Power distribution network comprehensive index system construction method based on dynamic characteristic evaluation

Abstract

The invention discloses a power distribution network comprehensive index system construction method based on dynamic characteristic evaluation, and relates to the technical field of power distribution network index system construction, and the method specifically comprises the following steps: obtaining the voltage fluctuation characteristic information of load equipment which is started and stopped at the same time in the starting and stopping process in real time, respectively generating a voltage fluctuation influence evaluation coefficient and a voltage fluctuation acceleration index of each load device; and constructing a voltage fluctuation influence evaluation model for the generated voltage fluctuation influence evaluation coefficient and the voltage fluctuation acceleration index of each load device, generating an influence evaluation coefficient of each load device, evaluating the influence degree of simultaneous start and stop of the multiple load devices on the voltage fluctuation stability, and dividing the influence degree into a low influence degree and a high influence degree. According to the method, the problem of voltage fluctuation evaluation distortion caused by synchronous start and stop of multiple load devices is solved, and the stability and regulation efficiency of the power grid are improved by accurately identifying the independent contribution of each device and optimizing the power grid regulation strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network indicator system construction, and in particular to a method for constructing a distribution network comprehensive indicator system based on dynamic characteristic evaluation. Background Art

[0002] The distribution network is a crucial component of the power system, primarily responsible for distributing electricity from the high-voltage transmission network to end users. It transmits and manages this energy through facilities such as transformers and distribution lines. Its operational efficiency and reliability are directly related to the quality of electricity users receive and the economic viability of the power grid. In recent years, with the rapid development of new energy technologies, particularly the widespread adoption of electric vehicle charging stations and mobile energy storage devices, the dynamic load characteristics of the distribution network have significantly increased. The centralized charging of electric vehicles can place high loads on the grid during specific time periods, while mobile energy storage devices can flexibly participate in the grid's energy dispatch, providing a new means for efficient power distribution. The integration of these new energy sources has not only promoted the intelligent upgrade of the distribution network but also increased the challenges of dynamic operational characteristics.

[0003] The comprehensive indicator system for distribution networks is a set of quantitative indicators used to comprehensively evaluate the performance, reliability, and operating status of distribution networks. It typically covers multiple dimensions, including power supply capacity, power quality, economy, and safety, and is a key tool for grid planning, operation, and optimization. However, with the increasing complexity of modern power grid load characteristics, dynamic characteristics such as the access of distributed power sources and load fluctuations have made the operating status of distribution networks more variable. Traditional static evaluation indicator systems are unable to accurately reflect the real-time status and potential risks of distribution networks. Therefore, constructing a comprehensive indicator system for distribution networks based on dynamic characteristic evaluation can capture the dynamic changes of distribution networks through real-time collection and analysis of operating data, providing a scientific basis for operation optimization, fault prediction, and risk control. This not only improves the applicability and flexibility of the indicator system, but also lays a technical foundation for the development of smart grids.

[0004] Existing technologies for constructing comprehensive distribution network indicator systems based on dynamic characteristics assessment typically involve the following steps: First, dynamic characteristic data is collected, including key operational data such as voltage, current, load fluctuation, and distributed energy output. Sensors and intelligent monitoring devices are then used to monitor the distribution network status in real time. Second, the collected data is dynamically analyzed and processed, applying time series analysis, frequency domain analysis, and machine learning algorithms to extract core variables that reflect the dynamic characteristics of the distribution network, such as voltage stability indicators and load responsiveness. Third, the analysis results are combined with the distribution network's operational objectives (such as reliability and economic efficiency) to construct an indicator system using a multi-objective optimization algorithm. Each indicator is assigned a weight to form a hierarchical structure, covering dimensions such as operational efficiency, safety, economic efficiency, and environmental adaptability. Finally, a verification mechanism is designed to evaluate and calibrate the indicator system using simulation models or actual operational data to ensure that it accurately reflects the operational characteristics of the distribution network under dynamic conditions. This entire process emphasizes real-time and flexibility, ensuring that the constructed comprehensive indicator system can provide reliable support for optimized scheduling and operational decision-making of the distribution network.

[0005] The existing technology has the following deficiencies:

[0006] In certain distribution network systems, when multiple high-power load devices (such as electric vehicle charging stations and mobile energy storage devices) start and stop almost simultaneously within extremely short time intervals, superimposed voltage fluctuations are generated. Because these devices start and stop at similar times and have similar power, existing dynamic characteristics evaluation index systems are unable to accurately identify the independent contribution of each device to voltage fluctuations, resulting in evaluation results that fail to truly reflect the combined impact of these devices on voltage fluctuation stability. This is primarily due to the evaluation technology's lack of ability to decouple and separate the fluctuations of synchronously started and stopped devices. As a result, when the start and stop times of devices overlap, the independent contributions of each device to voltage fluctuations are ignored or confused. Existing technologies are unable to assess the impact of the simultaneous start and stop of multiple load devices on voltage fluctuation stability, which may lead to evaluation results that underestimate the actual severity of the fluctuations, misleading grid control strategies, resulting in insufficient or delayed control measures, and ultimately the risk of exacerbated voltage fluctuations. Furthermore, because the extent of the impact of multiple load devices on voltage fluctuations cannot be accurately identified and classified, existing technologies cannot effectively quantify the specific role of each load device in the fluctuation process when constructing comprehensive distribution network index systems, resulting in distorted evaluation results from the comprehensive index systems. This distortion may make the comprehensive indicator system unable to fully reflect the dynamic characteristics and stability features of distribution network operation, thereby limiting its actual application effect in optimizing grid control strategies and improving operation efficiency.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0008] The purpose of the present invention is to provide a method for constructing a comprehensive indicator system for a distribution network based on dynamic characteristics evaluation, so as to solve the problems in the above-mentioned background technology.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a method for constructing a comprehensive indicator system for a distribution network based on dynamic characteristics evaluation, specifically comprising the following steps:

[0010] During the operation of the distribution network, all load devices in the distribution network are detected in real time. When multiple load devices are detected to be started or stopped at the same time, all load devices that are started or stopped at the same time are identified and marked.

[0011] Real-time acquisition of voltage fluctuation characteristic information of each load device during the start-up and shutdown process, and analysis after acquisition to generate the voltage fluctuation impact assessment coefficient and voltage fluctuation acceleration index for each load device;

[0012] A voltage fluctuation impact assessment model is constructed based on the generated voltage fluctuation impact assessment coefficients and voltage fluctuation acceleration indices for each load device. The impact assessment coefficients for each load device are generated and analyzed after generation to assess the impact of simultaneous start-up and shutdown of multiple load devices on voltage fluctuation stability, categorizing the impact into low impact and high impact.

[0013] If the assessment result is a high impact, the preset impact assessment coefficient threshold interval is determined, and after determination, it is compared with the generated impact assessment coefficient of each load device. Based on the comparison result, each load device is divided into major impact devices, minor impact devices and normal devices;

[0014] According to the classification results of each load equipment, corresponding measures are taken for the main impact equipment, secondary impact equipment and normal equipment respectively;

[0015] The impact assessment coefficients, equipment classification results and assessment conclusions of each load device generated are dynamically incorporated into the comprehensive indicator system of the distribution network, and a comprehensive indicator database covering voltage fluctuation characteristics, load equipment operating status and control strategy response is established. By dynamically optimizing the parameter weights of the evaluation model, the ability and stability of the distribution network comprehensive indicator system to reflect operating characteristics are continuously improved.

[0016] Preferably, the voltage fluctuation characteristic information of each load device that is started and stopped simultaneously during the start-up and shutdown process is obtained in real time, and analyzed after acquisition to generate a voltage fluctuation impact assessment coefficient and a voltage fluctuation acceleration index for each load device, specifically including the following steps:

[0017] Real-time acquisition of voltage fluctuation characteristic information of each load device that starts and stops simultaneously during the start-up and shutdown process, and pre-processing after acquisition;

[0018] Extracting voltage fluctuation change information and load power variation information from the pre-processed voltage fluctuation characteristic information of each load device;

[0019] The extracted voltage fluctuation change information and load power change information are analyzed to generate the voltage fluctuation impact assessment coefficient and voltage fluctuation acceleration index of each load device respectively.

[0020] Preferably, the logic for obtaining the voltage fluctuation impact assessment coefficient of each load device is as follows:

[0021] The voltage fluctuation change information of each load device is extracted from the pre-processed voltage fluctuation characteristic information, including the instantaneous voltage change value, actual power, start and stop time of each load device at different times during the start and stop process, and the total start and stop time of all load devices at the same time, and calibrated as QT i and ZQT, It represents the instantaneous voltage change value of the i-th load device at time m during the start-stop process. It represents the actual power of the i-th load device at time m during the start-stop process, QT i represents the duration of the start and stop of the i-th load device, ZQT represents the total duration of the simultaneous start and stop of all load devices, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0022] Calculate the voltage fluctuation impact assessment coefficient of each load device. The specific calculation formula is as follows:

[0023]

[0024] Where CMP i is the voltage fluctuation impact assessment coefficient of the i-th load equipment.

[0025] Preferably, the logic for obtaining the voltage fluctuation acceleration index of each load device is as follows:

[0026] Extract the load power change information from the pre-processed voltage fluctuation characteristic information of each load device, including the voltage value of each load device at different times during the start-up and shutdown process, the absolute value of the actual power change, and the voltage fluctuation amplitude, and use the function U i (t), ΔPB i (t) and ΔUB i (t) is used to represent the time point, U i(t) represents the voltage value of the i-th load device at time t during the start-stop process, ΔPB i (t) represents the absolute value of the actual power change of the i-th load device at time t during the start-up and shutdown process, ΔUB i (t) represents the voltage fluctuation amplitude of the i-th load device at time t during the start-up and shutdown process. The time period is defined as [t1, t2], i = 1, 2, 3, ..., k, where k is a positive integer;

[0027] The voltage fluctuation amplitude of each load device at different times during the start-up and shutdown process is constructed into a set, and the maximum value in the set is calibrated as

[0028] Calculate the voltage fluctuation acceleration index of each load device. The specific calculation formula is as follows:

[0029]

[0030] Where CAL i is the voltage fluctuation acceleration index of the i-th load device.

[0031] Preferably, the voltage fluctuation impact assessment coefficient CMP of each load device generated i and voltage fluctuation acceleration index CAL i Construct a voltage fluctuation impact assessment model and generate the impact assessment coefficient IEC of each load device through weighted summation i , the specific calculation formula is as follows:

[0032] IEC i =ω1*CMP i +ω2*CAL i

[0033] Where ω1 and ω2 are the voltage fluctuation impact assessment coefficients CMP of each load device respectively. i and voltage fluctuation acceleration index CAL i The non-zero weight coefficient of , and ω1+ω2=1;

[0034] The impact assessment coefficient IEC of each load device i Conduct comprehensive analysis and generate comprehensive evaluation coefficient CEC according to the formula:

[0035] Preferably, the generated comprehensive evaluation coefficient CEC is compared with a preset comprehensive evaluation coefficient threshold CEC yuzhi A comparison was conducted and the impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability was evaluated based on the comparison results. The impact was divided into low impact and high impact. The specific comparison analysis is as follows:

[0036] If CEC≤CEC yuzhi ,The impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability is low;

[0037] If CEC>CEC yuzhi The impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability is high.

[0038] Preferably, when the assessment result is a high impact, a predetermined threshold interval of the impact assessment coefficient is determined [IEC min , IEC max ] and after determination, generate the impact assessment coefficient IEC of each load device i Compare and classify each load device into major influencing devices, minor influencing devices and normal devices according to the comparison results. The specific classification is as follows:

[0039] If IEC i <IEC min , classify the load equipment as normal equipment;

[0040] If IEC min ≤IEC i ≤IEC max , classify the load equipment as secondary impact equipment;

[0041] If IEC max >IEC i , classify the load equipment as the main influencing equipment.

[0042] Preferably, according to the classification results of each load device, corresponding measures are taken for the main impact equipment, the secondary impact equipment and the normal equipment respectively, specifically:

[0043] For load devices classified as normal, the following measures are taken: maintain the existing voltage regulation strategy, do not make additional interventions, and continuously monitor their voltage changes and operating status. If there is a trend of increasing voltage fluctuations, prioritize reducing voltage fluctuation intervention on normal devices to ensure their normal operation while monitoring their impact on the power grid in real time.

[0044] For load devices classified as minor impact devices, the following measures are taken: dynamically adjust their voltage control strategies based on real-time voltage fluctuation data and the operating status of the load devices; adjust the power allocation and start-stop strategies of the devices to mitigate the impact of voltage fluctuations; and regularly evaluate their voltage fluctuation stability to optimize the voltage control process.

[0045] For load devices that are classified as major influencing devices, the measures taken are: give priority to emergency voltage regulation measures, reduce the impact of voltage fluctuations on the power grid by allocating stable voltage resources and adjusting equipment start-up and shutdown strategies; monitor the voltage fluctuations and load changes of the equipment in real time, conduct in-depth analysis and optimization of the operating status of high-impact equipment, and dynamically adjust the voltage control model to reduce its negative impact on power grid stability.

[0046] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0047] 1. The present invention accurately identifies the voltage fluctuation characteristics of each device during the start-up and shutdown process through real-time monitoring and data collection of multiple load devices in the distribution network. Specifically, a variety of key data such as voltage fluctuation amplitude, power change, and voltage acceleration are collected when the equipment starts and stops, and these data are converted into impact assessment coefficients and acceleration indices, thereby achieving an accurate assessment of the independent contribution of each load device to the voltage fluctuation. Through this method, the fluctuation decoupling problem caused by the close synchronous start-up and shutdown time of load devices in the existing technology can be effectively avoided, and the voltage fluctuation impact of multiple load devices can be accurately distinguished and quantified, thereby ensuring the accuracy and reliability of the grid voltage fluctuation assessment results. This technical effect provides strong data support for the optimization of grid control strategies.

[0048] 2. The present invention combines the voltage fluctuation impact assessment coefficient of each load device with the equipment classification result to carry out hierarchical management of the equipment (such as normal equipment, secondary impact equipment, and primary impact equipment), and takes corresponding control measures according to the degree of impact of different categories of equipment. This differentiated management strategy ensures that the power grid can take precise adjustment measures for each situation, avoiding the impact on the overall stability of the power grid when the voltage fluctuation is large. Emergency regulation of primary impact equipment, dynamic optimization and adjustment of secondary impact equipment, and continuous monitoring of normal equipment can achieve the optimal configuration of power grid resources in different situations, ensuring the efficient and safe operation of the power grid. This precise and flexible voltage control method significantly improves the control capability and adaptability of the power grid, and minimizes the potential risk of voltage fluctuations to the stability of the distribution network.

[0049] 3. The present invention dynamically optimizes the parameter weights of the evaluation model, so that the comprehensive evaluation system can be continuously and automatically adjusted according to real-time operating data, thereby improving the grid's responsiveness and adaptability to fluctuation characteristics. As the grid's operating status changes and the load equipment has different operating characteristics, the evaluation model can be automatically updated, ensuring that the grid's comprehensive indicator system accurately reflects the grid's operating dynamics. This adaptive optimization capability enables the distribution network to always perform precise regulation and timely risk warnings under complex and changeable load fluctuation conditions, which not only improves the stability of the grid, but also enhances the automation and intelligence level of grid management, thereby significantly improving the overall operating efficiency and long-term stability of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0051] Figure 1 The figure is a flow chart of the method for constructing a comprehensive indicator system for distribution networks based on dynamic characteristics evaluation according to the present invention. DETAILED DESCRIPTION

[0052] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0053] The present invention provides Figure 1 The method for constructing a comprehensive indicator system for a distribution network based on dynamic characteristics evaluation shown in the figure specifically includes the following steps:

[0054] During the operation of the distribution network, all load devices in the distribution network are detected in real time. When multiple load devices are detected to be started or stopped at the same time, all load devices that are started or stopped at the same time are identified and marked.

[0055] During the operation of the distribution network, high-precision voltage and current monitoring sensors can be deployed on the main transformer, branch transformers, and key line nodes, combined with intelligent data collection terminals to achieve real-time detection of all load devices. These sensors collect real-time data such as voltage, current, and power, and transmit the data to the central control system through the communication module. The software uses the topological information and numbering management functions of the equipment to accurately associate the monitoring data with each load device. By parsing and analyzing the collected data in real time, the software can dynamically identify the operating status of the equipment (such as normal operation, start-stop switching, etc.) and record the start-stop characteristics of the load equipment to ensure a comprehensive understanding of the operating status of all load devices in the distribution network.

[0056] On the basis of real-time detection of load equipment, the software performs time series analysis on the collected operating data of each device, extracts the timestamps of the equipment start and stop events, and dynamically compares and groups the timestamps. When two or more load devices are found to start and stop within a set time window (for example, between 0.5 seconds and 1 second), it is determined to be "simultaneous start and stop". Among them, "multiple" is defined as a number of load devices of not less than two, and "simultaneous start and stop" is based on the time difference between the start and stop of the equipment not exceeding the set time window. The software marks these detected devices and stores their numbers, operating data and characteristic parameters for subsequent analysis and processing. This process is completed automatically by the algorithm without the need for human intervention.

[0057] The reason for real-time detection of load devices and marking of devices that start and stop at the same time is that when multiple load devices start and stop almost simultaneously, the superimposed voltage fluctuations will have a significant impact on the operational stability of the distribution network, and existing technologies cannot accurately identify the independent contribution of each device. This real-time detection and marking function solves the problems of fluctuation decoupling and contribution quantification by providing basic data for subsequent generation and classification of fluctuation characteristic parameters, laying a data foundation for the dynamic construction of a comprehensive distribution network indicator system. At the same time, this approach can support accurate dynamic evaluation and real-time optimization, improve the regulation efficiency and stability of the distribution network, and ensure that the technical solution can fully cope with complex operating scenarios.

[0058] Real-time acquisition of voltage fluctuation characteristic information of each load device during the start-up and shutdown process, and analysis after acquisition to generate the voltage fluctuation impact assessment coefficient and voltage fluctuation acceleration index for each load device;

[0059] In this embodiment, voltage fluctuation characteristic information of each load device that is started and stopped simultaneously is obtained in real time during the start-up and shutdown process, and analyzed after acquisition to generate a voltage fluctuation impact assessment coefficient and a voltage fluctuation acceleration index for each load device. Specifically, the following steps are included:

[0060] Real-time acquisition of voltage fluctuation characteristic information of each load device that starts and stops simultaneously during the start-up and shutdown process, and pre-processing after acquisition;

[0061] In order to obtain real-time information on the voltage fluctuation characteristics of various load devices that are started and stopped simultaneously during the start-up and shutdown process, high-precision current and voltage sensors can be deployed at key nodes of the distribution network (such as transformers, load points, trunk lines, etc.), and the sensor data can be uploaded to the central data acquisition system in real time through intelligent acquisition terminals. By installing devices with real-time data acquisition capabilities, such as smart meters or remote monitoring devices, the system can instantly collect voltage fluctuation information when the equipment starts and stops. These devices transmit real-time data to the central control system via wireless or wired communication networks. The central system software is responsible for parsing and synchronizing the voltage fluctuation data of each device, and ensuring that the voltage changes of each device can be accurately captured when the load equipment starts and stops.

[0062] Preprocessing is performed on voltage fluctuation characteristic information acquired in real time to ensure the accuracy and reliability of the data. Since real-time voltage data may contain noise, outliers, and inconsistencies, it needs to be preprocessed. Preprocessing includes three main steps: first, denoising, which uses signal filtering techniques (such as low-pass filtering or Kalman filtering) to remove high-frequency noise in voltage fluctuations; second, time synchronization, which aligns the data timestamps of multiple devices to ensure that their acquisition times are consistent, avoiding errors caused by time asynchrony; and finally, outlier removal, which uses software algorithms to detect and remove abnormal data points caused by sensor failures or communication delays. These steps can be automated by software to ensure that the data used in subsequent analysis is accurate, stable, and comparable.

[0063] Extracting voltage fluctuation change information and load power variation information from the pre-processed voltage fluctuation characteristic information of each load device;

[0064] To extract voltage fluctuation and load power variation information from the preprocessed voltage fluctuation characteristic information for each load device, software can be used to automate the data analysis and feature extraction process. First, voltage fluctuation variation information can be extracted by calculating the rate of change of voltage values ​​for each device during startup and shutdown. Specifically, the software calculates the voltage variation amplitude for each load device at different time points based on time series data and uses differential calculation to determine the instantaneous rate of change of voltage fluctuation (i.e., the speed of voltage change). Second, load power variation information can be extracted by analyzing the power curve changes of the load devices. The software performs time series analysis on the real-time power data, calculates the instantaneous change in power and the rate of change, and quantifies the power fluctuation during the startup and shutdown of each load device. This extracted voltage fluctuation and power variation information can reflect the impact of the device startup and shutdown process on voltage fluctuation and provide data support for the subsequent calculation of impact assessment coefficients and indices. The entire extraction process is completed using automated algorithms, ensuring efficient and accurate processing of large amounts of real-time data.

[0065] The extracted voltage fluctuation change information and load power change information are analyzed to generate the voltage fluctuation impact assessment coefficient and voltage fluctuation acceleration index of each load device respectively.

[0066] In this embodiment, the logic for obtaining the voltage fluctuation impact assessment coefficient of each load device is as follows:

[0067] The voltage fluctuation change information of each load device is extracted from the pre-processed voltage fluctuation characteristic information, including the instantaneous voltage change value, actual power, start and stop time of each load device at different times during the start and stop process, and the total start and stop time of all load devices at the same time, and calibrated as QT i and ZQT, It represents the instantaneous voltage change value of the i-th load device at time m during the start-stop process. It represents the actual power of the i-th load device at time m during the start-stop process, QT i represents the duration of the start and stop of the i-th load device, ZQT represents the total duration of the simultaneous start and stop of all load devices, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0068] In order to obtain the instantaneous voltage change value, actual power, start-up and shutdown time of each load device at different times within a period of time during the start-up and shutdown process in real time, as well as the total start-up and shutdown time of all load devices, high-precision voltage and current monitoring equipment (such as smart meters, power analyzers, etc.) can be deployed at key nodes of the distribution network (such as transformers, trunk lines, branch lines and load equipment ends), and the real-time data collected by these devices can be transmitted to the central monitoring system through communication modules. These devices can collect voltage, current and power data at a frequency of milliseconds, and associate each data point with a timestamp through a time series label to ensure the real-time and accuracy of the data. Specifically, the software system can record the instantaneous change value of the voltage fluctuation of the load equipment during the start-up and shutdown process (i.e., ), by measuring the instantaneous current data and combining it with the power factor of the equipment, the power at that moment is calculated (i.e. At the same time, the system monitors the start and stop status of each device, identifies the start and stop events of the device by detecting significant changes in voltage or current, and calculates the duration of the start and stop process (i.e. QT i For the synchronized start and stop of all load devices, the total duration (ZQT) is calculated by the software system through timestamp comparison, identifying the time period when multiple load devices were started and stopped at the same time. All of this data is processed and stored by automated software algorithms, ensuring real-time analysis and calculation of relevant indicators, providing accurate data support for subsequent voltage fluctuation impact assessments.

[0069] Calculate the voltage fluctuation impact assessment coefficient of each load device. The specific calculation formula is as follows:

[0070]

[0071] Where CMP i is the voltage fluctuation impact assessment coefficient of the i-th load equipment.

[0072] Calculate the voltage fluctuation impact assessment coefficient CMP for each load device i When calculating the voltage fluctuation, we first need to consider the impact of the voltage fluctuation amplitude, power change and start-stop time length on the voltage fluctuation. Each step in the formula is carefully designed to ensure that these factors are correctly quantified and accurately reflect the contribution of the equipment start-stop process to the voltage fluctuation. First, the formula Indicates the instantaneous voltage change value of the device at each moment. This is a direct parameter to measure the contribution of the device to the voltage fluctuation amplitude. It represents the actual power of the device at the corresponding moment. Its relationship with voltage fluctuation can be reflected by the dynamic coupling between power and voltage. Therefore, the larger the power value, the more significant the impact of the device on voltage fluctuation. Then, by multiplying the voltage fluctuation contribution at each moment by the power value, the comprehensive impact on voltage fluctuation at each moment is obtained. Next, the time attenuation factor in the formula It is introduced to correct the impact of start and stop time. The contribution of equipment with longer start and stop times to fluctuations will gradually weaken. Therefore, this factor attenuates the impact of longer start and stop times, ensuring that the contribution of time length to voltage fluctuations is reasonably reflected. Finally, by taking the weighted average of all moments, the contribution of each equipment to voltage fluctuation is combined to obtain the voltage fluctuation impact assessment coefficient CMP for that equipment. i , ensuring that the evaluation results can reflect both the instantaneous impact of the equipment and the time factor of its start-up and shutdown process.

[0073] Voltage fluctuation impact assessment coefficient CMP of the i-th load equipment i The size of directly reflects the contribution of the device to the voltage fluctuation during the start-stop process. i A high value means that the device has a greater impact on voltage fluctuations, usually due to large voltage changes, significant power changes, or a long start-up and shutdown process when the device is started and stopped, resulting in a more significant superposition effect of voltage fluctuations. When evaluating the impact of the simultaneous start-up and shutdown of multiple load devices on voltage fluctuation stability, the voltage fluctuation impact assessment coefficients of multiple load devices will be combined together as a key indicator to measure the overall voltage fluctuation stability. Specifically, when the voltage fluctuation impact assessment coefficients of multiple load devices are high, it indicates that these devices contribute significantly to voltage fluctuations, which may lead to increased voltage fluctuations and thus affect the stability of the distribution network. Therefore, by combining the voltage fluctuation impact assessment coefficients of each load device, the overall impact of the simultaneous start-up and shutdown of multiple devices on voltage fluctuation stability can be effectively assessed, thereby guiding the optimization and adjustment of power grid control measures.

[0074] In this embodiment, the logic for obtaining the voltage fluctuation acceleration index of each load device is as follows:

[0075] Extract the load power change information from the pre-processed voltage fluctuation characteristic information of each load device, including the voltage value of each load device at different times during the start-up and shutdown process, the absolute value of the actual power change, and the voltage fluctuation amplitude, and use the function U i (t), ΔPB i (t) and ΔUB i (t) is used to represent the time point, U i (t) represents the voltage value of the i-th load device at time t during the start-stop process, ΔPBi (t) represents the absolute value of the actual power change of the i-th load device at time t during the start-up and shutdown process, ΔUB i (t) represents the voltage fluctuation amplitude of the i-th load device at time t during the start-up and shutdown process. The time period is defined as [t1, t2], i = 1, 2, 3, ..., k, where k is a positive integer;

[0076] To obtain real-time voltage values, the absolute value of actual power changes, and the magnitude of voltage fluctuations during the startup and shutdown processes of each load device, high-precision sensors and smart metering devices (such as voltage sensors, current sensors, and power analyzers) can be deployed at key distribution network nodes and load devices. These devices continuously monitor and record changes in voltage, current, and power. The voltage value of each load device can be measured in real time by voltage sensors, and the data is transmitted to the central control system via a built-in data acquisition module. To measure power fluctuations, smart meters and power analyzers collect power data from load devices in real time, calculate the power value by multiplying the current and voltage, and calculate the power change to obtain the absolute value of the power change. Furthermore, the magnitude of voltage fluctuations can be calculated based on the maximum voltage offset. The system automatically identifies the magnitude of voltage fluctuations by periodically analyzing the voltage signal and extracts the maximum value of the voltage fluctuation based on a pre-set algorithm. All of this real-time data is transmitted to the software platform via the communication network for unified storage, analysis, and processing. The software system synchronizes this data to ensure accurate alignment of data from all devices within the same time period and automatically generates the required voltage fluctuation characteristics. In this way, the system can monitor and obtain voltage fluctuations, power changes and fluctuation amplitudes in real time, providing accurate data support for subsequent analysis and calculations.

[0077] The voltage fluctuation amplitude of each load device at different times during the start-up and shutdown process is constructed into a set, and the maximum value in the set is calibrated as

[0078] Calculate the voltage fluctuation acceleration index of each load device. The specific calculation formula is as follows:

[0079]

[0080] Where CAL i is the voltage fluctuation acceleration index of the i-th load device.

[0081] Calculate the voltage fluctuation acceleration index CAL i The design of the formula is to comprehensively evaluate the degree of voltage fluctuation during the start-up and shutdown process of the i-th load equipment. First, the second-order derivative term in the formula The acceleration of voltage fluctuation captures the severity of voltage fluctuation. The greater the acceleration of voltage fluctuation, the more severe the impact of the equipment on voltage stability and the faster the rate of change of fluctuation. Therefore, this item is the core of the assessment of fluctuation intensification. Next, the voltage acceleration is compared with the load power change ΔPB. i (t) multiplied by the power change reflects the impact of power change on the acceleration of voltage fluctuation. The change of load power directly determines the load effect of load equipment on the grid voltage. Therefore, the contribution of power change rate to the aggravation of voltage fluctuation cannot be ignored. Furthermore, the maximum amplitude of voltage fluctuation is introduced. The purpose is to weight the fluctuation amplitudes during the equipment startup and shutdown process, ensuring that the maximum voltage fluctuation amplitude plays a prominent role in the assessment of the final impact. Finally, the formula integrates these factors to cumulatively calculate the acceleration effect of voltage fluctuations during the startup and shutdown of each device. This is then normalized by the duration t2-t1 of the time period [t1, t2] to derive the voltage fluctuation acceleration index for that device during that time period. This approach accurately assesses the contribution of each device to the increased voltage fluctuations during startup and shutdown, providing the necessary data support for evaluating the impact of simultaneous startup and shutdown of multiple load devices on voltage fluctuation stability.

[0082] Voltage fluctuation acceleration index CAL of the i-th load device i The size of CAL directly reflects the contribution of the device to the degree of voltage fluctuation during the start-stop process. i A smaller value indicates that the load equipment has a more significant acceleration effect on voltage fluctuations during the start-up and shutdown process, that is, the voltage fluctuation amplitude is not only larger, but also the rate of change is faster, which may lead to more severe and unstable voltage fluctuations. On the contrary, a smaller CAL i The value indicates that the aggravation effect of the voltage fluctuation when the equipment is started and stopped is weak. By evaluating the voltage fluctuation acceleration index of each load device, the impact of all load devices on the voltage fluctuation stability can be comprehensively obtained. When multiple load devices are started and stopped at the same time, the acceleration index of each device can reflect its contribution to the overall voltage fluctuation stability. If the CAL of most devices is i A large value indicates that the simultaneous startup and shutdown of multiple devices may cause large voltage fluctuations, thereby affecting the stability of the distribution network; conversely, a small value indicates that the impact of voltage fluctuations is small. Therefore, by combining the voltage fluctuation acceleration index of all load devices, we can accurately assess the overall impact of the simultaneous startup and shutdown of multiple load devices on voltage fluctuation stability, providing a scientific basis for power grid regulation strategies.

[0083] A voltage fluctuation impact assessment model is constructed based on the generated voltage fluctuation impact assessment coefficients and voltage fluctuation acceleration indices for each load device. The impact assessment coefficients for each load device are generated and analyzed after generation to assess the impact of simultaneous start-up and shutdown of multiple load devices on voltage fluctuation stability, categorizing the impact into low impact and high impact.

[0084] In this embodiment, the voltage fluctuation impact assessment coefficient CMP of each load device is generated. i and voltage fluctuation acceleration index CAL i Construct a voltage fluctuation impact assessment model and generate the impact assessment coefficient IEC of each load device through weighted summation i , the specific calculation formula is as follows:

[0085] IEC i =ω1*CMP i +ω2*CAL i

[0086] Where ω1 and ω2 are the voltage fluctuation impact assessment coefficients CMP of each load device respectively. i and voltage fluctuation acceleration index CAL i The non-zero weight coefficient of , and ω1+ω2=1;

[0087] In order to realize the voltage fluctuation impact assessment coefficient CMP of each load device i and voltage fluctuation acceleration index CAL i Combined with the above, the voltage fluctuation impact assessment model can be constructed by using the weighted summation algorithm in the software system. In the calculation process, the voltage fluctuation impact assessment coefficient CMP is first extracted from each load device. i and voltage fluctuation acceleration index CAL i Then, the weighted summation formula is applied, using non-zero weight coefficients ω1 and ω2, to weight the two indicators to obtain the impact assessment coefficient IEC of each load device. i The specific formula is: IEC i =ω1*CMP i +ω2*CAL i; Among them, ω1 and ω2 are the weight coefficients of the voltage fluctuation impact assessment coefficient and the voltage fluctuation acceleration index, which respectively measure the relative importance of these two indicators on the total impact of the load equipment. The setting of the weight coefficient is determined according to the needs and goals in the actual application. Generally, it is reasonably allocated through optimization algorithms or expert experience to ensure that the weighted values ​​of the two reasonably and effectively reflect the overall impact of the equipment. In particular, ω1+ω2=1 ensures that the sum of the two weight coefficients is 1 to maintain the balance of the calculation. This weighted summation method can flexibly adjust the weights of the voltage fluctuation amplitude and fluctuation acceleration on the overall impact assessment, thereby obtaining an accurate load equipment impact assessment coefficient, which provides support for the subsequent voltage fluctuation stability assessment.

[0088] The impact assessment coefficient IEC of each load device i Conduct comprehensive analysis and generate comprehensive evaluation coefficient CEC according to the formula:

[0089] In this embodiment, the generated comprehensive evaluation coefficient CEC is compared with the preset comprehensive evaluation coefficient threshold CEC. yuzhi A comparison was conducted and the impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability was evaluated based on the comparison results. The impact was divided into low impact and high impact. The specific comparison analysis is as follows:

[0090] If CEC≤CEC yuzhi ,The impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability is low;

[0091] This situation means that the simultaneous startup and shutdown of multiple load devices has a relatively small impact on voltage fluctuations, preventing significant voltage fluctuations or instability. In this scenario, the grid system's voltage stability is relatively good, and the impact of the synchronized startup and shutdown of load devices on voltage is within a controllable range, likely preventing significant voltage shifts or excessive voltage fluctuations. This means that in this scenario, the grid's regulation strategy can be relatively relaxed, potentially eliminating the need for emergency adjustments, and maintaining a high level of grid operation security.

[0092] If CEC>CEC yuzhi The impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability is high.

[0093] This situation indicates that the simultaneous startup and shutdown of multiple load devices can cause severe voltage fluctuations, potentially leading to voltage instability or large fluctuations. This situation demonstrates that the simultaneous startup and shutdown of multiple load devices in a short period of time has a significant impact on the power grid. The amplitude and frequency of voltage fluctuations may exceed the grid's designed safety range, thus affecting the overall stability of the grid. Therefore, grid regulation strategies require more precise and detailed adjustments, such as using automatic voltage regulation equipment, adjusting reactive power, or activating backup power sources to mitigate or eliminate the impact of voltage fluctuations and ensure safe grid operation.

[0094] The pre-set comprehensive evaluation coefficient threshold can be determined through a variety of methods, which can be automated through the software system's algorithms. First, the threshold can be set through statistical analysis of historical data. Specifically, this involves collecting and analyzing past voltage fluctuation data from the simultaneous startup and shutdown of multiple load devices. Based on the actual voltage fluctuations, an appropriate threshold range can be determined. The software system can then perform regression analysis on this data to identify the relationship between voltage fluctuations and the comprehensive evaluation coefficient, thereby generating a reference value based on historical trends. Second, simulation techniques can be used to simulate the startup and shutdown of multiple load devices in an experimental or simulated environment. A dynamic power grid simulation model can then be used to simulate voltage fluctuation stability under different scenarios. The threshold can then be automatically adjusted based on the simulation results to ensure that it accurately reflects the impact of voltage fluctuations on grid stability during actual operation. Furthermore, the software system can employ machine learning algorithms to train models to identify patterns and changes in voltage fluctuations, thereby dynamically optimizing and adjusting the comprehensive evaluation coefficient threshold for more precise grid control. Through these methods, the comprehensive evaluation coefficient threshold can be automatically adjusted and optimized based on the continuous updates of real-time and historical data, ensuring the safe and stable operation of the power grid.

[0095] If the assessment result is a high impact, the preset impact assessment coefficient threshold interval is determined, and after determination, it is compared with the generated impact assessment coefficient of each load device. Based on the comparison result, each load device is divided into major impact devices, minor impact devices and normal devices;

[0096] In this embodiment, when the evaluation result is a high impact, a predetermined impact evaluation coefficient threshold interval [IEC min , IEC max ] and after determination, generate the impact assessment coefficient IEC of each load device i Compare and classify each load device into major influencing devices, minor influencing devices and normal devices according to the comparison results. The specific classification is as follows:

[0097] If IEC i <IEC min, classify the load equipment as normal equipment;

[0098] If IEC min ≤IEC i ≤IEC max , classify the load equipment as secondary impact equipment;

[0099] If IEC max >IEC i , classify the load equipment as the main influencing equipment.

[0100] Determining the pre-set threshold range for the impact assessment coefficient can be achieved through various methods, all of which can be automated by software. In the first method, the software can determine the threshold range through statistical analysis of historical data. Specifically, the system first collects and analyzes data on voltage fluctuations and corresponding impact assessment coefficients from past load device startups and shutdowns. Using statistical methods (such as percentile analysis and standard deviation analysis), the system determines a reasonable range. For example, the system calculates the maximum, minimum, and median values ​​of the impact assessment coefficients in the historical data and sets upper and lower limits based on the distribution characteristics. In the second method, the software can use simulation analysis to simulate the impact of different load devices on voltage fluctuations under different startup and shutdown conditions and dynamically adjust the threshold range based on the simulation results. Through repeated testing and adjustment, the software can automatically optimize the threshold range to adapt to different grid operating environments. A third method uses a machine learning algorithm to train a model to learn the relationship between load device startup and shutdown and voltage fluctuations, automatically identifying a reasonable range for the impact assessment coefficient. This method combines real-time and historical data to flexibly adjust the threshold range to ensure it adapts to dynamic grid changes. Through these methods, the software can automatically set and optimize the impact assessment coefficient threshold range based on the actual grid operation, historical data, and real-time feedback.

[0101] When load devices are categorized into normal, minor, and major impact devices using their impact assessment coefficients, this represents their varying degrees of contribution to voltage fluctuation stability. A normal device with an impact assessment coefficient below the minimum value of a preset threshold range indicates that the device has a minimal impact on voltage fluctuations, enabling relatively stable grid operation. Therefore, more relaxed control measures can be adopted to avoid unnecessary intervention. A minor impact device with an impact assessment coefficient within the threshold range indicates that the device has a moderate impact on voltage fluctuations and may require certain grid regulation measures. However, conventional dispatch strategies can generally be used to maintain voltage stability and minimize disruption to grid operations. A major impact device with an impact assessment coefficient exceeding the maximum value of the threshold range indicates that the device has a significant impact on voltage fluctuations, potentially leading to grid instability. Emergency control measures, such as activating backup power sources or adjusting grid configuration, are necessary to quickly restore voltage stability. Therefore, this categorization helps dynamically adjust grid control strategies based on the impact of each device, ensuring grid stability and security.

[0102] In this embodiment, according to the classification results of each load device, corresponding measures are taken for the main impact device, the secondary impact device and the normal device respectively, specifically:

[0103] For load devices classified as normal, the following measures are taken: maintain the existing voltage regulation strategy, do not make additional interventions, and continuously monitor their voltage changes and operating status. If there is a trend of increasing voltage fluctuations, prioritize reducing voltage fluctuation intervention on normal devices to ensure their normal operation while monitoring their impact on the power grid in real time.

[0104] For load devices classified as normal equipment, the existing voltage regulation strategy is maintained and continuously monitored. The operating status of the equipment and voltage fluctuations can be tracked and analyzed in real time through the software system. Specifically, the software system continuously monitors voltage changes, the operating status of load equipment, and power usage through an automated data acquisition platform. When the voltage fluctuation is detected to be within the preset threshold, the system will maintain the existing voltage regulation strategy without additional intervention. This is because normal equipment has a small impact on voltage fluctuations, so there is no need to frequently adjust the grid configuration. If the system detects a trend of increasing voltage fluctuations, the software can issue a timely warning through a set alarm mechanism, assess the extent of the impact based on real-time monitoring data, and then take measures to adjust the voltage regulation strategy. This approach can effectively reduce unnecessary waste of resources while ensuring efficient management of the power grid under stable operation.

[0105] For load devices classified as minor impact devices, the following measures are taken: dynamically adjust their voltage control strategies based on real-time voltage fluctuation data and the operating status of the load devices; adjust the power allocation and start-stop strategies of the devices to mitigate the impact of voltage fluctuations; and regularly evaluate their voltage fluctuation stability to optimize the voltage control process.

[0106] For load devices classified as secondary impact devices, dynamic adjustment of voltage control strategies and optimization of power allocation are necessary. The software system collects voltage fluctuation data and device power change information in real time, dynamically analyzing the device's impact on voltage fluctuations. When voltage fluctuations are within an acceptable range, the system optimizes and controls based on the voltage and power change trends of the current load device, adjusting its power allocation and start-stop strategies. For example, if the voltage fluctuations of a load device are excessive, the system can prioritize adjusting the power usage of that device to reduce its impact on the grid voltage. Based on real-time grid load data, the software dynamically allocates voltage fluctuation compensation resources and regularly evaluates the voltage fluctuation stability of the device to ensure the safety of load device operation and the stability of the grid. This dynamic control effectively mitigates the fluctuation risks associated with secondary impact devices while avoiding excessive intervention.

[0107] For load devices that are classified as major influencing devices, the measures taken are: give priority to emergency voltage regulation measures, reduce the impact of voltage fluctuations on the power grid by allocating stable voltage resources and adjusting equipment start-up and shutdown strategies; monitor the voltage fluctuations and load changes of the equipment in real time, conduct in-depth analysis and optimization of the operating status of high-impact equipment, and dynamically adjust the voltage control model to reduce its negative impact on power grid stability.

[0108] Prioritizing emergency voltage regulation measures and real-time monitoring for load devices classified as major impact devices is crucial for ensuring grid stability. Software systems need to implement more refined control and immediate feedback mechanisms to prioritize the management of these devices. By real-time monitoring of voltage fluctuations and load changes, the system can automatically identify when voltage fluctuations exceed normal ranges and immediately initiate emergency regulation measures, such as adjusting load device start-up and shutdown strategies, allocating stable voltage resources, or activating backup power sources. The system must not only continuously monitor voltage fluctuation data but also conduct in-depth analysis of the operating status of high-impact devices to identify potential factors that could trigger greater fluctuations. Dynamically optimize the regulation model to minimize the negative impact of voltage fluctuations on the grid. For example, if a device consistently impacts grid voltage, the system can mitigate its impact by adjusting its power load or start-up and shutdown strategies. This flexible and timely grid regulation approach ensures maximum grid stability while preventing fluctuations from high-impact devices from causing instability across the entire grid.

[0109] The impact assessment coefficients, equipment classification results and assessment conclusions of each load device generated are dynamically incorporated into the comprehensive indicator system of the distribution network, and a comprehensive indicator database covering voltage fluctuation characteristics, load equipment operating status and control strategy response is established. By dynamically optimizing the parameter weights of the evaluation model, the ability and stability of the distribution network comprehensive indicator system to reflect operating characteristics are continuously improved.

[0110] In order to dynamically incorporate the impact assessment coefficients, equipment classification results, and assessment conclusions of the generated load equipment into the comprehensive indicator system of the distribution network, this can be achieved through the database management and data integration functions of the software system. First, all generated assessment data (including the impact assessment coefficients and classification results of each device) need to be automatically collected through the real-time data acquisition platform and control system. The software system will integrate this data into the comprehensive indicator database regularly or in real time to form a dynamically updated database so that the power grid operation status can be evaluated at any time. During the data integration process, the system will ensure that all types of data (such as voltage fluctuations, load equipment status, and its impact on the power grid) can be stored in a unified standard format, and establish real-time indexes in the database so that the data can be quickly accessed and analyzed. This approach ensures the real-time and integrity of all relevant data, providing precise support for subsequent dynamic optimization and power grid regulation.

[0111] To establish a comprehensive indicator database that comprehensively covers voltage fluctuation characteristics, load device operating status, and control strategy responses, the software system needs to integrate multiple data sources and perform multi-dimensional data aggregation and analysis. Voltage fluctuation characteristics, load device operating status (such as load changes, start-up and shutdown conditions), and control strategy responses (such as voltage regulation and power adjustment measures) are all essential data for power grid operation. Through real-time monitoring and historical data analysis, the system automatically obtains this data from various sources (such as sensors, smart meters, and monitoring platforms) and converts it into analyzable indicators. By establishing a hierarchical database system, the specific data of each device (such as voltage fluctuation amplitude and device power fluctuation) and the overall operation status of the power grid (such as voltage stability and control strategy effectiveness) can be integrated into a unified platform. This database will have powerful data storage and query capabilities, providing a comprehensive view of power grid control at any time.

[0112] Dynamically optimizing the parameter weights of the evaluation model is key to improving the responsiveness and stability of the distribution network's comprehensive indicator system. Software systems can incorporate machine learning or optimization algorithms to automatically adjust the evaluation model's parameter weights. For example, historical data can be used for model training. Based on the impact assessment coefficients of load devices, device classification results, and grid stability assessments, the weights of different indicators in the comprehensive evaluation are gradually adjusted to accurately reflect the grid's operating characteristics during real-time regulation. By continuously monitoring the grid's operating status, the software system can dynamically adjust the weights of various parameters to account for the varying impacts of different load device startups and shutdowns on grid stability. This dynamic optimization process ensures that the distribution network's comprehensive indicator system maintains a high degree of accuracy and adaptability in the face of varying load fluctuations or external disturbances, thereby improving the grid's responsiveness and long-term operational stability. This optimization process is not only automated but also continuously operates as the grid's operating environment changes, ensuring the system can adapt over time and maintain optimal performance.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0115] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0116] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0119] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0120] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for constructing a comprehensive indicator system for distribution networks based on dynamic characteristics evaluation, characterized in that: The specific steps include: During the operation of the distribution network, all load devices in the distribution network are detected in real time. When multiple load devices are detected to be started or stopped at the same time, all load devices that are started or stopped at the same time are identified and marked. Real-time acquisition of voltage fluctuation characteristic information of each load device during the start-up and shutdown process, and analysis after acquisition to generate the voltage fluctuation impact assessment coefficient and voltage fluctuation acceleration index for each load device; A voltage fluctuation impact assessment model is constructed based on the generated voltage fluctuation impact assessment coefficients and voltage fluctuation acceleration indices for each load device. The impact assessment coefficients for each load device are generated and analyzed after generation to assess the impact of simultaneous start-up and shutdown of multiple load devices on voltage fluctuation stability, categorizing the impact into low impact and high impact. If the assessment result is a high impact, the preset impact assessment coefficient threshold interval is determined, and after determination, it is compared with the generated impact assessment coefficient of each load device. Based on the comparison result, each load device is divided into major impact devices, minor impact devices and normal devices; According to the classification results of each load equipment, corresponding measures are taken for the main impact equipment, secondary impact equipment and normal equipment respectively; The impact assessment coefficients, equipment classification results and assessment conclusions of each load device generated are dynamically incorporated into the comprehensive indicator system of the distribution network, and a comprehensive indicator database covering voltage fluctuation characteristics, load equipment operating status and control strategy response is established. By dynamically optimizing the parameter weights of the evaluation model, the ability and stability of the distribution network comprehensive indicator system to reflect operating characteristics are continuously improved.

2. The method for constructing a comprehensive indicator system for distribution network based on dynamic characteristics evaluation according to claim 1, characterized in that: The voltage fluctuation characteristic information of each load device that is started and stopped simultaneously is obtained in real time, and analyzed after acquisition to generate the voltage fluctuation impact assessment coefficient and voltage fluctuation acceleration index of each load device respectively. The specific steps include: Real-time acquisition of voltage fluctuation characteristic information of each load device that starts and stops simultaneously during the start-up and shutdown process, and pre-processing after acquisition; Extracting voltage fluctuation change information and load power variation information from the pre-processed voltage fluctuation characteristic information of each load device; The extracted voltage fluctuation change information and load power change information are analyzed to generate the voltage fluctuation impact assessment coefficient and voltage fluctuation acceleration index of each load device respectively.

3. The method for constructing a comprehensive indicator system for distribution network based on dynamic characteristics evaluation according to claim 2, characterized in that: The logic for obtaining the voltage fluctuation impact assessment coefficient of each load device is as follows: The voltage fluctuation change information of each load device is extracted from the pre-processed voltage fluctuation characteristic information, including the instantaneous voltage change value, actual power, start and stop time of each load device at different times during the start and stop process, and the total start and stop time of all load devices at the same time, and calibrated as QT i and ZQT, It represents the instantaneous voltage change value of the i-th load device at time m during the start-stop process. It represents the actual power of the i-th load device at time m during the start-stop process, QT i represents the duration of the start and stop of the i-th load device, ZQT represents the total duration of the simultaneous start and stop of all load devices, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers; Calculate the voltage fluctuation impact assessment coefficient of each load device. The specific calculation formula is as follows: Where CMP i is the voltage fluctuation impact assessment coefficient of the i-th load equipment.

4. The method for constructing a comprehensive indicator system for distribution network based on dynamic characteristics evaluation according to claim 3, characterized in that: The logic for obtaining the voltage fluctuation acceleration index of each load device is as follows: Extract the load power change information from the pre-processed voltage fluctuation characteristic information of each load device, including the voltage value of each load device at different times during the start-up and shutdown process, the absolute value of the actual power change, and the voltage fluctuation amplitude, and use the function U i (t), ΔPB i (t) and ΔUB i (t) is used to represent the time point, U i (t) represents the voltage value of the i-th load device at time t during the start-stop process, ΔPB i (t) represents the absolute value of the actual power change of the i-th load device at time t during the start-up and shutdown process, ΔUB i (t) represents the voltage fluctuation amplitude of the i-th load device at time t during the start-up and shutdown process. The time period is defined as [t1, t2], i = 1, 2, 3, ..., k, where k is a positive integer; The voltage fluctuation amplitude of each load device at different times during the start-up and shutdown process is constructed into a set, and the maximum value in the set is calibrated as Calculate the voltage fluctuation acceleration index of each load device. The specific calculation formula is as follows: Where CAL i is the voltage fluctuation acceleration index of the i-th load device.

5. The method for constructing a comprehensive indicator system for distribution network based on dynamic characteristics evaluation according to claim 4, characterized in that: The voltage fluctuation impact assessment coefficient CMP for each generated load device i and voltage fluctuation acceleration index CAL i Construct a voltage fluctuation impact assessment model and generate the impact assessment coefficient IEC of each load device through weighted summation i , the specific calculation formula is as follows: IEC i =ω1*CMP i +ω2*CAL i Where ω1 and ω2 are the voltage fluctuation impact assessment coefficients CMP of each load device respectively. i and voltage fluctuation acceleration index CAL i The non-zero weight coefficient of , and ω1+ω2=1; The impact assessment coefficient IEC of each load device i Conduct comprehensive analysis and generate comprehensive evaluation coefficient CEC according to the formula:

6. The method for constructing a comprehensive indicator system for distribution network based on dynamic characteristics evaluation according to claim 5, characterized in that: The generated comprehensive evaluation coefficient CEC is compared with the pre-set comprehensive evaluation coefficient threshold CEC yuzhi A comparison was conducted and the impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability was evaluated based on the comparison results. The impact was divided into low impact and high impact. The specific comparison analysis is as follows: If CEC≤CEC yuzhi ,The impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability is low; If CEC>CEC yuzhi The impact of simultaneous start and stop of multiple load devices on voltage fluctuation stability is high.

7. The method for constructing a comprehensive indicator system for distribution network based on dynamic characteristics evaluation according to claim 6, characterized in that: If the assessment result is a high impact, determine the pre-set impact assessment coefficient threshold range [IEC min , IEC max ] and after determination, generate the impact assessment coefficient IEC of each load device i Compare and classify each load device into major influencing devices, minor influencing devices and normal devices according to the comparison results. The specific classification is as follows: If IEC i <IEC min , classify the load equipment as normal equipment; If IEC min ≤IEC i ≤IEC max , classify the load equipment as secondary impact equipment; If IEC max >IEC i , classify the load equipment as the main influencing equipment.

8. The method for constructing a comprehensive indicator system for distribution network based on dynamic characteristics evaluation according to claim 7, characterized in that: According to the classification results of each load equipment, corresponding measures are taken for the main affected equipment, secondary affected equipment and normal equipment respectively, specifically: For load devices classified as normal, the following measures are taken: maintain the existing voltage regulation strategy, do not make additional interventions, and continuously monitor their voltage changes and operating status. If there is a trend of increasing voltage fluctuations, prioritize reducing voltage fluctuation intervention on normal devices to ensure their normal operation while monitoring their impact on the power grid in real time. For load devices classified as minor impact devices, the following measures are taken: dynamically adjust their voltage control strategies based on real-time voltage fluctuation data and the operating status of the load devices; adjust the power allocation and start-stop strategies of the devices to mitigate the impact of voltage fluctuations; and regularly evaluate their voltage fluctuation stability to optimize the voltage control process. For load devices that are classified as major influencing devices, the measures taken are: give priority to emergency voltage regulation measures, reduce the impact of voltage fluctuations on the power grid by allocating stable voltage resources and adjusting equipment start-up and shutdown strategies; monitor the voltage fluctuations and load changes of the equipment in real time, conduct in-depth analysis and optimization of the operating status of high-impact equipment, and dynamically adjust the voltage control model to reduce its negative impact on power grid stability.

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