteristics of the distribution network containing distributed power sources.

[0003] The existing technology has the following drawbacks:

[0004] 1. Existing technologies lack comprehensive dynamic assessment of the operating status of distributed power sources after they are connected to the distribution network. In particular, when the grid load fluctuates drastically or the output of distributed power sources changes, it is impossible to obtain the stability of the power source or the energy storage status in real time, which can easily lead to unstable power sources participating in power supply and reduce the reliability of the grid.

[0005] 2. Existing distributed generation dispatch strategies are mostly based on fixed rules, failing to dynamically respond to the real-time operating status of the distribution network and the stability analysis results of distributed generation. The lack of flexibility in dispatch schemes leads to low efficiency in resource utilization and high operating costs. Summary of the Invention

[0006] The purpose of this invention is to provide a comprehensive load characteristic analysis method and system for distribution networks containing distributed generation sources. Based on the backtracking analysis results, the output of distributed generation sources is dynamically adjusted to match the load demand of the distribution network, thereby ensuring the stability and safety of the distribution network when the load is high.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a comprehensive load characteristic analysis method for a distribution network containing distributed generation sources, the analysis method comprising the following steps:

[0008] The analysis system obtains information on all distributed power sources connected to the distribution network through the distribution network management system, performs initial labeling on each distributed power source, and constructs a power source list from all labeled distributed power sources.

[0009] Stability analysis is performed on each connected distributed power source at regular intervals. When the load of the distribution network exceeds the preset load threshold, it is first determined whether the stored power of the distributed power source meets the needs of the distribution network. Distributed power sources that do not meet the needs are deleted from the power source list, thus completing one deletion process of the power source list.

[0010] The stability of the remaining distributed power sources is backtracked and analyzed. Distributed power sources that fail the stability backtracking analysis are removed from the power source list, completing the second deletion process of the power source list. The output power of each distributed power source is dynamically adjusted according to the stability backtracking analysis results.

[0011] After periodically obtaining the retrospective analysis results and usage status of distributed power sources, it is determined whether the distribution network still needs to use the distributed power source, and management strategies are generated based on the determination results.

[0012] In a preferred embodiment, stability analysis is performed periodically on each connected distributed power source, including the following steps:

[0013] After obtaining the operating data of the power generation equipment of each distributed power source, the operating data is substituted into a linear regression analysis algorithm for comprehensive calculation, thereby obtaining the anomaly index of each distributed power source, expressed as: In the formula, abnormal is the abnormality index. For the operating data of each generating device in a distributed power source, {G1, G2, ..., G... n} represents the regression coefficient, and the regression coefficient is greater than 0;

[0014] The obtained anomaly index is compared with a preset index threshold. The index threshold is used to determine whether the distributed power supply is stable or not. If the anomaly index is greater than the index threshold, the distributed power supply is considered to be unstable. If the anomaly index is less than or equal to the index threshold, the distributed power supply is considered to be stable.

[0015] In a preferred embodiment, the stability of the remaining distributed power sources is backtested, and those distributed power sources that fail the stability backtesting analysis are removed from the power source list, completing the second deletion process of the power source list, including the following steps:

[0016] A backtracking analysis is performed on the stability of the distributed power sources in the power source list after a deletion. The logic of the backtracking analysis is as follows: obtain the abnormal indexes obtained from multiple historical time points of the distributed power source, calculate the mean and standard deviation of the abnormal indexes based on the abnormal indexes obtained from multiple time points, and divide the mean of the obtained abnormal indexes by the standard deviation of the abnormal indexes to obtain the overall abnormal coefficient of the distributed power source.

[0017] The overall anomaly coefficient is compared with the preset second coefficient threshold. If the overall anomaly coefficient of the distributed power source is greater than the second coefficient threshold, the stability backtracking analysis of the distributed power source is deemed unqualified, and the distributed power source is removed from the power source list for the second time.

[0018] In a preferred embodiment, the mean and standard deviation of the anomaly index are calculated based on the anomaly index obtained at multiple time points, expressed as follows: In the formula, abnormal avg The mean of the abnormality index. Q The abnormality index is the standard deviation, where m is the number of time points. i The abnormality index value is obtained at the i-th time point.

[0019] In a preferred embodiment, the output power of each distributed power source is dynamically adjusted based on the results of stability backtracking analysis, including the following steps:

[0020] The output power of each distributed power source is dynamically adjusted by acquiring the overall anomaly coefficient. The adjustment algorithm is: dl new =dl old / ln(yc z +1), where dl new The adjusted output power, dl old The initial preset output power supply, yc z The overall anomaly coefficient is given.

[0021] In a preferred embodiment, after periodically obtaining the retrospective analysis results and usage status of distributed power sources, it is determined whether the distribution network still needs to use the distributed power source, and a management strategy is generated based on the determination result, including the following steps:

[0022] Regularly obtain the overall anomaly coefficient of the distributed power source, as well as the usage frequency and cumulative idle time of the distributed power source;

[0023] The management factor of distributed power sources is obtained by comprehensively calculating the overall anomaly coefficient, usage frequency, and accumulated idle time. The expression is as follows: In the formula, GL z δ represents the frequency of use, and yc represents the management factor. z ε is the overall anomaly coefficient, α is the idle cumulative duration, and β and γ are the adjustment coefficients for usage frequency, overall anomaly coefficient and idle cumulative duration, respectively, and α, β and γ are all greater than 0.

[0024] The acquired management factor is compared with the preset management threshold. The management threshold is used to determine whether the distribution network still needs to retain the distributed generation. If the management factor of the distributed generation is less than or equal to the management threshold, it is determined that the distribution network still needs to retain the distributed generation. If the management factor of the distributed generation is greater than the management threshold, it is determined that the distribution network does not need to retain the distributed generation.

[0025] The management strategy generated based on the judgment results is as follows: the distributed power sources that need to be retained are included in the retention set, and the distributed power sources that do not need to be retained are included in the deletion set. The retention set and the deletion set are then sent to the distribution network management platform.

[0026] In a preferred embodiment, the distributed power source is a photovoltaic power station, and the operating data of the photovoltaic power station's power generation equipment includes the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation, the partial discharge intensity of the step-up transformer, and the light intensity fluctuation.

[0027] The anomaly index of the photovoltaic power plant is obtained by substituting the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation, the partial discharge intensity of the step-up transformer, and the irradiance fluctuation into a linear regression analysis algorithm. The expression is as follows: In the formula, abnormal is the abnormality index. The parameters are the photovoltaic panel temperature rise rate, power factor deviation, step-up transformer partial discharge intensity, and light intensity fluctuation, respectively. {G1, G2, G3, G4} are regression coefficients, and the regression coefficients are greater than 0.

[0028] A comprehensive load characteristic analysis system for distribution networks containing distributed generation sources includes a power source list construction module, a distributed generation source analysis module, and a management module.

[0029] Power source list construction module: Obtain information on all distributed power sources connected to the distribution network through the distribution network management system, initially mark each distributed power source, and construct a power source list from all marked distributed power sources;

[0030] Distributed power source analysis module: Periodically performs stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, it first determines whether the stored power of the distributed power source meets the needs of the distribution network. Distributed power sources that do not meet the requirements are deleted from the power source list, completing the first deletion process of the power source list. The stability of the remaining distributed power sources is backtracked and analyzed. Distributed power sources that fail the stability backtracking analysis are deleted from the power source list, completing the second deletion process of the power source list. The output power of each distributed power source is dynamically adjusted according to the stability backtracking analysis results.

[0031] Management module: After periodically obtaining the retrospective analysis results and usage status of distributed power sources, it determines whether the distribution network still needs to use the distributed power source, and generates management strategies based on the judgment results.

[0032] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0033] 1. This invention performs stability analysis on each connected distributed power source at regular intervals. When the load of the distribution network exceeds a preset load threshold, it first determines whether the stored capacity of the distributed power source meets the network's needs. Distributed power sources that do not meet the requirements are removed from the power source list, completing the first deletion process. The remaining distributed power sources undergo stability backtracking analysis, and those that fail the backtracking analysis are removed from the power source list, completing the second deletion process. Based on the stability backtracking analysis results, the system dynamically adjusts the output capacity of each distributed power source. The analysis system dynamically adjusts the output of the distributed power sources according to the backtracking analysis results to match the load demand of the distribution network, ensuring the stability and safety of the distribution network under heavy load.

[0034] 2. This invention periodically acquires the retrospective analysis results and usage status of distributed generation sources to determine whether the distribution network still needs to use the distributed generation source, and generates management strategies based on the determination results. By analyzing the distributed generation source, the analysis system determines whether the distributed generation source still needs to be used, thereby improving the stability and scheduling flexibility of the subsequent operation of the distribution network, reducing unnecessary power source activation, and optimizing grid operating costs. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0036] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example 1: Please refer to Figure 1 As shown in this embodiment, the comprehensive load characteristic analysis method for a distribution network containing distributed generation includes the following steps:

[0039] The analysis system obtains information on all distributed power sources connected to the distribution network through the distribution network management system, initially marks each distributed power source, constructs a power source list for all marked distributed power sources, and periodically performs stability analysis on each connected distributed power source. When the load of the distribution network exceeds a preset load threshold, it first determines whether the stored capacity of the distributed power source meets the needs of the distribution network. Distributed power sources that do not meet the requirements are deleted from the power source list, completing the first deletion process. The system then performs a backtracking analysis on the stability of the remaining distributed power sources, and deletes distributed power sources that fail the stability backtracking analysis from the power source list, completing the second deletion process. Based on the stability backtracking analysis results, the system dynamically adjusts the output capacity of each distributed power source. After periodically obtaining the backtracking analysis results and usage status of the distributed power sources, it determines whether the distribution network still needs to use the distributed power source and generates management strategies based on the determination results.

[0040] This application performs stability analysis on each connected distributed power source at regular intervals. When the load of the distribution network exceeds a preset load threshold, it first determines whether the stored capacity of the distributed power source meets the network's needs. Distributed power sources that do not meet the requirements are removed from the power source list, completing the first deletion process. The remaining distributed power sources undergo stability backtracking analysis, and those that fail the backtracking analysis are removed from the power source list, completing the second deletion process. Based on the stability backtracking analysis results, the system dynamically adjusts the output capacity of each distributed power source. The analysis system dynamically adjusts the output of the distributed power sources according to the backtracking analysis results to match the load demand of the distribution network, ensuring the stability and safety of the distribution network under heavy load.

[0041] This application determines whether the distribution network still needs to use a distributed power source after periodically obtaining the retrospective analysis results and usage status of the distributed power source, and generates management strategies based on the judgment results. The analysis system improves the stability and scheduling flexibility of the distribution network's subsequent operation by analyzing the distributed power source and determining whether it needs to continue to be used, thereby reducing unnecessary power source activation and optimizing grid operating costs.

[0042] Example 2: The analysis system obtains information on all distributed power sources connected to the distribution network through the distribution network management system, initially marks each distributed power source, and constructs a power source list for all marked distributed power sources, including the following steps:

[0043] Information on all connected distributed power sources is collected through the distribution network management system, including but not limited to the following:

[0044] Distributed power source type (PV, wind power, energy storage, etc.). Connection point location and node number. Rated power, current output power, and energy storage status. Environmental parameters (e.g., solar irradiance, wind speed). Obtain distribution network topology information to clarify the connection relationship between distributed power sources and the grid. Validate the collected data, removing duplicates, missing information, or errors. Format the power source information uniformly and initialize it into a data structure recognizable by the analysis system.

[0045] Based on the operating characteristics and access conditions of distributed power sources, initial labeling rules are set, including: classifying and labeling distributed power sources according to their type (photovoltaic, wind power, energy storage, etc.); labeling the rated capacity and current output power level of each power source; labeling the specific location and node number of the power source's access point in the distribution network topology; and generating a unique set of labeling attributes for each distributed power source, which is stored as the power source's initial attributes.

[0046] All distributed power source information that has been initially marked will be aggregated to construct a power source list containing all connected power sources. The list will include: power source ID, connection location, type, capacity, operating status, and stability level. A dynamic update mechanism will be set up to periodically update the power source list based on real-time collected data on the operating status of the distributed power sources (such as output power and remaining energy storage capacity).

[0047] By associating the power source list with the distribution network topology information, the impact range of each distributed power source on the distribution network operation is clarified. Based on the power source list, fundamental data support is provided for subsequent stability analysis, output power optimization, and management strategy generation.

[0048] The completed power source list is stored in the analysis system's database to ensure efficient retrieval and updates in subsequent operations. A visual interface for the list is provided, facilitating grid operators to view and manage the status of connected distributed power sources.

[0049] Stability analysis is performed periodically on each connected distributed power source, including the following steps:

[0050] After obtaining the operating data of the power generation equipment of each distributed power source, the operating data is substituted into a linear regression analysis algorithm for comprehensive calculation, thereby obtaining the anomaly index of each distributed power source, expressed as: In the formula, abnormal is the abnormality index. This refers to the operational data of each generating device in a distributed power source, where n represents the number of generating devices' operational data, {G1, G2, ..., G...}. n} represents the regression coefficient, and the regression coefficient is greater than 0;

[0051] The larger the anomaly index, the worse the stability of the distributed power source. The obtained anomaly index is compared with the preset index threshold. The index threshold is used to determine whether the stability of the distributed power source is good or bad. If the anomaly index is greater than the index threshold, the distributed power source is judged to be in poor stability. If the anomaly index is less than or equal to the index threshold, the distributed power source is judged to be in good stability.

[0052] The logical components of the anomaly index used in this invention are as follows: taking the impact of the operating data of various power generation equipment on the operational stability of distributed power sources as an example, the components are: first, indicators, i.e., factors that cause changes in the operational stability of distributed power sources (in this invention, the impact of the operating data of various power generation equipment on the operational stability of distributed power sources); second, the weights of these indicators, i.e., the proportion of each major influencing data when it is generated; and third, the calculation equation, i.e., the mathematical calculation process used to obtain the result, which is the anomaly index obtained by calculating the indicators with their respective weights through the calculation equation.

[0053] The main impact data obtained from the sample were transformed and processed into data language recognizable by computer software. Secondly, these evaluation factors were analyzed using SPSS software through Logistic Regression to identify factors and their weights that were significantly correlated with the results. Thirdly, the evaluation factors and weights were substituted into the Logistic Regression equation to obtain the results, specifically:

[0054] First, ensure the integrity of the key influencing data by handling missing and outlier values. Transform the data into a format that SPSS can recognize, typically as .csv or .xlsx. Then import it into SPSS. Open SPSS, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analyze" menu, then select the "Regression" option and choose "Bivariate Logistic Regression." In the dialog box, add the dependent variable (outcome) and independent variables (key influencing data) to the corresponding boxes. SPSS will then fit a Logistic regression based on the selected variables. In the model fitting process, the output will show information such as model coefficients, standard errors, and p-values. Examining the coefficients and p-values ​​helps determine which variables are significantly correlated with the results. Typically, a p-value less than 0.05 is considered significant. While fitting the model, variable selection methods, such as stepwise regression, are used to help screen for the most relevant factors. Based on the coefficients of the logistic regression model, the magnitude of the coefficients reflects the degree of influence of each factor on the result, and the sign of the coefficients indicates the direction of influence. After obtaining the significant factors and their coefficients, the logistic regression equation is obtained. This equation is used to calculate the probability of each sample and thus predict the results.

[0055] To better illustrate the above scheme, the following examples are provided in this application;

[0056] If the distributed power source is a wind power station, the acquired equipment operation data includes the vibration amplitude of the rotor bearing of the wind turbine, the decibel of gearbox noise, and wind speed fluctuations.

[0057] Install vibration sensors or acceleration sensors on or near the bearing to collect the vibration amplitude of the rotor bearing in real time; install noise sensors (microphones) or acoustic sensors on the outside of the gearbox or near key components to collect the gearbox noise in decibels in real time.

[0058] The calculation logic for wind speed fluctuation is as follows: during the monitoring period, the wind speed at multiple time points in the environment where the wind turbine is located is obtained, and the wind speed standard deviation is calculated based on the wind speed at multiple time points. The wind speed standard deviation is the wind speed fluctuation, and the wind speed standard deviation is calculated using existing known standard deviations.

[0059] By substituting the rotor bearing vibration amplitude, gearbox noise decibels, and wind speed wave into a linear regression analysis algorithm, an anomaly index for the wind power station is obtained, expressed as: In the formula, abnormal is the abnormality index. The values ​​are rotor bearing vibration amplitude, gearbox noise decibels, and wind speed fluctuation, respectively. {G1, G2, G3} are regression coefficients, and the regression coefficients are greater than 0.

[0060] Rotor bearings are key mechanical components of wind turbines, and their vibration amplitude reflects the bearing's operating condition and the health of the mechanical structure.

[0061] Small vibration amplitude (within normal range): indicates that the bearing is running smoothly, there are no abnormalities in the mechanical structure, and the operation is highly stable.

[0062] Increased vibration amplitude (outside the normal range): Increased vibration may be caused by bearing wear, imbalance, poor lubrication or mechanical loosening, indicating that the fan is in abnormal operation and its stability has decreased.

[0063] Excessive vibration amplitude (severe abnormality): Excessive vibration can cause fatigue damage, increase the risk of mechanical component failure, and may lead to fan shutdown or equipment damage.

[0064] Gearbox noise is an important indicator of the internal operating status of a gearbox. The noise level is usually closely related to the gear meshing quality, wear degree, and lubrication condition.

[0065] Low noise level (within normal range): This indicates that the gears run smoothly, are well lubricated, and have high operational stability.

[0066] Increased noise level (beyond normal range): Increased noise may be caused by gear wear, poor meshing, or insufficient lubrication, indicating an abnormality inside the gearbox and a decrease in operational stability.

[0067] Excessive noise level (severe abnormality): Excessive noise may be accompanied by increased vibration or damage to internal components, resulting in a significant decrease in operational stability. Immediate shutdown and maintenance are required.

[0068] Wind speed fluctuations directly affect the operating load and output power of wind turbines. Excessive fluctuations may lead to unstable operation or even protective shutdown.

[0069] Wind speed fluctuations are small (within normal range): wind speed changes are stable, the fan operates stably, power output is stable, and the operation is highly stable.

[0070] Increased wind speed fluctuations (beyond normal range): Increased wind speed fluctuations will cause the wind turbine to frequently adjust the blade angle and power generation, increasing the mechanical load and pressure on the control system, and reducing operational stability.

[0071] Excessive wind speed fluctuations (severe fluctuations): Severe fluctuations may trigger the wind turbine's automatic protection system, leading to shutdown, or even causing fatigue damage to mechanical components.

[0072] If the distributed power source is a photovoltaic power station, the acquired equipment operation data includes the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation, the partial discharge intensity of the step-up transformer, and the light intensity fluctuation.

[0073] The temperature of the photovoltaic panel is monitored by a temperature sensor installed on the back of the photovoltaic panel. The temperature difference is obtained by subtracting the temperature of the previous moment from the current temperature. The temperature difference is then divided by the monitoring time to obtain the rate of temperature rise of the photovoltaic panel.

[0074] The deviation of the photovoltaic inverter's power factor is obtained by subtracting the actual power factor of the photovoltaic inverter from its standard power factor.

[0075] The method for obtaining light intensity fluctuations is similar to that for wind speed fluctuations, and will not be described in detail here.

[0076] The partial discharge intensity of the step-up transformer can be obtained online using existing partial discharge monitoring instruments, which can detect partial discharge activity in the transformer in real time.

[0077] Partial discharge monitoring instruments include:

[0078] OMICRON (such as: OMICRON-PD-Testing-System);

[0079] Megger (such as: Megger-PD-Detection-Equipment);

[0080] HIOKI (such as: HIOKI-PD-Monitoring-Instruments).

[0081] These instruments typically provide real-time numerical displays and are able to accurately monitor the intensity of partial discharge.

[0082] The anomaly index of the photovoltaic power plant is obtained by substituting the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation, the partial discharge intensity of the step-up transformer, and the irradiance fluctuation into a linear regression analysis algorithm. The expression is as follows: In the formula, abnormal is the abnormality index. The parameters are the photovoltaic panel temperature rise rate, power factor deviation, step-up transformer partial discharge intensity, and light intensity fluctuation, respectively. {G1, G2, G3, G4} are regression coefficients, and the regression coefficients are greater than 0.

[0083] A rapid rate of temperature increase indicates that the photovoltaic module may be malfunctioning due to poor heat dissipation, rapid changes in ambient temperature, or system failure.

[0084] High temperatures can reduce the output efficiency of photovoltaic modules, increase the risk of hot spot effects, and even cause module damage.

[0085] A slow or normal rate of temperature rise indicates that the system is operating relatively stably, the components have adequate heat dissipation capabilities, and the environmental conditions are suitable.

[0086] The higher the rate of temperature rise, the worse the operational stability of the photovoltaic panel. Prolonged high temperatures may adversely affect the lifespan of the module.

[0087] A large deviation in the power factor indicates an abnormal ratio of reactive to active power, which may be caused by inverter regulation failure, grid voltage fluctuations, or unstable output from photovoltaic modules. Excessive deviation increases the reactive power compensation pressure on the grid and affects power quality.

[0088] A small deviation in power factor or a value close to the rated value (e.g., 0.95 or above) indicates that the reactive power of the system is well matched with that of the power grid and the system is operating stably.

[0089] The greater the deviation of the power factor, the worse the adaptability of the photovoltaic power station to the grid, which may lead to grid connection failures or a decrease in efficiency.

[0090] High partial discharge intensity indicates a deterioration in the transformer's insulation condition, such as insulation aging, air gap discharge, or other dielectric defects, which may develop into insulation breakdown. Sustained high-intensity discharge will shorten the transformer's lifespan and increase the risk of failure.

[0091] Low or no partial discharge intensity indicates that the transformer has good insulation performance and is operating normally.

[0092] The higher the intensity of partial discharge, the worse the operational stability of the step-up transformer, which may lead to system shutdown or power outage in severe cases.

[0093] Significant fluctuations in sunlight intensity: This may be caused by rapid changes in cloud cover, sudden weather changes, or localized shading, leading to frequent fluctuations in the output power of photovoltaic modules. Excessive fluctuations in the output power of photovoltaic power plants pose challenges to the stability of inverters and grid connection.

[0094] Small or stable fluctuations in light intensity: The photovoltaic system outputs stable power and has high operational stability.

[0095] The greater the fluctuation in sunlight intensity, the worse the stability of the output power of the photovoltaic power station, which may affect the grid connection quality and power generation efficiency.

[0096] When the load on the distribution network exceeds a preset load threshold, first determine whether the stored capacity of the distributed generation meets the needs of the distribution network. Distributed generation that does not meet the requirements is removed from the power source list, completing one deletion process for the power source list. This includes the following steps:

[0097] The analysis system monitors the real-time load of the distribution network. When the real-time load exceeds the load threshold, it indicates that the load on the distribution network is increasing and it is necessary to supplement the current through distributed power sources to reduce the operating burden of the substation.

[0098] If the stored capacity of the distributed power source is less than the capacity threshold, it is determined that the stored capacity of the distributed power source does not meet the needs of the distribution network. The distributed power source that does not meet the needs is then removed from the power source list, completing one deletion process of the power source list.

[0099] The stability of the remaining distributed power sources is backtested. Distributed power sources that fail the stability backtesting analysis are removed from the power source list, completing the second deletion process of the power source list, including the following steps:

[0100] A backtracking analysis is performed on the stability of distributed power sources in the power source list after a deletion. The logic of the backtracking analysis is as follows: obtain the abnormal indices obtained from multiple historical time points of the distributed power sources, and calculate the mean and standard deviation of the abnormal indices based on the abnormal indices obtained from multiple time points. The expressions are as follows: In the formula, abnormal avg

[0101] The mean of the abnormality index. Q The abnormality index is the standard deviation, where m is the number of time points. i The abnormality index value obtained at the i-th time point;

[0102] The average value of the obtained anomaly index is divided by the standard deviation of the anomaly index to obtain the overall anomaly coefficient of the distributed power source, thus completing the retrospective analysis of the distributed power source.

[0103] This application performs a comprehensive backtracking analysis on the anomaly indices obtained from multiple historical data of distributed power sources. The larger the overall anomaly coefficient value, the worse the overall stability of the distributed power source over a period of time. The obtained overall anomaly coefficient is compared with a preset second coefficient threshold. If the overall anomaly coefficient of the distributed power source is greater than the second coefficient threshold, the stability backtracking analysis of the distributed power source is deemed unqualified, and the distributed power source is deleted from the power source list for the second time. Distributed power sources with an overall anomaly coefficient less than or equal to the second coefficient threshold are retained.

[0104] The output power of each distributed power source is dynamically adjusted based on the stability backtracking analysis results, including the following steps:

[0105] In the list of power sources that have completed the secondary deletion process, the larger the overall anomaly coefficient of the distributed power source, the worse the stability of the distributed power source is.

[0106] Therefore, the output power of each distributed power source is dynamically adjusted by obtaining the overall anomaly coefficient. The adjustment algorithm is: dl new =dl old / ln(yc z +1), where dl new The adjusted output power, dl old For the initial preset output power supply (usually based on the historical output power of a distributed power source), yc z The overall anomaly coefficient;

[0107] In other words, the higher the overall anomaly coefficient of a distributed power source in the power source list, the more necessary it is to reduce the output power of that distributed power source, so as to ensure the stable operation of not only the distributed power source but also the distribution power source.

[0108] It should be noted that when the output power of some distributed power sources is reduced, the total output power of all distributed power sources supplying power to the distribution network will decrease. If there are idle distributed power sources in the power source list, the output power will be compensated by these idle distributed power sources. If there are no idle distributed power sources, the output power will be compensated by distributed power sources whose overall anomaly coefficient is less than or equal to the first coefficient threshold. The first coefficient threshold is less than the second coefficient threshold. If the overall anomaly coefficient is less than or equal to the first coefficient threshold, it indicates that the stability of the distributed power source is good, and the output power of the distributed power source can be increased.

[0109] After periodically obtaining the retrospective analysis results and usage status of distributed generation sources, it is determined whether the distribution network still needs to use the distributed generation source, and a management strategy is generated based on the determination results, including the following steps:

[0110] Regularly obtain the overall anomaly coefficient of the distributed power source, as well as the usage frequency and cumulative idle time of the distributed power source;

[0111] Obtain the historical usage count of the distributed power source, and divide the historical usage count by the monitoring duration to obtain the usage frequency. The higher the usage frequency, the higher the confidence level of the distributed power source in the distribution network, and the more it needs to continue to be used.

[0112] The accumulated idle time is obtained through the management system logs of the distributed power source. The idle time of the distributed power source when it was in an unused state in history is obtained. The idle time of multiple unused states is summed to obtain the accumulated idle time. The larger the accumulated idle time, the lower the confidence of the distributed power source in the distribution network, and the less it needs to continue to be used.

[0113] The management factor of distributed power sources is obtained by comprehensively calculating the overall anomaly coefficient, usage frequency, and accumulated idle time. The expression is as follows: In the formula, GL z δ represents the frequency of use, and yc represents the management factor. z ε is the overall anomaly coefficient, α is the idle cumulative duration, and β and γ are the adjustment coefficients for usage frequency, overall anomaly coefficient and idle cumulative duration, respectively, and α, β and γ are all greater than 0.

[0114] The larger the management factor, the less the distributed power source needs to be used. The obtained management factor is compared with the preset management threshold. The management threshold is used to determine whether the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is less than or equal to the management threshold, it is determined that the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is greater than the management threshold, it is determined that the distribution network does not need to retain the distributed power source.

[0115] The management strategy generated based on the judgment results is as follows: the distributed power sources that need to be retained are included in the retention set, and the distributed power sources that do not need to be retained are included in the deletion set. The retention set and the deletion set are then sent to the distribution network management platform.

[0116] Example 3: The integrated load characteristic analysis system for a distribution network containing distributed power sources described in this example includes a power source list construction module, a distributed power source analysis module, and a management module;

[0117] Power source list construction module: Obtain information on all distributed power sources connected to the distribution network through the distribution network management system, initially mark each distributed power source, construct a power source list for all marked distributed power sources, and send the power source list to the distributed power source analysis module and management module;

[0118] Distributed power source analysis module: Periodically performs stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, it first determines whether the stored power of the distributed power source meets the needs of the distribution network. Distributed power sources that do not meet the requirements are deleted from the power source list, completing the first deletion process of the power source list. The stability of the remaining distributed power sources is backtracked and analyzed. Distributed power sources that fail the stability backtracking analysis are deleted from the power source list, completing the second deletion process of the power source list. The output power of each distributed power source is dynamically adjusted according to the stability backtracking analysis results. The stability backtracking analysis results are sent to the management module.

[0119] Management module: After periodically obtaining the retrospective analysis results and usage status of distributed power sources, it determines whether the distribution network still needs to use the distributed power source, and generates management strategies based on the judgment results.

[0120] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0121] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0122] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for comprehensive load characteristic analysis of distribution networks containing distributed generation sources, characterized in that: The analytical method includes the following steps: The analysis system obtains information on all distributed power sources connected to the distribution network through the distribution network management system, performs initial labeling on each distributed power source, and constructs a power source list from all labeled distributed power sources. Stability analysis is performed on each connected distributed power source at regular intervals. When the load of the distribution network exceeds the preset load threshold, it is first determined whether the stored power of the distributed power source meets the needs of the distribution network. Distributed power sources that do not meet the needs are deleted from the power source list, thus completing one deletion process of the power source list. The stability of the remaining distributed power sources is backtracked and analyzed. Distributed power sources that fail the stability backtracking analysis are removed from the power source list, completing the second deletion process of the power source list. The output power of each distributed power source is dynamically adjusted according to the stability backtracking analysis results. After periodically obtaining the retrospective analysis results and usage status of distributed power sources, it is determined whether the distribution network still needs to use the distributed power source, and management strategies are generated based on the determination results. After periodically obtaining the retrospective analysis results and usage status of distributed generation sources, it is determined whether the distribution network still needs to use the distributed generation source, including the following steps: The management factor of distributed power sources is obtained by comprehensively calculating the overall anomaly coefficient, usage frequency, and accumulated idle time. The expression is as follows: In the formula, GL z δ represents the frequency of use, and yc represents the management factor. z ε is the overall anomaly coefficient, α is the idle cumulative duration, and β and γ are the adjustment coefficients for usage frequency, overall anomaly coefficient and idle cumulative duration, respectively, and α, β and γ are all greater than 0. The acquired management factor is compared with the preset management threshold. The management threshold is used to determine whether the distribution network still needs to retain distributed generation. If the management factor of the distributed generation is greater than or equal to the management threshold, it is determined that the distribution network still needs to retain the distributed generation. If the management factor of the distributed generation is less than the management threshold, it is determined that the distribution network does not need to retain the distributed generation.

2. The method for comprehensive load characteristic analysis of a distribution network containing distributed generation sources according to claim 1, characterized in that: Stability analysis is performed periodically on each connected distributed power source, including the following steps: After obtaining the operating data of the power generation equipment of each distributed power source, the operating data is substituted into a linear regression analysis algorithm for comprehensive calculation, thereby obtaining the anomaly index of each distributed power source, expressed as: In the formula, abnormal is the abnormality index. For the operating data of each generating device in a distributed power source, {G1, G2, ..., G... n } represents the regression coefficient, and the regression coefficient is greater than 0; The obtained anomaly index is compared with a preset index threshold. The index threshold is used to determine whether the distributed power supply is stable or not. If the anomaly index is greater than the index threshold, the distributed power supply is considered to be unstable. If the anomaly index is less than or equal to the index threshold, the distributed power supply is considered to be stable.

3. The method for comprehensive load characteristic analysis of a distribution network containing distributed generation sources according to claim 2, characterized in that: The stability of the remaining distributed power sources is backtested. Distributed power sources that fail the stability backtesting analysis are removed from the power source list, completing the second deletion process of the power source list, including the following steps: A backtracking analysis is performed on the stability of the distributed power sources in the power source list after a deletion. The logic of the backtracking analysis is as follows: obtain the abnormal indexes obtained from multiple historical time points of the distributed power source, calculate the mean and standard deviation of the abnormal indexes based on the abnormal indexes obtained from multiple time points, and divide the mean of the obtained abnormal indexes by the standard deviation of the abnormal indexes to obtain the overall abnormal coefficient of the distributed power source. The overall anomaly coefficient is compared with the preset second coefficient threshold. If the overall anomaly coefficient of the distributed power source is greater than the second coefficient threshold, the stability backtracking analysis of the distributed power source is deemed unqualified, and the distributed power source is removed from the power source list for the second time.

4. The method for comprehensive load characteristic analysis of a distribution network containing distributed generation sources according to claim 3, characterized in that: The mean and standard deviation of the anomaly index are calculated based on anomaly indices obtained from multiple time points. The expressions are as follows: In the formula, abnormal avg The mean of the abnormality index. Q The abnormality index is the standard deviation, where m is the number of time points. i The abnormal index value is obtained at the i-th time point.

5. The method for comprehensive load characteristic analysis of a distribution network containing distributed generation sources according to claim 4, characterized in that: The output power of each distributed power source is dynamically adjusted based on the stability backtracking analysis results, including the following steps: The output power of each distributed power source is dynamically adjusted by acquiring the overall anomaly coefficient. The adjustment algorithm is: dl new =dl old / ln(yc z +1), where dl new The adjusted output power, dl old The initial preset output power supply, yc z This represents the overall anomaly coefficient.

6. The method for comprehensive load characteristic analysis of a distribution network containing distributed generation sources according to claim 5, characterized in that: The management strategy is generated based on the judgment results, including the following steps: Distributed power sources that need to be retained are added to the retention set, and distributed power sources that do not need to be retained are added to the deletion set. The retention set and the deletion set are then sent to the distribution network management platform.

7. The method for comprehensive load characteristic analysis of a distribution network containing distributed generation sources according to claim 2, characterized in that: The distributed power source is a photovoltaic power station. The operating data of the photovoltaic power station's power generation equipment includes the rate of temperature rise of the photovoltaic panel, the deviation of the photovoltaic inverter's power factor, the partial discharge intensity of the step-up transformer, and the fluctuation of the light intensity. The anomaly index of the photovoltaic power plant is obtained by substituting the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation, the partial discharge intensity of the step-up transformer, and the irradiance fluctuation into a linear regression analysis algorithm. The expression is as follows: In the formula, abnormal is the abnormality index. The parameters are the photovoltaic panel temperature rise rate, power factor deviation, step-up transformer partial discharge intensity, and light intensity fluctuation, respectively. {G1, G2, G3, G4} are regression coefficients, and the regression coefficients are greater than 0.

8. A comprehensive load characteristic analysis system for a distribution network containing distributed generation, used to implement the analysis method described in any one of claims 1-7, characterized in that: It includes a power supply list construction module, a distributed power supply analysis module, and a management module; Power source list construction module: Obtain information on all distributed power sources connected to the distribution network through the distribution network management system, initially mark each distributed power source, and construct a power source list from all marked distributed power sources; Distributed power source analysis module: Periodically performs stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, it first determines whether the stored power of the distributed power source meets the needs of the distribution network. Distributed power sources that do not meet the requirements are deleted from the power source list, completing the first deletion process of the power source list. The stability of the remaining distributed power sources is backtracked and analyzed. Distributed power sources that fail the stability backtracking analysis are deleted from the power source list, completing the second deletion process of the power source list. The output power of each distributed power source is dynamically adjusted according to the stability backtracking analysis results. Management module: After periodically obtaining the retrospective analysis results and usage status of distributed power sources, it determines whether the distribution network still needs to use the distributed power source, and generates management strategies based on the judgment results.

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