Power distribution area electric energy quality regulation and control method based on mobile module energy storage

Abstract

The invention relates to the technical field of electric energy quality regulation and control, in particular to a power distribution area electric energy quality regulation and control method based on mobile module energy storage, and the method comprises the steps: collecting the electric variables of each photovoltaic access node of a power distribution area at each moment during the sunshine period of each day; identifying each abrupt change point in the daily voltage time sequence data of each photovoltaic access node, and determining a daily voltage random fluctuation factor of each photovoltaic access node; identifying each out-of-limit abrupt change point in the voltage time sequence data by using the numerical value interval of the standard voltage; obtaining a voltage random out-of-limit parameter of each photovoltaic access node every day; using the voltage random out-of-limit parameter to correct the order of sparse polynomial chaos expansion on the electrical variable when the probabilistic load flow calculation algorithm is used to obtain the voltage distribution at each photovoltaic access node; and voltage distribution at each photovoltaic access node is evaluated, and electric energy quality regulation and control are performed on the photovoltaic access nodes in advance through the mobile energy storage module. According to the invention, the power quality regulation and control precision of the power distribution area is improved.

Description

Technical Field

[0001] This application relates to the field of power quality control technology, specifically to a power quality control method for distribution substations based on mobile modular energy storage. Background Technology

[0002] Mobile modular energy storage is an important component of smart grid emergency management, characterized by rapid deployment and flexible allocation. To ensure the charging and discharging stability of distribution transformer areas, control strategies and algorithms are typically used to monitor and regulate the power quality of distribution transformer areas. This enables mobile modular energy storage to respond promptly and safely to abnormal changes in power quality in distribution transformer areas, thereby improving the efficiency of emergency control in distribution transformer areas and the stability of grid operation.

[0003] Especially in remote application scenarios, due to the small and dispersed load size of distribution substations, the large-scale access of photovoltaic energy to distribution substations brings uncertainties to the power grid system. For example, there may be repeated cloud cover at different times, resulting in a high degree of random variation in photovoltaic power generation, which can easily lead to the risk of voltage exceeding the limit. Power flow calculation is a commonly used algorithm for solving power quality control strategies, and it is usually combined with sparse polynomial chaotic expansion to quantify the uncertain variables. However, when performing sparse polynomial chaotic expansion, a fixed order is often used, which results in poor ability to capture the characteristics of abnormal voltage fluctuations when representing uncertain electrical variables. This interferes with the accuracy of the voltage distribution output of power flow calculation, thereby reducing the power quality control effect of distribution substations. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a power quality control method for distribution substations based on mobile modular energy storage, thereby resolving the existing issues.

[0005] The power quality control method for distribution substations based on mobile modular energy storage in this application adopts the following technical solution:

[0006] One embodiment of this application provides a power quality control method for a distribution substation based on mobile modular energy storage, the method comprising the following steps:

[0007] The electrical variables of each photovoltaic access node in the distribution area are collected at different times during the daily sunshine period, including voltage.

[0008] Identify the abrupt changes in the daily voltage time-series data of each photovoltaic access node, analyze the density and randomness of the abrupt changes, and determine the daily voltage random fluctuation factor of each photovoltaic access node.

[0009] The voltage time series data is used to identify each limit-breaking abrupt change point in the standard voltage range; the voltage random limit-breaking parameters of each photovoltaic access node are obtained by using the limit-breaking duration corresponding to the limit-breaking abrupt change point, the proportion of the limit-breaking abrupt change point in all abrupt change points, and the voltage random fluctuation factor.

[0010] When using the voltage random over-limit parameter correction to obtain the voltage distribution at each photovoltaic access node using the probabilistic power flow calculation algorithm, the order of the sparse polynomial chaotic expansion of the electrical variables is adjusted.

[0011] Assess the voltage distribution at each photovoltaic access node and adjust its power quality in advance using mobile energy storage modules.

[0012] In one embodiment, determining the daily voltage random fluctuation factor for each photovoltaic access node includes:

[0013] For each photovoltaic access node, the proportion of the sum of time intervals between all adjacent abrupt changes in the daily voltage time series data to the total daily voltage time series data collection time is determined and denoted as the first proportion. The ratio of the total number of abrupt changes in the daily voltage time series data to the first proportion is determined and denoted as the abrupt change frequency.

[0014] By integrating the dispersion of daily voltage time-series data, the dispersion of time intervals between all adjacent abrupt change points in the daily voltage time-series data, and the frequency of the abrupt change, the daily voltage random fluctuation factor of each photovoltaic access node is determined.

[0015] In one embodiment, the daily voltage random fluctuation factor of each photovoltaic access node is a weighted sum of the dispersion of the daily voltage time series data, the dispersion of the time interval between all adjacent mutation points in the daily voltage time series data, and the frequency of the mutation.

[0016] In one embodiment, the weight of the frequency of mutations during weighted summation is greater than the weight of the dispersion of the time interval between all adjacent mutation points in the daily voltage time series data, which is greater than the weight of the dispersion of the daily voltage time series data.

[0017] In one embodiment, the out-of-limit abrupt change point is voltage data that is not distributed within the numerical range of the standard voltage.

[0018] In one embodiment, the process of obtaining the duration of the exceedance corresponding to the exceedance mutation point is as follows:

[0019] For any out-of-limit abrupt change point, the time between the out-of-limit abrupt change point and the most recent voltage data within the standard voltage range is counted, which is taken as the out-of-limit time of the out-of-limit abrupt change point.

[0020] In one embodiment, obtaining the daily random voltage exceedance parameters of each photovoltaic access node includes:

[0021] The proportion of the number of limit-breaking mutation points in the daily voltage time-series data of each photovoltaic access node to the total number of mutation points is denoted as the second proportion. The proportion of the sum of the limit-breaking durations of all limit-breaking mutation points in the daily voltage time-series data of each photovoltaic access node to the total daily voltage time-series data collection time is denoted as the third proportion.

[0022] By combining the voltage random fluctuation factor, the second proportion, and the third proportion, the daily voltage random over-limit parameters of each photovoltaic access node are obtained, wherein the voltage random over-limit parameters are positively correlated with the voltage random fluctuation factor, the second proportion, and the third proportion.

[0023] In one embodiment, the voltage random over-limit parameter is a weighted sum of the voltage random fluctuation factor, the second proportion, and the third proportion.

[0024] In one embodiment, the correction value for the order of the sparse polynomial chaotic expansion of the electrical variable is calculated as follows:

[0025] In the formula, The correction value for the order of the sparse polynomial chaotic expansion of the electrical variable. This is the normalized result of the mean of the random voltage limit exceedance parameters of all photovoltaic access nodes each day. , These are the preset maximum and minimum values ​​for the order of the sparse polynomial chaotic expansion, respectively. This indicates rounding up to the nearest integer.

[0026] In one embodiment, the advance power quality regulation via a mobile energy storage module includes:

[0027] By predicting potential voltage exceedances at each photovoltaic (PV) access node based on the voltage distribution at each PV access node, the voltage of the corresponding PV access node can be adjusted in advance.

[0028] This application has at least the following beneficial effects:

[0029] This application collects voltage time-series data from each photovoltaic (PV) access node in a distribution substation and identifies abrupt changes, effectively improving the accuracy of voltage fluctuation identification. The analysis of abrupt changes allows for accurate capture of voltage fluctuation trends, providing a more precise basis for subsequent power quality control. By analyzing the density and randomness of abrupt changes in the voltage time-series data of each PV access node, a voltage random fluctuation factor is determined, providing a more quantitative understanding of the randomness of voltage fluctuations. This helps identify potential factors affecting voltage fluctuations, further providing data support for refined power quality control management and enhancing the targeted nature of power quality control in the distribution substation. Furthermore, by identifying and analyzing the proportion and duration of out-of-limit abrupt changes, the application reflects the degree and randomness of abrupt changes in the voltage time-series data of the PV access nodes, and adjusts the sparse and dense... The order of the term expansion can model the stochasticity of voltage fluctuations, taking into account the influence of uncertainties, thus making the power flow calculation results more accurate. The corrected voltage distribution can more realistically reflect the power quality at the photovoltaic access point, especially under conditions of large voltage fluctuations, providing more stable and reliable voltage values. This provides more reliable data support for subsequent power quality regulation, enabling reasonable power allocation during grid dispatching, avoiding the impact of voltage abrupt changes and limit violations on grid operation, thereby improving the predictive ability of power quality regulation. It helps to achieve accurate prediction and control of voltage fluctuations and limit violations, thus improving the intelligence level of power quality management. It can quickly respond to changes in voltage fluctuations based on real-time grid data, improving the adaptability to emergencies and ensuring the stable and efficient operation of the grid. Furthermore, the use of mobile energy storage modules for dynamic power quality regulation significantly improves the dispatching and operation efficiency of the grid. The regulating role of the energy storage modules can balance the occurrence of voltage fluctuations and abrupt changes, reduce the volatility of power supply, effectively reduce the complexity of grid dispatching, make the grid operation more stable, enhance the reliability of power supply, and help improve the overall efficiency of the power system. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart illustrating the steps of the power quality control method for distribution substations based on mobile modular energy storage provided in this application. Detailed Implementation

[0032] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the power quality control method for distribution substations based on mobile modular energy storage proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0034] The following description, in conjunction with the accompanying drawings, details the specific scheme of the power quality control method for distribution substations based on mobile modular energy storage provided in this application.

[0035] This application provides an embodiment of a power quality control method for distribution substations based on mobile modular energy storage. Specifically, the method is described below. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0036] Step S001: Collect electrical variables at each photovoltaic access node in the distribution area at each time of day during the sunshine period, where the electrical variables include voltage.

[0037] Based on natural laws, the duration of sunlight each day is approximately 10 hours. Therefore, this embodiment utilizes smart meters to collect electrical variables at each photovoltaic access node in the distribution substation at various times during the daily sunlight period. The collection time interval for electrical variables is 1 second, and the total collection time per day is 10 hours. This embodiment selects the electrical variable data collected between 8:00 AM and 6:00 PM each day. The electrical variables in this embodiment include voltage, load, and power.

[0038] This embodiment uses the isnull() function of the Pandas library to check for missing values ​​in the daily electrical variable time series data, and uses linear interpolation to fill in the missing values. Linear interpolation is a well-known existing technology, and the specific process will not be described in detail.

[0039] Step S002: Identify each abrupt change point in the daily voltage time-series data of each photovoltaic access node, analyze the density and randomness of the abrupt change point distribution, and determine the daily voltage random fluctuation factor of each photovoltaic access node.

[0040] In remote areas, due to the changeable weather, frequent cloud cover may block sunlight, causing frequent voltage fluctuations at the photovoltaic input nodes of the distribution substation. This results in unstable photovoltaic input voltage and power quality problems such as voltage exceeding limits. Therefore, in order to ensure the stability of power quality in the distribution substation, it is necessary to fully explore the uncertainty of photovoltaic input voltage fluctuations to improve the accuracy of power quality control in the distribution substation.

[0041] Generally, the degree and duration of clouds frequently blocking sunlight are random. The density of clouds in the sky varies at different times, and the frequency of clouds blocking sunlight is also affected by wind speed. The frequent blocking of sunlight by clouds is highly random.

[0042] Specifically, taking the daily voltage time-series data of any photovoltaic input node in a distribution substation as an example, to analyze the fluctuation stability of the daily voltage time-series data of the photovoltaic input node in the distribution substation, the dispersion of the daily voltage time-series data is calculated to represent the degree of instability of the voltage time-series data under the influence of cloud cover. The dispersion can be calculated using methods such as variance, standard deviation, coefficient of variation, and entropy; this embodiment does not impose any restrictions on this. This embodiment calculates the coefficient of variation of the daily voltage time-series data to represent the degree of instability of the voltage time-series data under the influence of cloud cover.

[0043] Taking the example of clouds blocking sunlight on a sunny day, the light intensity decreases after the clouds begin to block the sunlight. This causes the input voltage of the photovoltaic input node in the distribution substation to decrease as the light intensity decreases. In other words, the voltage will undergo unstable and sudden changes with the change in light intensity. In order to obtain the abrupt change characteristics of the voltage time series data, the daily voltage time series data of the photovoltaic input node is used as input, and a mutation point detection algorithm is used to detect each mutation point in the voltage time series data. The mutation point detection algorithm can be the Pettitt algorithm, the MK algorithm, etc. This implementation does not impose any special restrictions. This embodiment adopts the MK algorithm, which is a well-known existing technology.

[0044] For the daily voltage time-series data of the photovoltaic input node, the time interval between adjacent abrupt changes is calculated. The smaller the time interval, the more frequent the cloud cover. The proportion of the sum of the time intervals between all adjacent abrupt changes in the daily voltage time-series data to the total daily voltage time-series data collection time is taken as the abrupt change interval proportion, denoted as the first proportion. Then, the ratio of the total number of abrupt changes in the daily voltage time-series data to the first proportion is denoted as the abrupt change frequency, which indicates the frequency of voltage abrupt changes under frequent cloud cover. The more abrupt changes there are and the smaller the abrupt change interval proportion, the greater the abrupt change frequency, indicating that there are many abrupt changes that are close in time, i.e., the voltage abrupt changes are relatively frequent.

[0045] To further measure the randomness of voltage mutations, it is necessary to further analyze the time intervals between mutation points. If the time intervals between mutation points are largely equal, there is a certain regularity, which may reduce the randomness of voltage fluctuations. In this embodiment, the coefficient of variation of the time intervals between all adjacent mutation points in the daily voltage time series data of the photovoltaic input node is calculated to represent the randomness of voltage time series data mutations affected by cloud cover. The larger the coefficient of variation value, the greater the randomness of the voltage mutation time and the higher the unpredictability.

[0046] Based on the above analysis, the daily voltage random fluctuation factor of each photovoltaic access node is calculated to characterize the degree of random fluctuation in the voltage time series data of the photovoltaic input nodes in the distribution substation due to cloud cover. The specific expression is as follows:

[0047] In the formula, This represents the daily random voltage fluctuation factor at each photovoltaic access node in the distribution substation area. This represents the coefficient of variation of the daily voltage time-series data for each photovoltaic access node in the distribution substation area. This indicates the frequency of daily voltage time-series data mutations at each photovoltaic access node in the distribution substation area. This represents the coefficient of variation of the time interval between all adjacent abrupt changes in the daily voltage time-series data of each photovoltaic input node in the distribution substation area. , , Both represent weights, and the magnitude of the weights depends on... , , The relative importance of voltage random fluctuation factors is determined, among which, , Based on the historical voltage timing data of the photovoltaic access node, this embodiment sets... , , .

[0048] It should be noted that if the number of abrupt changes detected in the daily voltage time-series data of the photovoltaic access node is less than 3, it indicates that the number of abrupt changes in the voltage time-series data is small and the irradiance is relatively stable. In order to ensure the reliability of the calculation of the voltage random fluctuation factor, this embodiment directly uses the coefficient of variation of the daily voltage time-series data of the photovoltaic access node as the daily voltage random fluctuation factor of the photovoltaic access node, without performing additional analysis on the distribution of its abrupt changes, that is, without calculating the coefficient of variation of the frequency of abrupt changes and the time interval between abrupt changes.

[0049] It should be understood that the coefficient of variation It reflects the degree of instability and fluctuation in voltage time series data, and the coefficient of variation. The larger the value, the greater the difference in voltage values ​​at different times, and the greater the randomness. This indicates the frequency of abrupt changes in voltage time series data, and also reflects the frequency of cloud cover obstructing sunlight. The more frequent the abrupt changes, the higher the coefficient of variation of the time interval between abrupt change points. The larger the value, the higher the randomness of the voltage time series data mutation. The larger the voltage random fluctuation factor, the greater the degree of random fluctuation of the voltage time series data of the photovoltaic input node in the distribution area due to cloud cover.

[0050] Step S003: Identify each limit-breaking abrupt change point in the voltage time series data using the numerical range of the standard voltage; obtain the daily voltage random limit-breaking parameters of each photovoltaic access node by using the limit-breaking duration corresponding to the limit-breaking abrupt change point, the proportion of the limit-breaking abrupt change point among all abrupt change points, and the voltage random fluctuation factor.

[0051] Even if the voltage timing data of the photovoltaic input node in the distribution substation experiences significant random fluctuations, the voltage fluctuations may still be within the normal range. Therefore, this embodiment also needs to analyze whether the voltage fluctuation amplitude exceeds the standard input voltage range. In this embodiment, the standard input voltage range is set to not exceed 10% of the standard voltage. Specifically, the standard input voltage in this embodiment is set to 380V, therefore the standard voltage range is... Voltage data whose voltage values ​​are not distributed within the standard voltage range in the daily voltage time-series data of photovoltaic access nodes are recorded as out-of-limit abrupt change points.

[0052] Since the start and end of cloud cover cause changes in sunlight intensity, resulting in two voltage abrupt changes, these abrupt changes correspond to voltage deviation from and recovery to the standard voltage. The duration between each out-of-limit abrupt change and the most recent voltage data point within the standard voltage range is recorded as the out-of-limit duration for each out-of-limit change. If there is no voltage data within the standard voltage range after an out-of-limit change, the out-of-limit duration of that out-of-limit change is represented by the average of the out-of-limit durations of all other out-of-limit change changes. A longer out-of-limit duration indicates that the voltage has been affected by clouds for a longer period, resulting in a longer voltage out-of-limit duration. It should be noted that if there is only one out-of-limit change in the daily voltage time-series data of the photovoltaic input node, and there is no voltage data within the standard voltage range after that out-of-limit change, then the out-of-limit duration of that out-of-limit change is the duration from that out-of-limit change to the end of the day's data collection.

[0053] The ratio of the number of voltage-limit-exceeding abrupt changes to the total number of abrupt changes in the daily voltage time-series data of each photovoltaic (PV) access node is calculated as the voltage-limit-exceeding abrupt change ratio, denoted as the second proportion. This ratio represents the percentage of abrupt changes related to voltage limits. A higher voltage-limit-exceeding abrupt change ratio indicates a higher likelihood that the voltage time-series data of the PV input nodes in the distribution substation is affected by cloud cover, leading to voltage abrupt changes and voltage limits exceeding the limit. All abrupt changes include voltage-limit-exceeding abrupt changes and abrupt changes detected using the abrupt change detection algorithm, with each abrupt change being counted only once.

[0054] Calculate the sum of the over-limit durations of all over-limit mutation points in the daily voltage time series data of each photovoltaic access node, and record it as the total over-limit duration. The ratio of the total over-limit duration to the total duration of the voltage time series data collected on the day is used as the over-limit duration ratio, which is recorded as the third proportion, indicating the duration of voltage over-limit.

[0055] Based on the above analysis, the random voltage limit exceedance parameter for each photovoltaic access node in the distribution transformer area is calculated daily. This parameter characterizes the random limit exceedance of the voltage time series data of the photovoltaic input nodes in the distribution transformer area due to cloud cover. The specific expression is as follows:

[0056] In the formula, This represents the daily random voltage exceedance parameters for each photovoltaic access node in the distribution substation area. This represents the percentage of time each photovoltaic access node's voltage time-series data exceeds the limit each day. This represents the percentage of voltage time-series data exceeding the limit for each photovoltaic access node per day. , , All represent weights. Based on the relative importance of the voltage random fluctuation factor, the over-limit duration ratio, and the over-limit mutation ratio to the voltage random over-limit parameters, this embodiment sets... , , Implementers can set the parameters themselves according to the actual situation.

[0057] It should be noted that when there are no over-limit abrupt changes in the daily voltage time-series data of the photovoltaic input node, the over-limit duration ratio and over-limit mutation ratio are not calculated. Instead, the calculation result of the voltage random fluctuation factor is directly used as the daily voltage random over-limit parameter for each photovoltaic access node in the distribution area.

[0058] It should be understood that the greater the random fluctuation, the higher the duration of voltage exceedances, and the higher the abrupt change ratio of voltage exceedances in the voltage time-series data of the photovoltaic input nodes in the distribution substation due to cloud cover, the higher the random exceedance of voltage time-series data under the influence of cloud cover. Therefore, the calculated random exceedance parameters of voltage will be higher. The larger the value, the better.

[0059] Step S004: When using the voltage random over-limit parameter to correct the order of the sparse polynomial chaotic expansion of the electrical variables at each photovoltaic access node using the probabilistic power flow calculation algorithm to obtain the voltage distribution, the voltage random over-limit parameter is used.

[0060] In remote areas, the weather is complex and changeable, and the timing and duration of cloud cover blocking sunlight are highly uncertain, leading to random variations in sunlight intensity and instability in photovoltaic power generation. This, in turn, results in a high degree of random exceedance of voltage time-series data at photovoltaic input nodes in distribution substations. Therefore, this application uses a distribution substation probabilistic power flow calculation method to optimize precise distribution substation scheduling parameters and uses mobile energy storage modules to promptly and efficiently solve power quality problems caused by voltage exceedance in distribution substations. However, when using sparse polynomial chaotic expansion to represent voltage distribution, its expansion order cannot be updated in real time according to the actual application scenario. Moreover, the chaotic expansion order directly affects the efficiency of optimized scheduling. An excessively large order reduces computational efficiency, while an excessively small order has poor ability to capture uncertain voltage fluctuation data characteristics, resulting in large deviations in power flow calculation results. The chaotic expansion order is directly related to the random exceedance of voltage time-series data; the higher the random exceedance, the higher the required chaotic expansion order is needed to accurately capture uncertain voltage fluctuation data characteristics.

[0061] Based on the above analysis, the chaotic expansion order is updated in real time according to the daily random voltage exceedance parameters of each photovoltaic access node in the distribution substation. The specific expression is as follows:

[0062] In the formula, The correction value for the order of the sparse polynomial chaotic expansion of the electrical variable. This is the normalized result of the mean of the random voltage exceedance parameters of all photovoltaic access nodes in the distribution substation area every day. , These are the preset maximum and minimum values ​​for the order of the sparse polynomial chaotic expansion, respectively. This indicates rounding up. This embodiment sets... , The normalization range can be determined through a conventional order search using sparse polynomial chaotic expansion, and the implementer can determine it according to the actual situation. It should be noted that the normalization method described in this embodiment is to obtain the mean value of the random voltage limit exceedance parameters of all photovoltaic access nodes in the previous N days, and normalize the mean value using Z-score standardization. In this embodiment, N=10, but the implementer can set it according to the actual situation. The implementer can choose other existing feasible normalization methods, and this embodiment does not impose any restrictions on this.

[0063] Step S005: Assess the voltage distribution at each photovoltaic access node and adjust its power quality in advance using a mobile energy storage module.

[0064] Furthermore, the network topology of the distribution substation, the load and power of each photovoltaic access node are used as inputs. The network topology serves as a deterministic parameter of the distribution substation, while the load and power of each photovoltaic access node are used as random input variables. Based on the probability distribution type of each random input variable, an orthogonal polynomial basis is selected to establish a surrogate model for the node voltage response. The surrogate model for the node voltage response takes the following form:

[0065]

[0066] In the formula, As a proxy model for node voltage response, Let be the expansion coefficients to be determined for the basis functions of the polynomial expansion, and P be the number of truncation terms. Let them be a vector of independent random variables. These are the basis functions for the polynomial expansion, possessing orthogonality. Among them, the correction value for the order of the chaotic expansion is... It is the highest degree of all monomials in the polynomial expansion basis functions, used to control the accuracy and computational complexity of the surrogate model.

[0067] Furthermore, based on the deterministic parameters of the input distribution network, the least squares method is used to solve for the undetermined expansion coefficients of the polynomial expansion basis functions, and a model for deterministic power flow calculation of the distribution network is established. Finally, the voltage distribution of each node is output through the model for deterministic power flow calculation of the distribution network, predicting the situation where each photovoltaic access node will experience voltage overruns in the future. Ultimately, the mobile energy storage module moves in advance to the photovoltaic access node where voltage overruns occur to adjust the voltage, thereby achieving regulation of the power quality of the distribution area. The least squares method and the establishment of the model for deterministic power flow calculation of the distribution network are both existing known technologies, and the specific process will not be elaborated further.

[0068] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0069] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0070] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A power quality control method for distribution substations based on mobile modular energy storage, characterized in that, The method includes the following steps: The electrical variables of each photovoltaic access node in the distribution area are collected at different times during the daily sunshine period, including voltage. Identify the abrupt changes in the daily voltage time-series data of each photovoltaic access node, analyze the density and randomness of the abrupt changes, and determine the daily voltage random fluctuation factor of each photovoltaic access node. The voltage time series data is used to identify each limit-breaking abrupt change point in the standard voltage range; the voltage random limit-breaking parameters of each photovoltaic access node are obtained by using the limit-breaking duration corresponding to the limit-breaking abrupt change point, the proportion of the limit-breaking abrupt change point in all abrupt change points, and the voltage random fluctuation factor. When using the voltage random over-limit parameter correction to obtain the voltage distribution at each photovoltaic access node using the probabilistic power flow calculation algorithm, the order of the sparse polynomial chaotic expansion of the electrical variables is used. Assess the voltage distribution at each photovoltaic access node and adjust its power quality in advance using mobile energy storage modules.

2. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 1, characterized in that, The determination of the daily voltage random fluctuation factor for each photovoltaic access node includes: For each photovoltaic access node, the proportion of the sum of time intervals between all adjacent abrupt changes in the daily voltage time series data to the total daily voltage time series data collection time is determined and denoted as the first proportion. The ratio of the total number of abrupt changes in the daily voltage time series data to the first proportion is determined and denoted as the abrupt change frequency. By integrating the dispersion of daily voltage time-series data, the dispersion of time intervals between all adjacent abrupt change points in the daily voltage time-series data, and the frequency of the abrupt change, the daily voltage random fluctuation factor of each photovoltaic access node is determined.

3. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 2, characterized in that, The daily voltage random fluctuation factor of each photovoltaic access node is a weighted sum of the dispersion of the daily voltage time series data, the dispersion of the time interval between all adjacent mutation points in the daily voltage time series data, and the frequency of mutation.

4. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 3, characterized in that, When performing a weighted summation, the weight of the frequency of mutations is greater than the weight of the dispersion of the time interval between all adjacent mutation points in the daily voltage time series data, which is greater than the weight of the dispersion of the daily voltage time series data.

5. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 1, characterized in that, The out-of-limit mutation point refers to voltage data that is not distributed within the numerical range of the standard voltage.

6. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 1, characterized in that, The process for obtaining the duration of the excess time corresponding to the excess mutation point is as follows: For any out-of-limit abrupt change point, the time between the out-of-limit abrupt change point and the most recent voltage data within the standard voltage range is counted, which is taken as the out-of-limit time of the out-of-limit abrupt change point.

7. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 6, characterized in that, The process of obtaining the daily random voltage exceedance parameters for each photovoltaic access node includes: The proportion of the number of limit-breaking mutation points in the daily voltage time-series data of each photovoltaic access node to the total number of mutation points is denoted as the second proportion. The proportion of the sum of the limit-breaking durations of all limit-breaking mutation points in the daily voltage time-series data of each photovoltaic access node to the total daily voltage time-series data collection time is denoted as the third proportion. By combining the voltage random fluctuation factor, the second proportion, and the third proportion, the daily voltage random over-limit parameters of each photovoltaic access node are obtained, wherein the voltage random over-limit parameters are positively correlated with the voltage random fluctuation factor, the second proportion, and the third proportion.

8. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 7, characterized in that, The voltage random over-limit parameter is the weighted sum of the voltage random fluctuation factor, the second proportion, and the third proportion.

9. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 1, characterized in that, The correction value for the order of the sparse polynomial chaotic expansion of the electrical variable is calculated as follows: In the formula, The correction value for the order of the sparse polynomial chaotic expansion of the electrical variable. This is the normalized result of the mean of the random voltage limit exceedance parameters of all photovoltaic access nodes each day. , These are the preset maximum and minimum values ​​for the order of the sparse polynomial chaotic expansion, respectively. This indicates rounding up to the nearest integer.

10. The power quality control method for distribution substations based on mobile modular energy storage as described in claim 1, characterized in that, The aforementioned advance power quality regulation via mobile energy storage modules includes: By predicting potential voltage exceedances at each photovoltaic access node based on the voltage distribution at each access node, the voltage of the corresponding photovoltaic access node can be adjusted in advance.

Images