A Cooperative Optimization Method for Distributed Power Sources in Low-Voltage Distribution Substations

Abstract

The present invention discloses a collaborative optimization method for distributed power sources in low-voltage distribution areas, specifically related to the technical field of collaborative optimization. It includes collecting the operation status data of each distributed power source during collaborative operation, where the operation status data includes the voltage signals and harmonic spectrum data of each distributed power source. According to the operation performance of different distributed power sources, the probability distribution of the power change of the distributed power source is obtained. The historical uncertainty data of different distributed power sources is determined through entropy calculation. The operation status data and historical uncertainty data of each distributed power source are comprehensively analyzed to construct a power source evaluation model and generate a power source evaluation coefficient. The preset threshold is compared with the power source evaluation coefficient of each distributed power source to generate a warning signal. The present invention helps to accurately monitor and optimize distributed power sources, improving the power quality and operation stability of low-voltage distribution areas.

Description

Technical Field

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

[0002] With the wide application of distributed power sources in low-voltage distribution areas, they play an important role in improving energy utilization efficiency, reducing carbon emissions, and enhancing power supply reliability. However, due to the diverse types of distributed power sources (such as photovoltaic, wind power, energy storage, etc.), their operating characteristics are uncertain and random, resulting in power quality problems and a decline in grid stability. Due to the shortage of monitoring equipment or the limitation of the monitoring range, there is a lack of comprehensive collection of the operating state data of distributed power sources, which may not be able to effectively capture the dynamic characteristics of the power source operation, and it is easy to lead to misjudgment or omission of abnormal situations. Therefore, there is a lack of an effective optimization evaluation mechanism, and it is impossible to dynamically adjust the usage priorities of each power source, resulting in unreasonable scheduling.

[0003] To solve the above defects, a technical solution is provided now. Summary of the Invention

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

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A collaborative optimization method for distributed power sources in low-voltage distribution areas specifically includes the following steps:

[0007] S1: Collect the operating state data of each distributed power source during collaborative work. The operating state data includes the voltage signals and harmonic spectrum data of each distributed power source.

[0008] S2: Obtain the probability distribution of the power change of the distributed power source according to the operating performance of different distributed power sources, and determine the historical uncertainty data of different distributed power sources through entropy calculation.

[0009] S3: Conduct a comprehensive analysis of the operating state data and historical uncertainty data of each distributed power source, construct a power source evaluation model, and generate a power source evaluation coefficient.

[0010] S4: Compare the preset threshold with the power source evaluation coefficient of each distributed power source, determine the distributed power source whose power source evaluation coefficient is greater than the threshold within the monitoring interval, and generate a warning signal.

[0011] In a preferred embodiment, the operating state data includes:

[0012] The operating state data is represented by the harmonic distortion abnormal change coefficient and the three-phase unbalance fluctuation coefficient;

[0013] The acquisition logic of the harmonic distortion abnormal change coefficient is as follows: Set the monitoring interval and sampling frequency, obtain the continuous voltage signal within the monitoring interval, and obtain the discrete voltage data at the sampling frequency within the monitoring interval. Mark the discrete voltage data at the sampling frequency within the monitoring interval as: , where k = 0, 1, 2,..., N - 1, N is a positive integer, and k is the serial number of the sampling points;

[0014] Perform discrete Fourier transform on the sampled discrete voltage data to obtain the frequency-domain expression as: ; where is the complex spectral value at the sampling frequency;

[0015] Obtain the amplitude of each frequency component. The calculation formula is: ; where is the amplitude of the k-th component, is the k-th frequency component;

[0016] Convert the amplitudes of each harmonic to the effective value, and mark the amplitudes of each harmonic converted to the effective value as: , where ;

[0017] Set thresholds for the amplitudes of each harmonic, and mark the thresholds of each harmonic as: Compare the thresholds of each harmonic with the amplitudes of each harmonic to obtain the harmonics whose amplitudes of each harmonic are greater than the thresholds of each harmonic, and mark the harmonics whose amplitudes of each harmonic are greater than the thresholds of each harmonic as: , where g = 1, 2, 3,..., G, G is a positive integer, and g is the serial number of the harmonics whose amplitudes of each harmonic are greater than the thresholds of each harmonic;

[0018] Calculate the harmonic distortion abnormal change coefficient. The calculation formula is: ; where is the harmonic distortion abnormal change coefficient, is the threshold of the harmonic corresponding to the harmonic whose amplitude of each harmonic is greater than the threshold of each harmonic.

[0019] In a preferred embodiment, the three-phase unbalance fluctuation coefficient includes:

[0020] The acquisition logic of the three-phase unbalance fluctuation coefficient is as follows: According to the continuous voltage signal within the monitoring interval, obtain the maximum three-phase voltage at different times, the minimum three-phase voltage at different times, and the average three-phase voltage at different times, calculate the three-phase voltage unbalance degree data within the monitoring interval, and mark the three-phase voltage unbalance degree data within the monitoring interval as: , where , is the maximum value of the three-phase voltage at different times, is the minimum value of the three-phase voltage at different times, is the average value of the three-phase voltage at different times;

[0021] Calculate the average value and standard deviation of the three-phase voltage unbalance degree within the monitoring interval, and mark the average value and standard deviation of the three-phase voltage unbalance degree within the monitoring interval as: and , where , ;

[0022] Calculate the coefficient of variation of the three-phase voltage unbalance degree within the monitoring interval, and mark the coefficient of variation of the three-phase voltage unbalance degree within the monitoring interval as: , where ;

[0023] Calculate the three-phase unbalance fluctuation coefficient, and the calculation formula is: ; where is the three-phase unbalance fluctuation coefficient.

[0024] In a preferred embodiment, the historical uncertainty data includes:

[0025] Represent the historical uncertainty data through the random power entropy value coefficient;

[0026] The acquisition logic of the random power entropy value coefficient is as follows: Obtain the operation historical data of the distributed power source, preprocess the operation historical data of the distributed power source, including denoising and standardization processing, divide the historical power data of the distributed power source, calculate the probability of each interval, and mark the probability of each interval as: , where , is the frequency in the i-th interval, M is the total number of samples of the historical power data, i = 1, 2, 3,... I, I is a positive integer, and i is the interval number of the division;

[0027] According to the probability distribution of the historical data, use the Shannon entropy formula to calculate the entropy value of the power data, and the calculation formula is: ;

[0028] Calculate the random power entropy value coefficient, and the calculation formula is: ; where is the random power entropy value coefficient, is the maximum power output in the historical power data of the distributed power source, is the minimum power output in the historical power data of the distributed power source.

[0029] In a preferred embodiment, a power supply evaluation model is constructed, including:

[0030] The harmonic distortion abnormal change coefficient, the three-phase unbalance fluctuation coefficient, and the random power entropy value coefficient are weighted and calculated to construct a power supply evaluation model, and a power supply evaluation coefficient is generated. The calculation formula of the power supply evaluation coefficient is: ; where is the power supply evaluation coefficient, is the proportionality coefficient of the harmonic distortion abnormal change coefficient, is the proportionality coefficient of the three-phase unbalance fluctuation coefficient, is the proportionality coefficient of the random power entropy value coefficient, , , are all greater than 0 respectively.

[0031] In a preferred embodiment, a warning signal is generated, including:

[0032] 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, 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.

[0033] The technical effects and advantages of the present invention:

[0034] The present invention uses monitoring equipment to collect voltage signals and harmonic spectrum data of distributed power supplies, obtains their real-time operating states, calculates the probability distribution of the power change of distributed power supplies, and calculates its historical uncertainty data based on the entropy value, quantifies the randomness and stability of power output. By monitoring the power supply operating state and analyzing historical data, the coordinated operation of distributed power supplies is optimized, grid fluctuations and power quality problems are reduced. The present invention helps to accurately monitor and optimize distributed power supplies, improves the power quality and operating stability of low-voltage distribution substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings;

[0036] Figure 1 is a schematic flow chart of a method for coordinated optimization of distributed power supplies for low-voltage distribution substations according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1

[0039] Figure 1 It is a schematic flowchart of a distributed power source collaborative optimization method for a low-voltage distribution substation area of the present invention, specifically including the following steps:

[0040] S1: Collect the operation status data of each distributed power source during collaborative operation. The operation status data includes the voltage signals and harmonic spectrum data of each distributed power source;

[0041] S2: Obtain the probability distribution of the power change of the distributed power source according to the operation performance of different distributed power sources, and determine the historical uncertainty data of different distributed power sources through entropy value calculation;

[0042] S3: Conduct a comprehensive analysis of the operation status data and historical uncertainty data of each distributed power source, construct a power source evaluation model, and generate a power source evaluation coefficient;

[0043] S4: Compare the preset threshold with the power source evaluation coefficients of each distributed power source, determine the distributed power sources with power source evaluation coefficients greater than the threshold within the monitoring interval, and generate a warning signal.

[0044] In the low-voltage distribution substation area, different numbers and types of distributed power sources such as photovoltaic, wind power, and energy storage systems are connected, resulting in increased voltage fluctuations, power flow reversals, and the operational complexity of the distribution network. Therefore, by collecting the operation status data and historical uncertainty data of each distributed power source during collaborative operation, the operation performance of different distributed power sources can be evaluated.

[0045] The operation status data is represented by the harmonic distortion abnormal change coefficient and the three-phase unbalance fluctuation coefficient. Among them, the advantages of the harmonic distortion abnormal change coefficient are as follows:

[0046] The connection of distributed power sources to the low-voltage distribution substation area (such as photovoltaic, wind power, energy storage systems, etc.) will affect the harmonic characteristics of the power grid. Due to the non-linear inverters, power fluctuations, and grid-connected equipment characteristics of distributed power sources, it may cause voltage and current waveform distortion, thereby affecting the power quality. Using the harmonic distortion abnormal change coefficient helps to evaluate the changes of distributed power sources during operation;

[0047] Inverters of distributed power sources usually adopt different grid - connection control strategies (such as reactive power compensation, harmonic suppression, virtual synchronous machines, etc.). By monitoring the abnormal change coefficient of harmonic distortion, when the abnormal change coefficient of harmonic distortion continuously exceeds the threshold, the filtering strategy or control parameters of the inverter can be adjusted to reduce the influence of specific sub - harmonics. In areas with high penetration of distributed power sources, the active power filter (APF) can be intelligently adjusted to dynamically suppress key harmonic components and optimize the coordinated control of multiple distributed power sources, reducing the harmonic superposition effect between multiple power sources and improving the overall power quality.

[0048] The acquisition logic of the abnormal change coefficient of harmonic distortion is as follows: Set the monitoring interval and sampling frequency, obtain the continuous voltage signal within the monitoring interval, and obtain the discrete voltage data at the sampling frequency within the monitoring interval. Mark the discrete voltage data at the sampling frequency within the monitoring interval as: , where k = 0, 1, 2, ……, N - 1, N is a positive integer, and k is the serial number of the sampling points;

[0049] Perform discrete Fourier transform on the sampled discrete voltage data, and the frequency - domain expression is: ; where is the complex spectral value at the sampling frequency;

[0050] Obtain the amplitude of each frequency component, and the calculation formula is: ; where is the amplitude of the k - th component, is the k - th frequency component;

[0051] Convert the amplitude of each harmonic to the effective value, and mark the amplitude of each harmonic converted to the effective value as: , where ;

[0052] Set the threshold for the amplitude of each harmonic, and mark the threshold of each harmonic as: Compare the threshold of each harmonic with the amplitude of each harmonic, obtain the harmonics whose amplitude of each harmonic is greater than the threshold of each harmonic, and mark the harmonics whose amplitude of each harmonic is greater than the threshold of each harmonic as: , where g = 1, 2, 3, ……, G, G is a positive integer, and g is the harmonic number of the harmonics whose amplitude of each harmonic is greater than the threshold of each harmonic;

[0053] Calculate the abnormal change coefficient of harmonic distortion, and the calculation formula is: ; where is the abnormal change coefficient of harmonic distortion, is the harmonic threshold corresponding to the harmonics whose amplitude of each harmonic is greater than the threshold of each harmonic.

[0054] It should be noted that the monitoring interval, sampling frequency, and the thresholds of each harmonic are set by the staff in the professional field. The larger the abnormal change coefficient of harmonic distortion, the greater the distortion of voltage and current of the distributed power source within the monitoring interval, the less stable the system operation, which may cause equipment overheating, increased vibration, and electromagnetic interference, reduce the equipment life, and there may be an exacerbation of harmonic pollution, changes in system impedance, or some compensation devices failing to work properly.

[0055] Among them, the advantages of the three-phase unbalance fluctuation coefficient are as follows:

[0056] The three-phase unbalance fluctuation coefficient takes into account the maximum value, minimum value, and average value of voltage, and combines the coefficient of variation, making the evaluation of unbalance fluctuation more comprehensive. It is suitable for long-term monitoring of voltage fluctuations, especially in low-voltage distribution networks with distributed power sources, and can reflect the changes in power quality.

[0057] The output of distributed power sources such as photovoltaic and wind power in low-voltage distribution networks is random, and the voltage unbalance degree will fluctuate with time. By calculating the three-phase unbalance fluctuation coefficient, the impact of the distributed power source operation on the grid unbalance fluctuation can be evaluated.

[0058] By analyzing the unbalance fluctuations under different distributed power source operation modes (grid-connected, off-grid, power adjustment), the grid connection strategy can be optimized, reactive power compensation and power regulation can be optimized, the impact of distributed power source operation on grid stability can be reduced, and the power supply quality can be improved. If the control of a certain distributed power source inverter is abnormal, resulting in a large fluctuation in its output power, the three-phase unbalance fluctuation coefficient will increase, which may mean problems such as line impedance mismatch and poor contact of switching equipment, helping the operation and maintenance personnel quickly locate the fault point.

[0059] The acquisition logic of the three-phase unbalance fluctuation coefficient is as follows: According to the continuous voltage signals within the monitoring interval, obtain the maximum values of the three-phase voltages at different times, the minimum values of the three-phase voltages at different times, and the average values of the three-phase voltages at different times, calculate the three-phase voltage unbalance degree data within the monitoring interval, and mark the three-phase voltage unbalance degree data within the monitoring interval as: , where, , is the maximum value of the three-phase voltages at different times, is the minimum value of the three-phase voltages at different times, is the average value of the three-phase voltages at different times;

[0060] Calculate the average value and standard deviation of the three-phase voltage unbalance degree within the monitoring interval, and mark the average value and standard deviation of the three-phase voltage unbalance degree within the monitoring interval as: and , where, , ;

[0061] Calculate the coefficient of variation of the three-phase voltage unbalance degree within the monitoring interval, and mark the coefficient of variation of the three-phase voltage unbalance degree within the monitoring interval as: , where ;

[0062] Calculate the three-phase unbalance fluctuation coefficient, and the calculation formula is: ; where is the three-phase unbalance fluctuation coefficient.

[0063] It should be noted that the larger the three-phase unbalance fluctuation coefficient, the more it reflects the relative fluctuation of the voltage unbalance degree, avoiding misjudgment caused by relying solely on the standard deviation, indicating that the output power of this distributed power source fluctuates greatly. Especially for energy sources such as wind power and photovoltaics that are easily affected by the environment, their power changes may directly lead to an increase in voltage unbalance fluctuations, and this distributed power source cannot effectively adapt to the load characteristics of the substation area during operation within the monitoring interval.

[0064] There is a certain randomness in distributed power sources. If the randomness of the distributed power source is greater, it means that the output of this power source is more unstable and difficult to predict. Therefore, in some specific application scenarios, it may be necessary to optimize or temporarily not use this power source. The impacts of large randomness entropy on the low-voltage distribution network include:

[0065] Increased voltage volatility: If the randomness entropy of the distributed power source is relatively large, it means that its power output fluctuates greatly. Especially for power sources such as photovoltaics and wind power that are greatly affected by the weather, it 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 cope with these fluctuations;

[0066] Deterioration of power quality: Power quality problems mainly include voltage fluctuations, unbalances, frequency fluctuations, etc. Distributed power sources with larger entropy values are prone to cause these problems. Especially in the case of rapid load changes or multiple distributed power sources being connected to the grid, it may lead to frequent instability of the power grid;

[0067] Difficulty in scheduling: The power output of power sources with high entropy values is highly volatile and difficult to predict, posing challenges to power grid scheduling and power balance. For example, when the distribution network scheduling system faces highly unstable power sources, it may be difficult to perform effective power prediction and scheduling arrangements, thereby reducing the operating efficiency of the power grid.

[0068] Represent historical uncertainty data through the random power entropy value coefficient. The advantages of the random power entropy value coefficient are:

[0069] The random power entropy coefficient can effectively quantify the randomness and uncertainty of the output of distributed power sources, helping to identify the volatility of the power output. A larger entropy coefficient means that the power output has greater randomness and larger fluctuations, which may pose challenges to the stability and scheduling of the power grid. While a smaller entropy coefficient indicates that the power output is more stable and easier to schedule;

[0070] By calculating the entropy values of the historical data of each distributed power source in the low-voltage distribution substation area, the impact of these power sources on the power grid scheduling can be evaluated. When the entropy coefficient is large, additional scheduling strategies can be considered to balance the power grid load, such as increasing energy storage devices, adjusting the load, or optimizing the power source combination, to reduce the fluctuations and pressure on the power grid;

[0071] The random power entropy coefficient can be used to evaluate the operating performance of different distributed power sources at different times, especially the predictability of their outputs. Through historical data analysis, the power grid can understand which distributed power sources have poor output stability and which power sources have smoother outputs, facilitating targeted optimization;

[0072] With more and more distributed power sources (such as photovoltaic and wind energy) connected to the low-voltage distribution substation area, the power grid needs to improve its adaptability to these uncertain power sources. The calculation of the random power entropy coefficient not only helps to evaluate the volatility of the power source, but also enables the power grid to operate more smoothly in a high-uncertainty environment, promoting the power grid to achieve higher sustainability in the context of increasing renewable energy penetration.

[0073] The acquisition logic of the random power entropy coefficient is as follows: Obtain the operating historical data of the distributed power source, preprocess the operating historical data of the distributed power source, including denoising and normalization processing, divide the historical power data of the distributed power source, calculate the probability of each interval, and mark the probability of each interval as: , where, , is the frequency in the i-th interval, M is the total number of samples of the historical power data, i = 1, 2, 3,... I, I is a positive integer, and i is the interval number of the division;

[0074] It should be noted that the historical data may come from the output power of the distributed power source in different time periods (such as daily, weekly, monthly, etc.). These data points can include each timestamp of the power output, such as the power data per hour, per minute, or per second. The 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.

[0075] According to the probability distribution of the historical data, the Shannon entropy formula is used to calculate the entropy value of the power data, and the calculation formula is: ;

[0076] Calculate the random power entropy value coefficient, and the calculation formula is: ; where is the random power entropy value coefficient, is the maximum power output in the historical power data of the distributed power source, is the minimum power output in the historical power data of the distributed power source.

[0077] It should be noted that the larger the random power entropy value coefficient, the stronger the randomness of the power output in the historical data, the greater the fluctuation, and the greater the difficulty of power grid scheduling. It may be necessary to take measures to optimize the output stability of the power source (for example, smoothing the output through energy storage, adjusting the load distribution, etc.). On the contrary, it indicates that the power output is relatively stable, with less fluctuation, easy to predict and schedule, and is suitable for coordinated operation with the power grid.

[0078] Comprehensively analyze the operation status data and historical uncertainty data of each distributed power source during collaborative work, perform weighted calculations on the harmonic distortion abnormal change coefficient, three-phase unbalance fluctuation coefficient, and random power entropy value coefficient, construct a power source evaluation model, and generate a power source evaluation coefficient. The calculation formula of the power source evaluation coefficient is: ; where is the power source evaluation coefficient, is the proportional coefficient of the harmonic distortion abnormal change coefficient, is the proportional coefficient of the three-phase unbalance fluctuation coefficient, is the proportional coefficient of the random power entropy value coefficient, 、 、 are all greater than 0.

[0079] It can be seen from the formula that the larger the harmonic distortion abnormal change coefficient, the three-phase unbalance fluctuation coefficient, and the random power entropy value coefficient, the larger the power source evaluation coefficient, indicating that the operation performance of the distributed power source is poor. It is necessary to reduce the priority of using this distributed power source. According to the grid load situation, distributed power sources with smaller power source evaluation coefficients should be preferentially scheduled.

[0080] Set the power source evaluation coefficient threshold, compare the power source evaluation coefficient of each distributed power source with the power source evaluation coefficient threshold. If the power source evaluation coefficient is greater than the power source evaluation coefficient threshold, generate a warning signal and transmit the warning signal to the system background. The staff will determine whether to use this distributed power source based on the comprehensive consideration of the grid load situation. If the power source evaluation coefficient is less than the power source evaluation coefficient threshold, no warning signal will be generated.

[0081] The present invention uses monitoring devices to collect voltage signals and harmonic spectrum data of distributed power sources, obtains their real-time operating states, calculates the probability distribution of the power changes of the distributed power sources, and calculates their historical uncertainty data based on entropy values to quantify the randomness and stability of the power output. By monitoring the power operating states and analyzing historical data, the coordinated operation of the distributed power sources is optimized, grid fluctuations and power quality problems are reduced. The present invention helps to accurately monitor and optimize the distributed power sources, improving the power quality and operating stability of low-voltage distribution substations.

[0082] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0083] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 a website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0084] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0085] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0086] In several embodiments provided in this 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 merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0087] If the functions are implemented in the form of software function 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 this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0088] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

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: comparing a preset threshold with a power supply evaluation coefficient of each distributed power source, determining a distributed power source whose power supply evaluation coefficient is greater than the threshold within the monitoring interval, and generating a warning signal; 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.

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: 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.

5. A method for coordinated optimization of distributed power sources for low-voltage distribution stations according to claim 4, 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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