Power system load unbalance compensation method for photovoltaic power network access

Abstract

The invention relates to the technical field of power systems, in particular to a photovoltaic power grid-connected power system load imbalance compensation method, which comprises the following steps of: acquiring generated power of photovoltaic power generation at each moment in each period, voltage data of photovoltaic power generation at a power grid access end and three-phase current data; collecting power data of each load end in the power grid at each moment, and collecting voltage and impedance of each line in the power grid at each moment; determining a waveform difference index of the three-phase current in each period; obtaining a network access influence coefficient of the photovoltaic power of each period; determining a power supply consistency coefficient and a supply-demand matching coefficient of each period; acquiring an impedance fluctuation coefficient of each period; and optimizing a droop coefficient in the droop control algorithm, and compensating the load imbalance of the power system by using the optimized droop control algorithm. According to the invention, the load imbalance compensation effect of the power system is improved.

Description

Technical Field

[0001] This application relates to the technical field of power systems, and particularly to a method for compensating load imbalance in a power system for photovoltaic power grid connection. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, photovoltaic power generation, as a clean and sustainable energy form, is being widely used globally. The large-scale grid connection of photovoltaic power brings new opportunities to the power system, but also brings many challenges. However, due to the intermittent and volatile characteristics of photovoltaic power generation, its output power is greatly affected by weather conditions, which also leads to the increasingly prominent problem of load imbalance in the power system.

[0003] The droop control algorithm is widely used in the power regulation of microgrids because it can dynamically allocate power to provide additional compensation to the phases with too high or too low loads, thus alleviating the load imbalance problem. However, when the existing technology distributes power to the power grid through the droop control algorithm, it usually adopts the traditional droop control strategy with fixed coefficients, and does not deeply consider the matching relationship between the random power supply of wind power and the load changes of the power grid, resulting in poor effects of the set droop control parameters on balancing the load of the power system. Summary of the Invention

[0004] To solve the above technical problems, this application provides a method for compensating load imbalance in a power system for photovoltaic power grid connection to solve the existing problems.

[0005] The method for compensating load imbalance in a power system for photovoltaic power grid connection of this application adopts the following technical solutions: An embodiment of this application provides a method for compensating load imbalance in a power system for photovoltaic power grid connection, and the method includes the following steps: Collect the power generation power of photovoltaic power generation at each moment in each period, the voltage data of photovoltaic power generation at the grid connection end, and the three-phase current data, collect the power data of each load end in the grid at each moment, and collect the voltage and impedance of each line in the grid at each moment; Analyze the similarity degree between the voltage data distribution of photovoltaic power generation at the grid connection end in each period and the sine signal to obtain the sine similarity coefficient of each period; based on the fluctuation difference between any two phases in the three-phase current data of each period, determine the waveform difference index of the three-phase current in each period; Denote the similarity between the phase A current and the phase B current of each cycle at different time delays as the first similarity, and denote the similarity between the phase B current and the phase C current of each cycle at different time delays as the second similarity. Analyze the difference between the first similarity and the second similarity, denoted as the first difference. Combine the sine similarity coefficient and the waveform difference index to determine the grid connection influence coefficient of each cycle of photovoltaic power. Denote the sum of the powers of all load ends at each moment as the load power. Analyze the correlation between the power generation power and the load power of each cycle to determine the power supply consistency coefficient of each cycle. Form a power supply sequence and a load sequence respectively with the power generation power and the load power at all moments of each cycle. Obtain all the peaks in the power supply sequence and the load sequence respectively. Take the mean value of the differences of the peaks with the same serial numbers in the power supply sequence and the load sequence as the peak difference index of each cycle. Take the difference of the slopes of the fitting lines of the power supply sequence and the load sequence of each cycle as the trend difference index of each cycle. Combine the power supply consistency coefficient to determine the supply-demand matching coefficient of each cycle. Obtain the voltage mean value and the impedance mean value of all moments of each line within each cycle. Denote the difference between the voltage mean value and the impedance mean value of each line as the second difference. Calculate the difference between the dispersion degree of the voltage mean values of all lines and the dispersion degree of the impedance mean values, denoted as the third difference. Combine the dispersion degree of the impedance mean value to determine the impedance fluctuation coefficient of each cycle. Calculate the product of the grid connection influence coefficient and the impedance fluctuation coefficient of each cycle. Calculate the sum value of the product and a preset value greater than 0. Take the ratio of the supply-demand matching coefficient of each cycle to the sum value as the droop stability factor of each cycle. Adjust the droop coefficient in the droop control algorithm based on the droop stability factor of each cycle, and use the optimized droop control algorithm to compensate for the load imbalance of the power system.

[0006] In one of the embodiments, the determination of the sine similarity coefficient includes: Form a voltage sequence with the voltage data of photovoltaic power generation at all moments of each cycle at the grid connection end. Use the frequency domain transformation algorithm to obtain the frequency domain information of the voltage sequence, and construct a standard sine signal of the voltage sequence. The sine similarity coefficient is the correlation coefficient between the voltage sequence and the standard sine signal.

[0007] In one of the embodiments, the determination process of the waveform difference index is as follows: Form an A-phase sequence, a B-phase sequence, and a C-phase sequence respectively with the phase A current, the phase B current, and the phase C current at all moments of each cycle. Calculate the metric distance between any two of the A-phase sequence, the B-phase sequence, and the C-phase sequence. The waveform difference index is the mean value of all the metric distances of each cycle.

[0008] In one embodiment, the determination of the network access influence coefficient includes: The network access influence coefficient of each period is positively correlated with the waveform difference index and the first difference, and negatively correlated with the sine similarity coefficient.

[0009] In one embodiment, the power supply consistency coefficient is the correlation coefficient between the power generation power and the load power at all times in each period.

[0010] In one embodiment, the determination of the supply-demand matching coefficient includes: The supply-demand matching coefficient of each period is positively correlated with the power supply consistency coefficient, and negatively correlated with the peak difference index and the trend difference index.

[0011] In one embodiment, the determination of the impedance fluctuation coefficient includes: The impedance fluctuation coefficient is positively correlated with the dispersion degree of the impedance mean value of all lines, and negatively correlated with the dispersion degrees of the third difference and the second difference of all lines.

[0012] In one embodiment, the second difference is the ratio of the voltage mean value of each line to the impedance mean value.

[0013] In one embodiment, adjusting the droop coefficient in the droop control algorithm based on the droop stability factor of each period includes: Calculating the addition result of the normalized value of the reciprocal of the droop stability factor of each period and a preset constant greater than 0 and less than 1. The droop coefficient in the droop control algorithm of each period is the product of the addition result of each period and the droop coefficient of the previous period of each period.

[0014] This application has at least the following beneficial effects: This application collects the power generation power of photovoltaic power generation at each moment in each cycle, the voltage data of photovoltaic power generation at the grid connection end, and the three-phase current data, collects the power data of each load end in the grid at each moment, and collects the voltage and impedance of each line in the grid at each moment; by collecting various types of grid-related data, it avoids the blind areas of traditional single data sources and improves the reliability of grid data analysis; analyzes the similarity degree between the voltage data distribution of photovoltaic power generation at the grid connection end in each cycle and the sine signal to obtain the sine similarity coefficient of each cycle; the determination of the sine similarity coefficient reflects the voltage harmonic content, reduces the interference to sensitive equipment, and improves the accuracy of power quality assessment; based on the fluctuation difference between any two phases in the three-phase current data of each cycle, determines the waveform difference index of the three-phase current in each cycle; the waveform difference index quantifies the three-phase current imbalance and accurately reflects the power quality of each cycle, providing a basis for subsequent load imbalance compensation; uses the similarity between the phase A current and the phase B current, and the phase B current and the phase C current in the three-phase current data of each cycle at different time delays, combines the sine similarity coefficient and the waveform difference index to determine the grid connection influence coefficient of the photovoltaic power in each cycle; the grid connection influence coefficient reflects the influence degree of the photovoltaic power grid connection in each cycle, identifies the correlation between photovoltaic fluctuations and grid oscillations, and improves the accuracy of droop coefficient correction in the droop control algorithm; records the sum of the powers of all load ends at each moment as the load power, analyzes the correlation between the power generation power and the load power in each cycle, and determines the power supply consistency coefficient of each cycle; based on the data trend difference and peak difference between the power generation power and the load power in each cycle, combines the power supply consistency coefficient to determine the supply-demand matching coefficient of each cycle; the supply-demand matching coefficient reflects the matching degree between the photovoltaic power generation power and the total load power in each cycle, reflects whether the photovoltaic power can better adapt to the grid load demand, and helps to optimize the droop coefficient in the droop control algorithm; obtains the voltage mean value and impedance mean value of all moments of each line in each cycle, analyzes the difference change between the voltage mean value and the impedance mean value of each line, and the dispersion degree of the voltage mean value and the impedance mean value, and determines the impedance fluctuation coefficient of each cycle; the impedance fluctuation coefficient reflects the influence degree of the grid line impedance on the voltage in the line and the difference degree between the line impedances, and improves the accuracy and reliability of the droop coefficient adjustment in the droop control algorithm; comprehensively combines the grid connection influence coefficient, the supply-demand matching coefficient, and the impedance fluctuation coefficient to optimize the droop coefficient in the droop control algorithm, uses the optimized droop control algorithm to compensate for the load imbalance of the power system, and realizes precise balance control of the load in the power system by dynamically adjusting the load distribution of the power system, thereby improving the grid stability. Description of the Drawings

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of the steps of a method for compensating load imbalance in a power system for photovoltaic power grid connection provided by the present application; Figure 2 It is a flowchart for regulating the droop coefficient in the droop control algorithm. Specific embodiments

[0017] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of a method for compensating load imbalance in a power system for photovoltaic power grid connection proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0019] The following specifically describes the specific solution of a method for compensating load imbalance in a power system for photovoltaic power grid connection provided by the present application in combination with the accompanying drawings.

[0020] A method for compensating load imbalance in a power system for photovoltaic power grid connection provided by an embodiment of the present application. Specifically, a method for compensating load imbalance in a power system for photovoltaic power grid connection is provided as follows. Please refer to Figure 1 This method includes the following steps: Step S001: Collect the power generation power of photovoltaic power generation at each moment in each cycle, the voltage data of photovoltaic power generation at the grid connection end, and the three-phase current data, collect the power data of each load end in the grid at each moment, and collect the voltage and impedance of each line in the grid at each moment.

[0021] In this embodiment, a power analyzer installed at the output end of the photovoltaic array is used to collect the power generation power of photovoltaic power generation in real time; a watt-hour meter is used to collect the voltage data and three-phase current data of photovoltaic power generation at the grid connection end, and a watt-hour meter is used to collect the power data of each load end in the grid. The sum of the collected power of all load ends at the same moment is used as the total load power of the grid, which is briefly recorded as the load power. The time intervals for collecting the power generation power, voltage, three-phase current, and power data of the load end are all set to 2 milliseconds, and the data collection period is set to 10 seconds. Among them, the implementer can set the time interval for collecting data and the collection period according to the actual situation, and this embodiment does not limit it here.

[0022] According to the time sequence of data collection, the power generation powers at all moments in each period are formed into a power supply sequence, the voltage data of photovoltaic power generation at the grid connection end at all moments in each period are formed into a voltage sequence, the phase A current in the three-phase current at all moments in each period is formed into a phase A sequence, the phase B current is formed into a phase B sequence, and the phase C current is formed into a phase C sequence.

[0023] Furthermore, the voltage of each line in the grid is obtained through a watt-hour meter, and the impedance of each line in the grid is obtained through an impedance analyzer. The voltage mean value and impedance mean value at all moments of each line in each period are calculated and recorded as the average voltage and average impedance of each line in each period.

[0024] In order to eliminate the influence of the dimension between data, all the above sequences, as well as the average voltage and average impedance, are respectively normalized. This embodiment adopts the maximum-minimum normalization method, and the implementer can choose other existing feasible normalization methods by himself / herself. This embodiment does not limit it here.

[0025] Step S002: Analyze the similarity degree between the voltage data distribution of photovoltaic power generation at the grid connection end in each period and the sine signal to obtain the sine similarity coefficient of each period; based on the fluctuation difference between any two phases in the three-phase current data of each period, determine the waveform difference index of the three-phase current in each period.

[0026] The output power of photovoltaic power generation is greatly affected by the light intensity and has significant volatility and uncertainty. When photovoltaic power is incorporated into the grid, it will produce harmonic effects. If the harmonic content is relatively high, it will have a greater impact on the stable operation of the grid, resulting in voltage fluctuations or flicker phenomena in the grid. At the same time, the load at the grid end itself also has strong randomness. If the harmonic content generated by the photovoltaic power generation system is relatively high and does not match the change in the load demand at the grid end, it will exacerbate the load difference in the grid, and may further cause the problem of load imbalance.

[0027] Under normal circumstances, the voltage and three-phase current waveforms in the power grid conform to the sine function. If the harmonic content brought by photovoltaic power is relatively large, it will have a serious impact on the power quality of the power grid, resulting in distortion of the voltage and current waveforms. Therefore, in this embodiment, by analyzing the waveforms of the voltage and the changes in the three-phase current waveforms, the degree of adverse impact of photovoltaic power grid connection on the power grid is evaluated.

[0028] Perform a fast Fourier transform on the voltage sequences of each cycle to convert the time-domain signal into a frequency-domain signal; construct a standard sine signal corresponding to the voltage sequence according to the fundamental frequency, phase, and amplitude of the frequency-domain voltage signal. Among them, the fast Fourier transform and signal generation technology are both well-known existing technologies, and this embodiment will not elaborate on them in detail here.

[0029] Calculate the Pearson correlation coefficient between the voltage sequences of each cycle and their corresponding standard sine signals as the sine similarity coefficient of each cycle. The sine similarity coefficient can reflect the waveform similarity degree between the voltage sequence and its corresponding standard sine signal. The larger the sine similarity coefficient, the more the waveform of the voltage sequence approximates a sine wave.

[0030] It should be noted that the Pearson correlation coefficient is a well-known existing technology, and implementers can choose other existing feasible calculation methods for the correlation coefficient by themselves, such as the Spearman similarity coefficient, cosine similarity, etc.

[0031] Furthermore, in a normal AC power system, when the three-phase loads are symmetric and balanced, the phase differences between the three-phase currents are consistent, and the waveform amplitudes are the same. However, due to the instability of photovoltaic power, the power provided at different times may be inconsistent, which may exacerbate the imbalance degree of the three-phase loads in the power grid. At the same time, since the connection of photovoltaic power to the grid will also bring a certain degree of harmonic pollution, it will further cause fluctuations in the waveforms and phase differences between the three-phase currents, resulting in inconsistent phenomena.

[0032] Considering the influence of the phase differences of the three-phase currents, the waveform changes of the three-phase currents have a certain chronological order in time. Therefore, in this embodiment, the DTW distances between any two sequences in the A-phase sequence, B-phase sequence, and C-phase sequence of each cycle are calculated respectively, and the mean value of all the DTW distances of each cycle is obtained and recorded as the waveform difference index of each cycle. The waveform difference index can reflect the difference degree between the three-phase current waveforms.

[0033] It should be noted that this embodiment only provides a calculation method for measuring the metric distance between sequences, and implementers can choose other existing feasible calculation methods for the metric distance by themselves.

[0034] Step S003: Determine the grid connection influence coefficient of the photovoltaic power for each cycle by using the similarities between the phase A current and the phase B current, and between the phase B current and the phase C current in the three-phase current data of each cycle at different time delays, in combination with the sine similarity coefficient and the waveform difference index.

[0035] In this embodiment, all the peaks in the phase A sequence of each cycle are obtained through a peak detection algorithm. According to the order of each peak in the phase A sequence, a peak position sequence is constructed in ascending order, and a first-order difference sequence of the peak position sequence is obtained. The mean value of this first-order difference sequence is denoted as the cycle interval index of the phase A sequence of each cycle. The cycle interval index can reflect how long it takes for the phase A current to reach the peak again. Among them, the peak detection algorithm in this embodiment adopts an extreme value detection algorithm, which is a well-known existing technology. Implementers can choose other existing feasible peak detection algorithms by themselves, and this embodiment does not limit this.

[0036] For each cycle, the cross-correlation coefficient between the phase A sequence and the phase B sequence at the time delay k is obtained successively through the cross-correlation function, where , a is the cycle interval index of the phase A sequence of each cycle, and k is incremented by 1 successively. The time delay k corresponding to the maximum value among all the cross-correlation coefficients is used as the AB phase difference between the phase A sequence and the phase B sequence , denoted as the first similarity. The AB phase difference reflects the delay time of the phase B current relative to the phase A current in time.

[0037] For the phase B sequence of each cycle, the same calculation method as the cycle interval index of the phase A sequence is adopted to obtain the cycle interval index of the phase B sequence. Similarly, using the cycle interval index of the phase B sequence, the same calculation method as the AB phase difference is adopted to obtain the BC phase difference between the phase B sequence and the phase C sequence , denoted as the second similarity.

[0038] Determine the grid connection influence coefficient of the photovoltaic power for each cycle by combining the sine similarity coefficient, the waveform difference index, and the difference between the first similarity and the second similarity. The specific calculation method is as follows: ; where A is the grid connection influence coefficient of the photovoltaic power for each cycle; B is the waveform difference index of each cycle; C is the absolute value of the difference between the AB phase difference and the BC phase difference , denoted as the first difference; P is the sine similarity coefficient of each cycle; is a preset value greater than 0. To avoid the denominator being 0, in this embodiment , it can be set by the implementer according to the actual situation, and this embodiment does not limit it here.

[0039] It should be noted that the difference represents the degree of difference between two variables. Specifically, it can be calculated by means of the absolute value of the difference, the square of the difference, the ratio, etc. The grid connection impact index can reflect the impact on the power grid when photovoltaic power is incorporated into the grid. If the waveform difference index is larger, it reflects that the waveforms of the three-phase currents are less similar; if the first difference is larger, it reflects that the phase difference between the three-phase currents is larger; if the sine similarity coefficient is smaller, it reflects that the difference between the voltage waveform and the sine waveform is larger. Therefore, if the grid connection impact coefficient is larger, it reflects that the adverse impact on the power grid caused by the grid connection of photovoltaic power is greater. At this time, a greater adjustment ability is required to adjust the impact brought by the grid connection of photovoltaic power.

[0040] Step S004, analyze the correlation between the generated power and the load power in each period, and determine the power supply consistency coefficient in each period; based on the data trend difference and peak difference between the generated power and the load power in each period, and combined with the power supply consistency coefficient, determine the supply-demand matching coefficient in each period.

[0041] Furthermore, since most loads are single-phase loads, and the electricity consumption nature and electricity consumption time of the loads are both relatively random, if the supply of photovoltaic power is relatively consistent with the grid demand load, the operation of the power grid is relatively stable; if the power supply of photovoltaic power fluctuates greatly from the demand load, and there is no corresponding change relationship, it may occur that at a certain moment, the power supply of photovoltaic power is significantly higher or lower than the demand load of a certain phase, resulting in a large difference in the demand load between this phase and other phases, further exacerbating the load imbalance phenomenon of the power grid.

[0042] Therefore, based on the above analysis, in this embodiment, the total load power at all moments in each period is formed into a load sequence in chronological order, and the Pearson correlation coefficient between the power supply sequence and the load sequence in each period is calculated as the power supply consistency coefficient in each period; the power supply consistency coefficient can reflect the degree of coordination between the photovoltaic supply power and the grid demand load. The smaller the power supply consistency coefficient, the lower the matching degree between the photovoltaic power supply and the demand load. When the power supply consistency coefficient is negative, it means that the grid connection of photovoltaic power not only cannot alleviate the problem of grid load imbalance, but also the possibility of further exacerbating the load imbalance is greater.

[0043] All the peaks in the power supply sequences of each period are obtained through the extreme value detection algorithm, sorted from small to large according to the order of the peaks in the power supply sequence to form a peak sequence, and the first-order difference sequence of the peak sequence is obtained, which is denoted as the peak difference sequence of the power supply sequence. The peak difference sequence can reflect the change amount of the photovoltaic power supply fluctuation; if the element value in the peak difference sequence is larger, it reflects that the power supply of the photovoltaic is in the process of gradually increasing. For the load sequence, the same acquisition method as the peak difference sequence of the power supply sequence is used to obtain the peak difference sequence of the load sequence.

[0044] For each period, calculate the absolute value of the difference between the elements at the same position in the peak difference sequence of the power supply sequence and the peak difference sequence of the load sequence, which is denoted as the first absolute difference value, and take the mean value of all the first absolute difference values in each period as the peak difference index of each period. It should be noted that: if the lengths of the peak difference sequence of the power supply sequence and the peak difference sequence of the load sequence are inconsistent, 0 is padded at the end of the shorter sequence to make the lengths of the peak difference sequence of the power supply sequence and the peak difference sequence of the load sequence equal. The peak difference index can reflect the fluctuation difference between the power supply sequence and the load sequence. The larger the peak difference index, the greater the difference between the output power fluctuation of the photovoltaic power generation and the demand load fluctuation of the power grid, and the greater the possibility of supply-demand mismatch.

[0045] Furthermore, the power supply sequence and the load sequence are respectively linearly fitted through the linear fitting algorithm, and the absolute value of the difference between the slopes of the two fitted straight lines is denoted as the trend difference index of each period. The trend difference index can reflect whether the data change rates and change directions between the power supply sequence and the load sequence are consistent.

[0046] Combining the power supply consistency coefficient, the peak difference index, and the trend difference index of each period, determine the supply-demand matching coefficient of each period. The specific calculation method is as follows: ; where D is the supply-demand matching coefficient of each period; Y is the power supply consistency coefficient of each period. Adding 1 to the power supply consistency coefficient is to avoid affecting the positive or negative nature of the supply-demand matching coefficient when the power supply consistency coefficient is negative, thereby affecting the correlation analysis between data; G is the peak difference index of each period; H is the trend difference index of each period, is a preset value greater than 0, and the purpose is to avoid the denominator being 0. In this embodiment , which can be set by the implementer according to the actual situation and is not limited in this embodiment.

[0047] It should be understood that the supply-demand matching coefficient can comprehensively reflect the consistency of the change trends between the power supply sequence and the load sequence. The larger the supply-demand matching coefficient is, the better the matching degree between the photovoltaic power generation and the grid load is, the photovoltaic power can better adapt to the grid load demand, and the grid operation is relatively stable.

[0048] Step S005: Obtain the voltage mean value and impedance mean value of all moments of each line in each period, analyze the difference change between the voltage mean value and the impedance mean value of each line, as well as the dispersion degree of the voltage mean value and the impedance mean value, and determine the impedance fluctuation coefficient of each period.

[0049] In addition, the impedance of the power grid line will also affect the distribution of reactive power. When the impedance differences of different lines in the power grid are large, the path of reactive power flow will change, resulting in too high voltage in some areas and too low voltage in other areas, generating an imbalance phenomenon. To ensure the balance of the power grid load, in this embodiment, the output of reactive power is adjusted by droop control. If the line impedance differences are large, a larger droop coefficient is required to compensate for the voltage differences to ensure a more balanced distribution of reactive power, thereby maintaining the stability of the power grid. Therefore, in this embodiment, the line impedance difference and voltage difference between each line are used as the control adjustment factors of the droop coefficient in the droop control algorithm.

[0050] For each period, in this embodiment, the ratio of the average voltage to the average impedance of each line in the power grid is used as the impedance voltage ratio of each line, denoted as the second difference; calculate the dispersion degree of the impedance voltage ratios of all lines in the power grid, denoted as the impedance influence difference index of the power grid; the impedance influence difference index can reflect whether the degree of influence of impedance changes on voltage in the line is consistent. If the impedance influence difference index is smaller, it indicates that the influence of impedance on voltage in each line is more consistent, and the greater the influence of impedance changes on voltage is, the larger the droop coefficient is required for adjustment when there are differences in line impedance.

[0051] It should be noted that in this embodiment, the calculation method of the dispersion degree is calculated using variance, and the implementer can choose other existing feasible calculation methods of the dispersion degree by himself, such as standard deviation, coefficient of variation, etc.

[0052] Calculate the variance of the average impedance of all lines in each period of the power grid, denoted as the impedance dispersion index of the line; calculate the variance of the average voltage of all lines in each period of the power grid, denoted as the voltage dispersion index of the line. Denote the absolute value of the difference between the impedance dispersion index and the voltage dispersion index as the third difference. The third difference can reflect whether the fluctuation degrees of the impedance and voltage of the lines in the power grid are consistent. The smaller the third difference is, the more consistent the fluctuation is, thereby reflecting that the influence of impedance on voltage is greater.

[0053] Combining the impedance influence difference index, the impedance dispersion index, the voltage dispersion index, and the third difference of each period, determine the impedance fluctuation coefficient of each period. The specific calculation method is as follows: ; where, is the impedance fluctuation coefficient of each period in the power grid; is the impedance influence difference index of each period; is the impedance dispersion index of the line of each period; K is the third difference of each period, is a preset value greater than 0, and the purpose is to avoid the denominator being 0. In this embodiment, , and the implementer can set it according to the actual situation. This embodiment does not limit it here.

[0054] It should be understood that the impedance fluctuation coefficient can reflect the influence degree of the line impedance on the voltage in the line, as well as the difference degree between the impedances. If the impedance fluctuation coefficient is larger, it means that the influence of the impedance on the voltage is greater, and the difference between the line impedances is larger, and the possibility of power grid instability is higher. At this time, a larger droop coefficient is required to adjust the power of the power grid.

[0055] Step S006, combining the grid connection influence coefficient, the supply-demand matching coefficient, and the impedance fluctuation coefficient, optimize the droop coefficient in the droop control algorithm, and use the optimized droop control algorithm to compensate for the load imbalance of the power system.

[0056] Combining the grid connection influence coefficient of the photovoltaic power of each period, the supply-demand matching coefficient of each period, and the impedance fluctuation coefficient of each period, construct the droop stability factor of each period. The specific calculation method is as follows: ; where, L is the droop stability factor of each period, A is the grid connection influence coefficient of the photovoltaic power of each period, D is the supply-demand matching coefficient of each period, is the impedance fluctuation coefficient of each period in the power grid, is a preset value greater than 0, and its function is to avoid the denominator being 0. In this embodiment, , and the implementer can set it according to the actual situation. This embodiment does not limit it here.

[0057] It should be understood that the droop stability factor can reflect whether the impact of photovoltaic power grid connection on the power system is stable in each cycle. If the grid connection impact coefficient A and the impedance fluctuation coefficient F are larger, it indicates that the difference between the line impedances in the power grid at the current time is larger, and the adverse impact on the power grid after photovoltaic power grid connection is greater. If the supply-demand matching coefficient D is smaller, it reflects that the matching degree between the power supply of photovoltaic power and the demand load of the power grid is lower. Therefore, if the droop stability factor is smaller, a larger droop coefficient is required to adjust the load of the power grid to balance the load between phases in a timely manner and compensate the load of the power system.

[0058] Furthermore, in the droop control algorithm of reactive power, the goal is to adjust the output reactive power to compensate for the voltage fluctuation of the power grid. In this embodiment, the droop coefficient in the droop control algorithm is adjusted in real time. Specifically: , where R is the droop coefficient in the droop control algorithm of the current cycle, U is the droop coefficient in the droop control algorithm of the previous cycle of the current cycle, L is the droop stability factor of the current cycle, norm() is a normalization function, and r is a preset constant greater than 0 and less than 1, whose function is to avoid the problem that the droop coefficient is 0 or too small, resulting in the power grid being unable to respond to the load change in a timely manner and causing uneven load distribution. In this embodiment, r = 0.5, and the implementer can set it according to the actual situation. This embodiment does not limit it here. The flowchart of the droop coefficient regulation in the droop control algorithm is as Figure 2 shown.

[0059] It should be noted that in this embodiment, the initial droop coefficient is set to 0.4, and the implementer can set it by himself; by adjusting the droop coefficient in the droop control algorithm in real time, the adjusted droop control algorithm is used to control the output of the corresponding reactive power to compensate for the voltage fluctuation, and through the adjustment of the voltage, the unbalanced compensation of the load of the power system is realized, making the operation of the power grid more stable. Among them, the droop control algorithm is a well-known existing technology, and this embodiment does not elaborate on it in detail here.

[0060] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0061] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0062] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; modifying the technical solutions described in the foregoing embodiments, or equivalently replacing some of the technical features therein, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and all should be included within the protection scope of the present application.

Claims

1. A method for compensating the load imbalance of a power system for grid connection of photovoltaic power, characterized in that, The method includes the following steps: Collect the power generation power of photovoltaic power generation at each moment in each cycle, the voltage data of photovoltaic power generation at the grid connection end, and the three-phase current data, collect the power data of each load end in the grid at each moment, and collect the voltage and impedance of each line in the grid at each moment; Analyze the similarity degree between the voltage data distribution of photovoltaic power generation at the grid connection end in each cycle and the sine signal to obtain the sine similarity coefficient of each cycle; based on the fluctuation difference between any two phases in the three-phase current data of each cycle, determine the waveform difference index of the three-phase current in each cycle; Record the similarity between the phase A current and the phase B current in each cycle at different time delays as the first similarity, record the similarity between the phase B current and the phase C current in each cycle at different time delays as the second similarity, analyze the difference between the first similarity and the second similarity, record it as the first difference, and combine the sine similarity coefficient and the waveform difference index to determine the grid connection influence coefficient of photovoltaic power in each cycle; Record the sum of the powers of all load ends at each moment as the load power, analyze the correlation between the power generation power and the load power in each cycle, and determine the power supply consistency coefficient of each cycle; Form the power supply sequence and the load sequence with the power generation power and the load power at all moments in each cycle respectively, obtain all the peaks in the power supply sequence and the load sequence respectively, take the mean value of the differences of the peaks with the same serial number in the power supply sequence and the load sequence as the peak difference index of each cycle; take the difference of the slopes of the fitting lines of the power supply sequence and the load sequence in each cycle as the trend difference index of each cycle, and combine the power supply consistency coefficient to determine the supply-demand matching coefficient of each cycle; Obtain the voltage mean value and the impedance mean value of each line at all moments in each cycle, record the difference between the voltage mean value and the impedance mean value of each line as the second difference, calculate the difference between the dispersion degree of the voltage mean values of all lines and the dispersion degree of the impedance mean values, record it as the third difference, and combine the dispersion degree of the impedance mean value to determine the impedance fluctuation coefficient of each cycle; Calculate the product of the grid connection influence coefficient and the impedance fluctuation coefficient of each cycle, calculate the sum value of the product and a preset value greater than 0, and take the ratio of the supply-demand matching coefficient of each cycle to the sum value as the droop stability factor of each cycle; adjust the droop coefficient in the droop control algorithm based on the droop stability factor of each cycle, and use the optimized droop control algorithm to compensate for the load imbalance of the power system.

2. The method for compensating load imbalance of a power system for photovoltaic power grid connection according to claim 1, characterized in that, The determination of the sine similarity coefficient includes: Form a voltage sequence with the voltage data of photovoltaic power generation at the grid connection end at all moments in each cycle, use the frequency domain transformation algorithm to obtain the frequency domain information of the voltage sequence, construct a standard sine signal of the voltage sequence, and the sine similarity coefficient is the correlation coefficient between the voltage sequence and the standard sine signal.

3. A method for compensating load imbalance in a power system for photovoltaic power grid connection as claimed in claim 1, characterized in that, The determination process of the waveform difference index is: Form the phase A sequence, the phase B sequence, and the phase C sequence with the phase A current, the phase B current, and the phase C current at all moments in each cycle respectively, calculate the metric distance between any two sequences in the phase A sequence, the phase B sequence, and the phase C sequence, and the waveform difference index is the mean value of all the metric distances in each cycle.

4. A method for compensating load imbalance of a power system for photovoltaic power grid connection according to claim 1, characterized in that, The determination of the access influence coefficient includes: The access influence coefficient in each period is positively correlated with the waveform difference index and the first difference, and negatively correlated with the sine similarity coefficient.

5. A method for compensating load imbalance of a power system for photovoltaic power grid connection according to claim 1, characterized in that, The power supply consistency coefficient is the correlation coefficient between the power generation power and the load power at all times in each period.

6. The method for compensating the load imbalance of a power system for photovoltaic power grid connection according to claim 1, characterized in that, The determination of the supply-demand matching coefficient includes: The supply-demand matching coefficient in each period is positively correlated with the power supply consistency coefficient, and negatively correlated with the peak difference index and the trend difference index.

7. A method for compensating load imbalance of a power system for photovoltaic power grid connection according to claim 1, characterized in that, The determination of the impedance fluctuation coefficient includes: The impedance fluctuation coefficient is positively correlated with the dispersion degree of the impedance mean value of all lines, and negatively correlated with the dispersion degree of the third difference and the second difference of all lines.

8. The method for compensating load imbalance of a power system for grid connection of photovoltaic power as claimed in claim 7, wherein The second difference is the ratio of the voltage mean value to the impedance mean value of each line.

9. The method for compensating load imbalance of a power system for photovoltaic power grid connection according to claim 1, wherein, Adjusting the droop coefficient in the droop control algorithm based on the droop stability factor in each period includes: Calculating the addition result of the normalized value of the reciprocal of the droop stability factor in each period and a preset constant greater than 0 and less than 1. The droop coefficient in the droop control algorithm in each period is the product of the addition result in each period and the droop coefficient in the previous period of each period.

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