Distributed power supply collaborative optimization method for low-voltage distribution area

Abstract

The invention discloses a low-voltage distribution area-oriented distributed power supply collaborative optimization method, and particularly relates to the technical field of collaborative optimization, comprising the following steps: acquiring operation state data of each distributed power supply during collaborative work, the operation state data comprising voltage signals and harmonic spectrum data of each distributed power supply; according to the operation performance of different distributed power supplies, the probability distribution of the power change of the distributed power supplies is obtained, the historical uncertainty data of the different distributed power supplies are determined through entropy calculation, the operation state data and the historical uncertainty data of each distributed power supply are comprehensively analyzed, and a power supply evaluation model is constructed. And generating a power supply evaluation coefficient, comparing a preset threshold value with the power supply evaluation coefficient of each distributed power supply, and generating an early warning signal, thereby facilitating accurate monitoring and optimization of the distributed power supplies, and improving electric energy quality and operation stability of a low-voltage distribution area.

Description

Technical Field

[0001] The present invention relates to the field of collaborative optimization technology, and more specifically, to a distributed power supply collaborative optimization method for low-voltage distribution station areas. Background Art

[0002] With the widespread application of distributed power sources in low-voltage distribution areas, they have played an important role in improving energy utilization efficiency, reducing carbon emissions, and improving power supply reliability. However, due to the variety of distributed power sources (such as photovoltaics, wind power, energy storage, etc.), their operating characteristics are uncertain and random, leading to power quality problems and reduced grid stability. Due to the lack of monitoring equipment or the limitation of the monitoring scope, there is a lack of comprehensive operating status data collection for distributed power sources, which may not be able to effectively capture the dynamic characteristics of power supply operation, which may easily lead to misjudgment or omission of abnormal situations. Therefore, there is a lack of effective optimization and evaluation mechanism, and it is impossible to dynamically adjust the use priority of each power source, resulting in unreasonable scheduling.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a distributed power supply collaborative optimization method for a low-voltage distribution station area to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A distributed power supply collaborative optimization method for low-voltage distribution station areas specifically includes the following steps: S1: Collecting the operating status data of each distributed power source when working in coordination, the operating status data includes the voltage signal and harmonic spectrum data of each distributed power source; S2: According to the operating performance of different distributed power sources, the probability distribution of distributed power source power changes is obtained, and the historical uncertainty data of different distributed power sources are determined by entropy calculation; S3: Comprehensively analyze the operating status data and historical uncertainty data of each distributed power source, build a power supply evaluation model, and generate a power supply evaluation coefficient; S4: Compare the preset threshold with the power supply evaluation coefficient of each distributed power supply, determine the distributed power supply whose power supply evaluation coefficient is greater than the threshold within the monitoring interval, and generate an early warning signal.

[0006] In a preferred embodiment, the operating status data includes: The operating status data is represented by the harmonic distortion abnormal variation coefficient and the three-phase unbalance fluctuation coefficient; The acquisition logic of the harmonic distortion abnormal variation coefficient is: set the monitoring interval and sampling frequency, obtain the continuous voltage signal in the monitoring interval, and obtain the discrete voltage data under the sampling frequency in the monitoring interval, and mark the discrete voltage data under the sampling frequency in the monitoring interval as: , where k=0, 1, 2, ..., N-1, N is a positive integer, and k is the number of sampling points; Performing discrete Fourier transform on the sampled discrete voltage data, the frequency domain expression is: ;in, is the complex spectrum value at the sampling frequency; The amplitude of each frequency component is obtained using the following calculation formula: ;in, is the magnitude of the kth component, is the kth frequency component; Convert the amplitude of each harmonic into an effective value and mark it as: ,in, ; Set a threshold for each harmonic amplitude and mark each harmonic threshold as: , compare each harmonic threshold with the amplitude of each harmonic, obtain the harmonics whose harmonics are greater than each harmonic threshold, and mark the harmonics whose harmonics are greater than each harmonic threshold as: , where g=1, 2, 3, ..., G, G is a positive integer, and g is the harmonic number of each harmonic greater than the harmonic threshold; Calculate the abnormal variation coefficient of harmonic distortion, the calculation formula is: ;in, is the abnormal variation coefficient of harmonic distortion, It is the harmonic threshold corresponding to the harmonics greater than each harmonic threshold.

[0007] In a preferred embodiment, the three-phase unbalanced fluctuation coefficient includes: The acquisition logic of the three-phase unbalanced fluctuation coefficient is as follows: according to the continuous voltage signal in the monitoring interval, the maximum value of the three-phase voltage at different times, the minimum value of the three-phase voltage at different times and the average value of the three-phase voltage at different times are obtained, and the three-phase voltage unbalance data in the monitoring interval are calculated, and the three-phase voltage unbalance data in the monitoring interval are marked as: ,in, , is the maximum value of three-phase voltage at different times, is the minimum value of the three-phase voltage at different times, is the average value of three-phase voltage at different times; Calculate the average value and standard deviation of the three-phase voltage unbalance within the monitoring interval, and mark the average value and standard deviation of the three-phase voltage unbalance within the monitoring interval as: and ,in, , ; Calculate the coefficient of variation of the three-phase voltage unbalance within the monitoring interval, and mark the coefficient of variation of the three-phase voltage unbalance within the monitoring interval as: ,in, ; Calculate the three-phase unbalanced fluctuation coefficient using the following formula: ;in, is the three-phase unbalanced fluctuation coefficient.

[0008] In a preferred embodiment, the historical uncertainty data includes: The historical uncertainty data is represented by the random power entropy coefficient; The acquisition logic of the random power entropy coefficient is: obtain the operating history data of the distributed power source, pre-process the operating history data of the distributed power source, including denoising and standardization, divide the historical power data of the distributed power source, calculate the probability of each interval, and mark the probability of each interval as: ,in, , is the frequency in the i-th interval, M is the total number of samples of historical power data, i=1, 2, 3, ... I, I is a positive integer, and i is the number of the divided interval; According to the probability distribution of historical data, the Shannon entropy formula is used to calculate the entropy value of power data. The calculation formula is: ; Calculate the random power entropy coefficient, the calculation formula is: ;in, is the random power entropy coefficient, is the maximum power output in the historical power data of the distributed power source, It is the minimum power output in the historical power data of the distributed power source.

[0009] In a preferred embodiment, constructing a power supply evaluation model includes: The harmonic distortion abnormal variation coefficient, three-phase unbalanced fluctuation coefficient and random power entropy value coefficient are weighted and calculated to construct a power supply evaluation model and generate a power supply evaluation coefficient. The calculation formula of the power supply evaluation coefficient is: ;in, is the power supply evaluation coefficient, is the proportional coefficient of the abnormal variation coefficient of harmonic distortion, is the proportional coefficient of the three-phase unbalanced fluctuation coefficient, is the proportional coefficient of the random power entropy coefficient, , , Both are greater than 0.

[0010] In a preferred embodiment, generating an early warning signal includes: A power supply evaluation coefficient threshold is set, and the power supply evaluation coefficient of each distributed power supply is compared with the power supply evaluation coefficient threshold. If the power supply evaluation coefficient is greater than the power supply evaluation coefficient threshold, a warning signal is generated; if the power supply evaluation coefficient is less than the power supply evaluation coefficient threshold, no warning signal is generated.

[0011] Technical effects and advantages of the present invention: The present invention utilizes monitoring equipment to collect voltage signals and harmonic spectrum data of distributed power sources, obtains their real-time operating status, calculates the probability distribution of power changes of distributed power sources, and calculates their historical uncertainty data based on entropy values, quantifies the randomness and stability of power output, and optimizes the coordinated operation of distributed power sources by monitoring the power supply operating status and analyzing historical data, thereby reducing grid fluctuations and power quality issues. The present invention contributes to the accurate monitoring and optimization of distributed power sources, and improves the power quality and operating stability of low-voltage distribution station areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 The present invention is a flow chart of a distributed power supply collaborative optimization method for low-voltage distribution station areas. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Example 1 Figure 1 The present invention is a flow chart of a distributed power supply collaborative optimization method for low-voltage distribution station areas, which specifically includes the following steps: S1: Collecting the operating status data of each distributed power source when working in coordination, the operating status data includes the voltage signal and harmonic spectrum data of each distributed power source; S2: According to the operating performance of different distributed power sources, the probability distribution of distributed power source power changes is obtained, and the historical uncertainty data of different distributed power sources are determined by entropy calculation; S3: Comprehensively analyze the operating status data and historical uncertainty data of each distributed power source, build a power supply evaluation model, and generate a power supply evaluation coefficient; S4: Compare the preset threshold with the power supply evaluation coefficient of each distributed power supply, determine the distributed power supply whose power supply evaluation coefficient is greater than the threshold within the monitoring interval, and generate an early warning signal.

[0015] The access of different numbers and types of distributed power sources, such as photovoltaic, wind power and energy storage systems, to low-voltage distribution substations leads to voltage fluctuations, power flow reversal and increased operational complexity of the distribution network. Therefore, the operating performance of different distributed power sources is evaluated by collecting the operating status data and historical uncertainty data of each distributed power source when working in collaboration.

[0016] The operating status data is represented by the harmonic distortion abnormal variation coefficient and the three-phase unbalance fluctuation coefficient, wherein the harmonic distortion abnormal variation coefficient has the advantages of: The access of distributed power sources to low-voltage distribution areas (such as photovoltaic, wind power, energy storage systems, etc.) will affect the harmonic characteristics of the power grid. Due to the nonlinear inverter, power fluctuation and grid-connected equipment characteristics of distributed power sources, voltage and current waveforms may be distorted, thus affecting the power quality. The use of the abnormal variation coefficient of harmonic distortion helps to evaluate the changes of distributed power sources during operation. Inverters of distributed power sources usually adopt different grid-connected control strategies (such as reactive power compensation, harmonic suppression, virtual synchronous machines, etc.). By monitoring the abnormal variation coefficient of harmonic distortion, when the abnormal variation coefficient of harmonic distortion continues to exceed the threshold, the filtering strategy or control parameters of the inverter can be adjusted to reduce the impact of specific harmonics. In areas with high penetration rates of distributed power sources, the active filter (APF) can be intelligently adjusted to dynamically suppress key harmonic components, and optimize the coordinated control of multiple distributed power sources to reduce the harmonic superposition effect between multiple power sources and improve the overall power quality.

[0017] The acquisition logic of the harmonic distortion abnormal variation coefficient is: set the monitoring interval and sampling frequency, obtain the continuous voltage signal in the monitoring interval, and obtain the discrete voltage data under the sampling frequency in the monitoring interval, and mark the discrete voltage data under the sampling frequency in the monitoring interval as: , where k=0, 1, 2, ..., N-1, N is a positive integer, and k is the number of sampling points; Performing discrete Fourier transform on the sampled discrete voltage data, the frequency domain expression is: ;in, is the complex spectrum value at the sampling frequency; The amplitude of each frequency component is obtained using the following calculation formula: ;in, is the magnitude of the kth component, is the kth frequency component; Convert the amplitude of each harmonic into an effective value and mark it as: ,in, ; Set a threshold for each harmonic amplitude and mark each harmonic threshold as: , compare each harmonic threshold with the amplitude of each harmonic, obtain the harmonics whose harmonics are greater than each harmonic threshold, and mark the harmonics whose harmonics are greater than each harmonic threshold as: , where g=1, 2, 3, ..., G, G is a positive integer, and g is the harmonic number of each harmonic greater than the harmonic threshold; Calculate the abnormal variation coefficient of harmonic distortion, the calculation formula is: ;in, is the abnormal variation coefficient of harmonic distortion, It is the harmonic threshold corresponding to the harmonics greater than each harmonic threshold.

[0018] It should be noted that the monitoring interval, sampling frequency and each harmonic threshold are set by professional staff. The larger the harmonic distortion abnormal variation coefficient, the greater the voltage and current distortion of the distributed power supply within the monitoring interval, and the more unstable the system operation, which may cause equipment overheating, increased vibration and electromagnetic interference, shorten the equipment life, and there may be increased harmonic pollution, changes in system impedance or failure of some compensation devices to work properly.

[0019] Among them, the advantages of the three-phase unbalanced fluctuation coefficient are: The three-phase unbalanced fluctuation coefficient takes into account the maximum, minimum and average voltage values, and combines them with the coefficient of variation to make the unbalanced fluctuation assessment more comprehensive. It is suitable for long-term monitoring of voltage fluctuations, especially in low-voltage distribution networks containing distributed power sources, and can reflect changes in power quality. The output of distributed power sources such as photovoltaic and wind power in low-voltage distribution networks is random, and the voltage imbalance will fluctuate over time. By calculating the three-phase imbalance fluctuation coefficient, the impact of distributed power generation on grid imbalance fluctuation during operation can be evaluated; By analyzing the unbalanced fluctuations under different distributed power supply operation modes (grid-connected, off-grid, power adjustment), the grid-connected strategy can be optimized, reactive power compensation and power regulation can be optimized, the impact of distributed power supply operation on grid stability can be reduced, and the power supply quality can be improved. If a distributed power supply inverter is abnormally controlled, causing its output power to fluctuate greatly, the three-phase unbalanced fluctuation coefficient will increase, which may mean problems such as line impedance mismatch and poor contact of switching equipment, helping operation and maintenance personnel to quickly locate the fault point.

[0020] The acquisition logic of the three-phase unbalanced fluctuation coefficient is as follows: according to the continuous voltage signal in the monitoring interval, the maximum value of the three-phase voltage at different times, the minimum value of the three-phase voltage at different times and the average value of the three-phase voltage at different times are obtained, and the three-phase voltage unbalance data in the monitoring interval are calculated, and the three-phase voltage unbalance data in the monitoring interval are marked as: ,in, , is the maximum value of three-phase voltage at different times, is the minimum value of the three-phase voltage at different times, is the average value of three-phase voltage at different times; Calculate the average value and standard deviation of the three-phase voltage unbalance within the monitoring interval, and mark the average value and standard deviation of the three-phase voltage unbalance within the monitoring interval as: and ,in, , ; Calculate the coefficient of variation of the three-phase voltage unbalance within the monitoring interval, and mark the coefficient of variation of the three-phase voltage unbalance within the monitoring interval as: ,in, ; Calculate the three-phase unbalanced fluctuation coefficient using the following formula: ;in, is the three-phase unbalanced fluctuation coefficient.

[0021] It should be noted that the larger the three-phase unbalanced fluctuation coefficient is, the more it reflects the relative fluctuation of the voltage imbalance, avoiding misjudgment caused by relying solely on the standard deviation. This indicates that the output power fluctuation of the distributed power source is large, especially for energy sources such as wind power and photovoltaics that are easily affected by the environment. Their power changes may directly lead to increased voltage imbalance fluctuations. When the distributed power source operates within the monitoring range, it cannot effectively adapt to the load characteristics of the substation.

[0022] Distributed power sources have a certain degree of randomness. The greater the randomness of distributed power sources, the more unstable and unpredictable the output of the power source will be. Therefore, in some specific applications, it may be necessary to optimize or temporarily not use the power source. The impact of high random entropy on low-voltage distribution networks includes: Increased voltage fluctuation: If the random entropy of distributed power sources is large, it means that their power output fluctuates greatly, especially for power sources such as photovoltaics and wind power that are greatly affected by weather, which may cause more severe voltage fluctuations in the substation area. The stability of the power grid will be greatly challenged, especially the low-voltage distribution network may not be able to quickly adjust to these fluctuations; Power quality degradation: Power quality issues mainly include voltage fluctuation, imbalance, frequency fluctuation, etc. Distributed power sources with large entropy values ​​are prone to these problems, especially when the load changes rapidly or multiple distributed power sources are connected to the grid, which may lead to frequent grid instability; Difficulty in dispatching: The output of high-entropy power sources is highly volatile and difficult to predict, which brings challenges to grid dispatching and power balance. For example, when the distribution network dispatching system faces highly unstable power sources, it may be difficult to make effective power forecasts and dispatch arrangements, thereby reducing the operating efficiency of the grid.

[0023] The historical uncertainty data is represented by the random power entropy coefficient. The advantages of the random power entropy coefficient are: The random power entropy coefficient can effectively quantify the randomness and uncertainty of distributed power output and help identify the volatility of power output. A larger entropy coefficient means that the power output has greater randomness and greater volatility, which may pose challenges to the stability and scheduling of the power grid. A smaller entropy coefficient indicates that the power output is relatively stable and easy to schedule; By calculating the entropy value of the historical data of each distributed power source in the low-voltage distribution area, the impact of these power sources on the grid dispatch can be evaluated. When the entropy coefficient is large, additional dispatch strategies can be considered to balance the grid load, such as adding energy storage equipment, adjusting the load, or optimizing the power source combination to reduce the fluctuation and pressure of the grid; The random power entropy coefficient can be used to evaluate the operating performance of different distributed power sources in different time periods, especially the predictability of their output. Through historical data analysis, the power grid can understand which distributed power sources have poor output stability and which power sources have smoother output, so as to facilitate targeted optimization; As more and more distributed power sources (such as photovoltaics and wind power) are connected to low-voltage distribution areas, the power grid needs to improve its ability to adapt to these uncertain power sources. The calculation of random power entropy coefficients not only helps to evaluate the volatility of power sources, but also helps the power grid to operate more smoothly under high uncertainty environments, and promotes the power grid to achieve higher sustainability in the context of increasing penetration of renewable energy.

[0024] The acquisition logic of the random power entropy coefficient is: obtain the operating history data of the distributed power source, pre-process the operating history data of the distributed power source, including denoising and standardization, divide the historical power data of the distributed power source, calculate the probability of each interval, and mark the probability of each interval as: ,in, , is the frequency in the i-th interval, M is the total number of samples of historical power data, i=1, 2, 3, ... I, I is a positive integer, and i is the number of the divided interval; It should be noted that historical data may come from the output power of distributed power sources in different time periods (such as days, weeks, months, etc.). These data points may include every timestamp of power output, such as power data every hour, every minute, or every second. Historical data may have different output ranges, so the power data in different time periods are partitioned and the frequency (probability) of each interval is calculated.

[0025] According to the probability distribution of historical data, the Shannon entropy formula is used to calculate the entropy value of power data. The calculation formula is: ; Calculate the random power entropy coefficient, the calculation formula is: ;in, is the random power entropy coefficient, is the maximum power output in the historical power data of the distributed power source, It is the minimum power output in the historical power data of the distributed power source.

[0026] It should be noted that the larger the random power entropy coefficient is, the more random the power output in the historical data is, the greater the fluctuation is, the more difficult the grid dispatch is, and measures may need to be taken to optimize the output stability of the power supply (for example, smoothing the output through energy storage, adjusting load distribution, etc.). On the contrary, it means that the power output is relatively stable, with less volatility, easy to predict and dispatch, and suitable for coordinated operation with the grid.

[0027] The operating status data and historical uncertainty data of each distributed power source when working together are comprehensively analyzed, and the harmonic distortion abnormal variation coefficient, three-phase unbalanced fluctuation coefficient and random power entropy value coefficient are weighted and calculated to build a power supply evaluation model and generate a power supply evaluation coefficient. The calculation formula of the power supply evaluation coefficient is: ;in, is the power supply evaluation coefficient, is the proportional coefficient of the abnormal variation coefficient of harmonic distortion, is the proportional coefficient of the three-phase unbalanced fluctuation coefficient, is the proportional coefficient of the random power entropy coefficient, , , Both are greater than 0.

[0028] It can be seen from the formula that the larger the abnormal variation coefficient of harmonic distortion, the three-phase unbalanced fluctuation coefficient and the random power entropy coefficient, the larger the power supply evaluation coefficient, which means that the operating performance of the distributed power supply is poor and the priority of using the distributed power supply needs to be reduced. According to the grid load conditions, the distributed power supply with a smaller power supply evaluation coefficient is preferentially dispatched.

[0029] Set a power supply evaluation coefficient threshold, compare the power supply evaluation coefficient of each distributed power supply with the power supply evaluation coefficient threshold. If the power supply evaluation coefficient is greater than the power supply evaluation coefficient threshold, generate a warning signal, transmit the warning signal to the system background, and the staff will determine whether to use the distributed power supply based on comprehensive consideration of the load conditions of the power grid. If the power supply evaluation coefficient is less than the power supply evaluation coefficient threshold, no warning signal is generated.

[0030] The present invention utilizes monitoring equipment to collect voltage signals and harmonic spectrum data of distributed power sources, obtains their real-time operating status, calculates the probability distribution of power changes of distributed power sources, and calculates their historical uncertainty data based on entropy values, quantifies the randomness and stability of power output, and optimizes the coordinated operation of distributed power sources by monitoring the power supply operating status and analyzing historical data, thereby reducing grid fluctuations and power quality issues. The present invention contributes to the accurate monitoring and optimization of distributed power sources, and improves the power quality and operating stability of low-voltage distribution station areas.

[0031] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0032] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0033] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0034] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0035] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0036] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0037] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed power supply collaborative optimization method for low-voltage distribution station areas, characterized in that: The specific steps include: S1: Collecting the operating status data of each distributed power source when working in coordination, the operating status data includes the voltage signal and harmonic spectrum data of each distributed power source; S2: According to the operating performance of different distributed power sources, the probability distribution of distributed power source power changes is obtained, and the historical uncertainty data of different distributed power sources are determined by entropy calculation; S3: Comprehensively analyze the operating status data and historical uncertainty data of each distributed power source, build a power supply evaluation model, and generate a power supply evaluation coefficient; S4: Compare the preset threshold with the power supply evaluation coefficient of each distributed power supply, determine the distributed power supply whose power supply evaluation coefficient is greater than the threshold within the monitoring interval, and generate an early warning signal.

2. According to the method of claim 1, the distributed power supply collaborative optimization method for low-voltage distribution station area is characterized in that: Operational status data, including: The operating status data is represented by the harmonic distortion abnormal variation coefficient and the three-phase unbalance fluctuation coefficient; The acquisition logic of the harmonic distortion abnormal variation coefficient is: set the monitoring interval and sampling frequency, obtain the continuous voltage signal in the monitoring interval, and obtain the discrete voltage data under the sampling frequency in the monitoring interval, and mark the discrete voltage data under the sampling frequency in the monitoring interval as: , where k=0, 1, 2, ..., N-1, N is a positive integer, and k is the number of sampling points; Performing discrete Fourier transform on the sampled discrete voltage data, the frequency domain expression is: ;in, is the complex spectrum value at the sampling frequency; The amplitude of each frequency component is obtained using the following calculation formula: ;in, is the magnitude of the kth component, is the kth frequency component; Convert the amplitude of each harmonic into an effective value and mark it as: ,in, ; Set a threshold for each harmonic amplitude and mark each harmonic threshold as: , compare each harmonic threshold with the amplitude of each harmonic, obtain the harmonics whose harmonics are greater than each harmonic threshold, and mark the harmonics whose harmonics are greater than each harmonic threshold as: , where g=1, 2, 3, ..., G, G is a positive integer, and g is the harmonic number of each harmonic greater than the harmonic threshold; Calculate the abnormal variation coefficient of harmonic distortion, the calculation formula is: ;in, is the abnormal variation coefficient of harmonic distortion, It is the harmonic threshold corresponding to the harmonics greater than each harmonic threshold.

3. A method for coordinated optimization of distributed power sources for low-voltage distribution stations according to claim 2, characterized in that: Three-phase unbalanced fluctuation coefficient, including: The acquisition logic of the three-phase unbalanced fluctuation coefficient is as follows: according to the continuous voltage signal in the monitoring interval, the maximum value of the three-phase voltage at different times, the minimum value of the three-phase voltage at different times and the average value of the three-phase voltage at different times are obtained, and the three-phase voltage unbalance data in the monitoring interval are calculated, and the three-phase voltage unbalance data in the monitoring interval are marked as: ,in, , is the maximum value of three-phase voltage at different times, is the minimum value of the three-phase voltage at different times, is the average value of three-phase voltage at different times; Calculate the average value and standard deviation of the three-phase voltage unbalance within the monitoring interval, and mark the average value and standard deviation of the three-phase voltage unbalance within the monitoring interval as: and ,in, , ; Calculate the coefficient of variation of the three-phase voltage unbalance within the monitoring interval, and mark the coefficient of variation of the three-phase voltage unbalance within the monitoring interval as: ,in, ; Calculate the three-phase unbalanced fluctuation coefficient using the following formula: ;in, is the three-phase unbalanced fluctuation coefficient.

4. A method for coordinated optimization of distributed power sources for low-voltage distribution stations according to claim 3, characterized in that: Historical uncertainty data, including: The historical uncertainty data is represented by the random power entropy coefficient; The acquisition logic of the random power entropy coefficient is: obtain the operating history data of the distributed power source, pre-process the operating history data of the distributed power source, including denoising and standardization, divide the historical power data of the distributed power source, calculate the probability of each interval, and mark the probability of each interval as: ,in, , is the frequency in the i-th interval, M is the total number of samples of historical power data, i=1, 2, 3, ... I, I is a positive integer, and i is the number of the divided interval; According to the probability distribution of historical data, the Shannon entropy formula is used to calculate the entropy value of power data. The calculation formula is: ; Calculate the random power entropy coefficient, the calculation formula is: ;in, is the random power entropy coefficient, is the maximum power output in the historical power data of the distributed power source, It is the minimum power output in the historical power data of the distributed power source.

5. A method for coordinated optimization of distributed power sources for low-voltage distribution stations according to claim 4, characterized in that: Build a power supply assessment model, including: The harmonic distortion abnormal variation coefficient, three-phase unbalanced fluctuation coefficient and random power entropy value coefficient are weighted and calculated to construct a power supply evaluation model and generate a power supply evaluation coefficient. The calculation formula of the power supply evaluation coefficient is: ;in, is the power supply evaluation coefficient, is the proportional coefficient of the abnormal variation coefficient of harmonic distortion, is the proportional coefficient of the three-phase unbalanced fluctuation coefficient, is the proportional coefficient of the random power entropy coefficient, , , Both are greater than 0.

6. A method for coordinated optimization of distributed power sources for low-voltage distribution stations according to claim 5, characterized in that: Generate early warning signals, including: A power supply evaluation coefficient threshold is set, and the power supply evaluation coefficient of each distributed power source is compared with the power supply evaluation coefficient threshold. If the power supply evaluation coefficient is greater than the power supply evaluation coefficient threshold, a warning signal is generated; if the power supply evaluation coefficient is less than the power supply evaluation coefficient threshold, no warning signal is generated.

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