Multi-scale adjustment demand evaluation method and system adaptive to load characteristics of mountainous area

Abstract

The invention discloses a multi-scale adjustment demand evaluation method and system adaptive to mountainous area load characteristics, and the method comprises the steps: obtaining the voltage data of a mountainous area power grid, carrying out the preprocessing of the voltage data through different time scales, and obtaining different types of load data samples under multiple time scales; constructing a load characteristic description model of each type of load unit based on the load data sample, and integrating power system data to establish a power distribution network structure topology model; and the load characteristic description model and the network structure topology model are combined to evaluate the power grid regulation requirements of different time scales. According to the method, the operation data of the power grid are accurately acquired, a load characteristic description model of multiple types of units is made based on historical load fluctuation information, the power loss and voltage optimization results of the power grid are comprehensively considered, node power adjustment demand evaluation results are given in different time scales, and reliable data support is provided for subsequent operation adjustment of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid regulation, and in particular to a multi-scale regulation demand assessment method and system adapted to mountainous area load characteristics. Background Art

[0002] The load of mountain power grid presents significant complexity and multi-dimensional characteristics, including residential load, small industrial load and other different types. Its complex and changeable load characteristics bring severe challenges to the stability and reliability of the power grid. Therefore, it is very important to monitor the operation status of the power grid in real time, deal with voltage fluctuations in time and realize rapid power regulation. In recent years, in the field of power grid regulation, the research and application of multi-time scale coordinated control in power system has received extensive attention and continuous development. This control method achieves the best overall performance by coordinating and optimizing the dispatching of different time scales. Among them, long-term dispatching mainly focuses on energy combination and power plant planning, planning the layout of power supply from a macro level; medium-term dispatching involves the economic dispatching of generators and the adjustment of transmission network operation mode to ensure the reasonable allocation of power generation resources and the economy and flexibility of power grid operation; short-term dispatching focuses on real-time generator output control and voltage control to cope with immediate changes in power demand and voltage fluctuations. Through this coordinated optimization, it aims to more effectively manage the operation and decision-making problems of the power system at different time scales to ensure the efficient operation, stability and reliability of the system. At the same time, the development of smart grids has promoted the research on multi-time scale coordinated control to adapt to the integration of new technologies such as distributed energy, electric vehicles and demand response. By integrating the above-mentioned new technical means, the technical effectiveness of multi-time scale collaborative dispatching and control strategies will be further enhanced.

[0003] On the other hand, from the perspective of grid structure characteristics, due to the geographical environment, the line extension distance of mountain power grids is long, which causes large line losses in the process of power transmission. This phenomenon not only weakens the effective utilization rate of electric energy, but also leads to a significant increase in the operation cost of the power grid. In addition, the weak power grid in mountainous areas has a large reactance value. When the load increases or decreases suddenly, it is easy to cause node voltage fluctuations. This fluctuation phenomenon will have a negative impact on the stability of the power grid and threaten the safe, stable and reliable operation of the power grid system. Therefore, in the process of implementing power regulation operations, voltage over-limit constraints must be taken into consideration to ensure the safe and stable operation of the power grid and maintain the normal function of the power system and the quality of power supply. However, most of the existing multi-time scale coordinated control strategies suitable for urban power grids adopt the method of superimposing the short-term prediction results of the power system, and directly realize the rolling superposition of multi-time operation capabilities based on the current operation status of all units. When such strategies are applied to mountainous power grids with large line losses and voltage fluctuations, regulation deviations may occur. In view of this, it is urgent to build a multi-time scale power regulation demand assessment model that comprehensively considers the network loss and voltage optimization results of the power grid, and equip it with a high-precision power grid data monitoring device to meet the needs of load management and stable operation of mountain power grids. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a multi-scale regulation demand assessment method and system adapted to mountain load characteristics to solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a multi-scale regulation demand assessment method adapted to mountainous area load characteristics, comprising: obtaining voltage data of a mountainous area power grid, preprocessing the voltage data at different time scales, and obtaining different types of load data samples at multiple time scales;

[0009] Based on the load data samples, load characteristic description models of various types of load units are constructed, and power system data are integrated to establish a distribution network structure topology model;

[0010] The load characteristic description model and the network structure topology model are combined to evaluate the power grid regulation requirements at different time scales.

[0011] As a preferred solution of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics of the present invention, the voltage data is preprocessed at different time scales to obtain different types of load data samples at multiple time scales, including:

[0012] Performing power detection on the voltage data based on a first time scale to extract current impact characteristic information to monitor power changes in the power system;

[0013] The second time scale power data window is updated by using the mean frequency reduction method, the second time scale power detection is performed, and the power fluctuation characteristics and change trends are extracted;

[0014] Based on the detection data of the first time scale and the second time scale, the overall power of the distribution network is analyzed in units of the third time scale to obtain the change trend characteristics of the load within a preset time range, so as to obtain different types of load data samples under multiple time scales.

[0015] As a preferred solution of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics of the present invention, wherein: constructing a load characteristic description model of each type of load unit based on the load data sample includes: based on the power data samples of the different types of loads at multiple time scales, using a clustering algorithm to classify the load data, and obtaining characteristic parameters of the power data samples;

[0016] A characteristic vector of each cluster is constructed according to the characteristic parameters, and a load characteristic description model of each type of load unit is constructed based on the characteristic vector. The input of the model is the characteristic vector, and the output is the load type.

[0017] As a preferred solution of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics described in the present invention, wherein: integrating power system data to establish a distribution network network structure topology model includes: integrating power grid electrical parameter data, operating status and spatial data to establish a distribution network network structure topology model;

[0018] The distribution network network structure topology model updates the network topology information according to the key factors of the distribution network to ensure that the distribution network network structure topology model is consistent with the actual operating state of the distribution network.

[0019] As a preferred solution of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics described in the present invention, wherein: combining the load characteristic description model and the network structure topology model to assess the grid regulation demand at different time scales includes: presetting voltage fluctuation upper and lower limit constraints according to the grid voltage fluctuation coefficient based on the network topology information and the load data of each node;

[0020] If the voltage does not meet the constraint conditions of the node, the required reactive compensation amount or active power adjustment amount is calculated to determine the reactive compensation demand and active power regulation demand of the power grid;

[0021] Based on the regulation demand, by changing the load data, repeating the flow calculation and regulation demand analysis, the regulation characteristics of the power grid under different working conditions are obtained.

[0022] As a preferred solution of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics of the present invention, it also includes:

[0023] Calculate the data obtained by power detection at the first time scale to obtain a sensitivity coefficient between the load power change and the voltage change;

[0024] Extract load change data, calculate voltage change rate and voltage amplitude, and obtain the required reactive power compensation or active power adjustment to determine the second-level reactive power compensation demand and active power regulation demand of the power grid.

[0025] As a preferred solution of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics of the present invention, it further includes: based on the historical data of the second time scale and the real-time monitoring data, according to the load characteristics of each type of unit, calculating the load fluctuation rate and the load peak-to-valley difference rate of adjacent second time scales to determine the short-term load change trend;

[0026] The power-voltage sensitivity coefficient of the second time scale is calculated based on the short-term load change trend, the voltage change is calculated according to the load change, and the power regulation demand required in the second time scale is calculated in combination with the fast power flow analysis method;

[0027] Obtain the load data of each node at the third scale to predict the load peak and valley conditions in different time periods, and evaluate the regulation demand at the third time scale based on the power flow distribution and dynamic change characteristics of the transmission line under different load conditions.

[0028] In a second aspect, the present invention provides a multi-scale regulation demand assessment system adapted to mountainous area load characteristics, comprising: a data acquisition processing module, for acquiring voltage data of a mountainous area power grid, preprocessing the voltage data at different time scales, and obtaining different types of load data samples at multiple time scales;

[0029] A model building module, used to build load characteristic description models of various types of load units based on the load data samples, and integrate power system data to build a distribution network network structure topology model;

[0030] An evaluation module is used to combine the load characteristic description model and the network structure topology model to evaluate the power grid regulation requirements at different time scales.

[0031] In a third aspect, the present invention provides an electronic device, comprising:

[0032] Memory and processor;

[0033] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics are implemented.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, can implement the steps of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics.

[0035] Compared with the prior art, the present invention has the following beneficial effects: the present invention accurately obtains grid operation data, makes load characteristic description models for multiple types of units based on historical load fluctuation information, comprehensively considers grid losses and voltage optimization results, and gives node power regulation demand assessment results within different time scales, providing reliable data support for subsequent grid operation adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0037] Figure 1 A schematic diagram of a method flow of a multi-scale regulation demand assessment method and system adapted to mountainous area load characteristics according to an embodiment of the present invention;

[0038] Figure 2 This is an example diagram of a method flow of a multi-scale regulation demand assessment method and system adapted to mountainous area load characteristics according to an embodiment of the present invention;

[0039] Figure 3A 24-hour load curve diagram of a load point on a certain day in a multi-scale regulation demand assessment method and system adapted to mountainous area load characteristics according to an embodiment of the present invention;

[0040] Figure 4 A load curve diagram of a load point at 12:00-13:00 of a multi-scale regulation demand assessment method and system adapted to mountainous area load characteristics according to an embodiment of the present invention;

[0041] Figure 5 A load curve diagram of a certain load point 12:10:00-12:14:00 of a multi-scale regulation demand assessment method and system adapted to mountainous area load characteristics according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0045] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0046] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] Example 1

[0049] Reference Figure 1-2 , is an embodiment of the present invention, which provides a multi-scale regulation demand assessment method adapted to mountainous area load characteristics, including:

[0050] S100: Acquire voltage data of the mountain power grid, pre-process the voltage data at different time scales, and obtain different types of load data samples at multiple time scales;

[0051] In the embodiment of the present application, the voltage data is preprocessed at different time scales to obtain different types of load data samples at multiple time scales, including:

[0052] Performing power detection on voltage data based on the first time scale and extracting current impact characteristic information to monitor power changes in the power system;

[0053] The second time scale power data window is updated by using the mean frequency reduction method, the second time scale power detection is performed, and the power fluctuation characteristics and change trends are extracted;

[0054] Based on the detection data of the first time scale and the second time scale, the overall power of the distribution network is analyzed in units of the third time scale to obtain the change trend characteristics of the load within a preset time range, so as to obtain different types of load data samples under multiple time scales.

[0055] It should be noted that the voltage data of the mountain power grid is obtained by installing high-precision voltage sensors at key locations such as substation outlets, line branch points, and important load access points to monitor and obtain voltage amplitude and phase information in real time, and using a LoRa communication module with long-distance communication function and low power consumption to transmit the collected voltage data to the main station system, ensuring the measurement accuracy and stability of voltage data collection in the complex environment of mountainous areas.

[0056] It should be noted that, in the embodiment of the present application, the first time scale may be a time scale of seconds, the second time scale may be a time scale of minutes, and the third time scale may be a time scale of hours.

[0057] Exemplarily, high-precision power monitoring equipment and sensors are used to perform power detection at a time scale of seconds. In each independent sampling cycle, a digital signal processor or a field programmable gate array is used to calculate and extract various types of current impact characteristic information contained in the current data window corresponding to the current sampling cycle, including current change rate, current peak value, and current pulse width. At the same time, on the basis of considering a certain safety margin, according to the actual operation experience and equipment characteristics of the mountain power grid, the starting current change rate of large units or the change rate of grid fault current is set as the current change rate threshold; according to the rated parameters of the power grid equipment, the rated current of the equipment is multiplied by a certain coefficient as the current peak threshold, and the current pulse width of the equipment when it is normally started is set as the current pulse width threshold. Whether there is a rapid change in load is detected based on whether the relevant current information exceeds the threshold. This detection technology can significantly improve the accuracy and timeliness of power system monitoring and analysis of power changes at a time scale of seconds.

[0058] Exemplarily, the mean frequency reduction method is used to update the minute-level power data window to perform power detection at the minute-level time scale, and the power standard deviation and power change rate of the mean frequency reduction power collected within several consecutive minutes are calculated. The power fluctuation severity is evaluated based on the calculated power standard deviation, and the number of positive and negative changes in the power change rate over a period of time is counted to evaluate the power fluctuation frequency, thereby extracting the power fluctuation characteristics. At the same time, by drawing the power change curve over time after mean frequency reduction and fitting the power data using analysis methods such as linear regression, the power change trend is quantitatively analyzed.

[0059] It should be noted that the above method not only ensures the accuracy of minute-level power detection, but also significantly improves the data processing efficiency, providing a reliable data basis and key technical support for the real-time evaluation of the operating status of the power system and the optimal allocation of power resources.

[0060] For example, based on the load data obtained by high-precision second-level power detection and minute-level power detection, the second-level and minute-level power data are summarized according to the weights to obtain the overall power of the distribution network in hours. Further, the moving average method is used to smooth the power data, and a curve of hour-level power changes over time is drawn to obtain the long-term change trend of the load; the fast Fourier transform method is used to analyze the power data, and the peak value of the power data spectrum is used to determine whether there is a periodic law in the load change; by calculating the correlation coefficient between the load power and external environmental factors such as temperature and humidity, and calculating the cross-correlation function of the load in different regions, the correlation between the load and external factors and the coordinated change of the load are determined.

[0061] S200: constructing load characteristic description models of various types of load units based on load data samples, and integrating power system data to establish a distribution network structure topology model;

[0062] In an embodiment of the present application, constructing a load characteristic description model of each type of load unit based on load data samples includes: based on power data samples of different types of loads at multiple time scales, using a clustering algorithm to classify the load data and obtain characteristic parameters of the power data samples;

[0063] The characteristic vector of each cluster is constructed according to the characteristic parameters, and the load characteristic description model of each type of load unit is constructed based on the characteristic vector. The model input is the characteristic vector and the output is the load type.

[0064] In an optional embodiment, after collecting and processing load data at different time scales, the embodiment of the present application uses the K-Means clustering algorithm to classify the pre-processed data, and determines the number of clusters K by analyzing the characteristics of different types of load data and business needs. For residential loads, based on the obvious differences in their electricity consumption behaviors in different seasons and time periods, K=4 is preliminarily determined (such as winter heating electricity consumption mode, summer cooling electricity consumption mode, weekday daily electricity consumption mode, and holiday electricity consumption mode). For agricultural irrigation loads, considering the different irrigation needs of crops and weather influences, K=3 is preliminarily set (such as drought irrigation mode, normal irrigation mode, and small amount of irrigation mode in rainy season). For small-scale processing and manufacturing loads, different K values ​​are set according to different production processes and scales. After obtaining the clustering results through iterative calculations, the average power is calculated:

[0065]

[0066] Where N is the number of data points in the cluster, and Pi is the power value of the i-th data point;

[0067] Power fluctuation range R = P max -P min, the distribution of the time points when the power in the cluster reaches the peak is statistically analyzed to determine the peak power occurrence time, and then the key characteristic parameters of the cluster load are extracted. Finally, the load characteristic description model of each type of load unit is constructed based on the extracted characteristic parameters to provide data support and technical guarantee for load forecasting, operation and dispatch optimization of the power system.

[0068] In an optional embodiment, the load characteristic description model may also include other relevant information.

[0069] It should be noted that the characteristic parameters in the present application at least include average power, power fluctuation range, power peak occurrence time and power variance.

[0070] It should also be noted that training the load characteristic description model enables the model to automatically identify the load type or predict the load characteristics based on the input feature vector, and then obtain the load characteristic description model of each type of load unit, which can provide data basis and technical support for load forecasting, operation and dispatch optimization, and regulation demand assessment of the power system.

[0071] In the embodiment of the present application, integrating the power system data to establish the distribution network network structure topology model includes: integrating the power grid electrical parameter data, operating status and spatial data to establish the distribution network network structure topology model;

[0072] The distribution network network structure topology model updates the network topology information according to the key factors of the distribution network to ensure that the distribution network network structure topology model is consistent with the actual operating status of the distribution network.

[0073] In an optional embodiment, real-time electrical parameter data such as voltage, current, and power are obtained through a power grid monitoring system, power grid operation status information such as switch status and equipment operation parameters are obtained through a dispatching automation system, and spatial data such as the geographical location, topography, and topography of lines and towers are obtained through a geographic information system. A network structure topology model of the distribution network is established in combination with a graph theory algorithm; intelligent inspection equipment is used to inspect and collect data on key elements such as the actual line direction and tower position of the distribution network, and compare them with the original topology model. By matching key information such as GPS coordinates and tower numbers, the parts that need to be updated are determined, and then the tower position, line direction, and other information are updated according to the new data. The properties of the nodes and edges in the network structure topology model are adjusted accordingly, and the line length and connection relationship related to the tower are recalculated to ensure that the model is always consistent with the actual operation status of the distribution network.

[0074] S300: Combine the load characteristic description model and the network structure topology model to evaluate the grid regulation requirements at different time scales.

[0075] In the embodiment of the present application, combining the load characteristic description model and the network structure topology model to evaluate the grid regulation requirements at different time scales includes: presetting the upper and lower limit constraints of voltage fluctuation according to the grid voltage fluctuation coefficient based on the network topology information and the load data of each node;

[0076] If the voltage does not meet the constraint conditions of the node, the required reactive compensation amount or active power adjustment amount is calculated to determine the reactive compensation demand and active power regulation demand of the power grid;

[0077] Based on the regulation demand, by changing the load data, repeating the flow calculation and regulation demand analysis, the regulation characteristics of the power grid under different working conditions are obtained.

[0078] In an optional embodiment, based on the accurately acquired network topology information and the collection and integration of the load data of each node, the voltage fluctuation coefficient that meets the stable operation requirements of the power grid is determined according to the short-circuit capacity, load characteristics and other factors of the power grid, and then the upper and lower limits of the voltage fluctuation are set. Further, the variable step power flow calculation method is used to analyze the voltage level and power distribution of each node according to the power flow calculation results. For nodes whose voltage does not meet the constraint conditions, the required reactive compensation amount or active power adjustment amount is calculated to determine the reactive compensation demand and active power regulation demand of the power grid. At the same time, the changing law of the regulation demand of the power grid under different load levels and operating modes is analyzed. By changing the load data, the power flow calculation and regulation demand analysis are repeated to obtain the regulation characteristics of the power grid under different working conditions, which provides solid technical support and data basis for the precise regulation, optimized operation and safe and stable guarantee of the power grid, and effectively improves the ability of the power grid to cope with complex working conditions and multi-load changes.

[0079] For example, based on power collection, combined with the load characteristic description model, network structure topology model, and voltage fluctuation constraints, a variable-step power flow calculation method is used to evaluate the power regulation demand. After establishing a network structure topology model, accurately describing the connection relationship between each node in the power grid, such as power generation nodes, load nodes, and substation nodes, as well as line parameters such as resistance, reactance, conductance, and susceptance) and transformer parameters (ratio, short-circuit impedance, etc.), a variable-step Newton-Raphson method is used to iteratively solve the node power equation. According to the power change rate indicator Adjust the step size. When the power change rate is less than the set threshold, it means that the system power changes slowly. You can increase the step size h. k+1 =αh k (α>1 is the step length magnification factor), otherwise the step length h is reduced k+1 =βh k(0<β<1 is the step size reduction coefficient). In each iteration, the Jacobian matrix and the unbalanced value are calculated according to the current voltage phase angle and amplitude approximation, and the iterative formula is solved to obtain the correction value of the voltage phase angle and amplitude. The voltage phase angle and amplitude approximation are updated according to the calculation step size, thereby improving the accuracy and efficiency of the calculation.

[0080] In the embodiment of the present application, it also includes:

[0081] Calculate the data obtained by power detection at the first time scale to obtain a sensitivity coefficient between the load power change and the voltage change;

[0082] Extract load change data, calculate voltage change rate and voltage amplitude, and obtain the required reactive power compensation or active power adjustment to determine the second-level reactive power compensation demand and active power regulation demand of the power grid.

[0083] Exemplarily, based on the load data obtained by high-precision second-level power detection, the power regulation demand within the second time scale is evaluated. Through statistical analysis of massive historical data or based on power grid flow calculation, the sensitivity coefficient between the load power change and the voltage change is obtained, and the load data such as power change rate and power peak and valley values ​​are extracted to calculate the voltage change rate and voltage amplitude, and then the required reactive power compensation or active power adjustment is obtained through fast power flow calculation to determine the second-level reactive power compensation demand and active power regulation demand of the power grid.

[0084] In the embodiment of the present application, it also includes: based on the historical data of the second time scale and the real-time monitoring data, according to the load characteristics of each type of unit, calculating the load fluctuation rate and the load peak-to-valley difference rate of adjacent second time scales to determine the short-term load change trend;

[0085] The power-voltage sensitivity coefficient of the second time scale is calculated based on the short-term load change trend, the voltage change is calculated according to the load change, and the power regulation demand required in the second time scale is calculated in combination with the fast power flow analysis method;

[0086] Obtain the load data of each node at the third scale to predict the load peak and valley conditions in different time periods, and evaluate the regulation demand at the third time scale based on the power flow distribution and dynamic change characteristics of the transmission line under different load conditions.

[0087] In an optional embodiment, on a minute-level time scale, based on the load characteristics of each type of unit, its historical data and real-time monitoring data are used to calculate the minute-level load fluctuation rate and load peak-to-valley difference rate, so as to measure the severity of load fluctuations and extreme load changes between adjacent minutes. The short-term load change trend is comprehensively considered and the power-voltage sensitivity coefficient at the minute level is calculated. The voltage change is calculated based on the load change. Combined with the fast power flow analysis method, the power regulation demand parameters and range required within the minute-level time scale are calculated, providing key technical support and reliable data basis for the stable and efficient operation of the power system.

[0088] In an optional embodiment, on an hourly time scale, by analyzing the data accumulated over a long period of time and integrated by the system at each node, taking into account the periodicity of the load, the correlation between the load and external factors, and the coordinated changes in the load, a differential autoregressive moving average model is used to predict the peak and valley conditions of the load at different time periods. Comprehensively consider the power flow distribution and its dynamic change characteristics of the transmission line under different load conditions, and evaluate the regulation needs such as power adjustment, reactive power compensation, and optimization of the grid operation mode on an hourly time scale. It can effectively improve the control efficiency and scientific decision-making of the power system in response to load changes and the complexity of power flow distribution on an hourly time scale.

[0089] For example, based on the results of the power flow equation and combined with the load characteristic data, a multi-scale power regulation demand assessment is performed. For the second-level power regulation demand assessment, based on the actual active power P of the current node, s,i The expected active power P determined by the actual operation demand and stability target of the power grid s,i,des , calculate the second-level active power regulation demand ΔP s,i,req =P s,i,des -P s,i Similarly, according to the actual reactive power Q s,i and the desired reactive power Q to maintain voltage stability s,i,des , calculate the reactive power regulation demand ΔQ in seconds s,i,req =Q s,i,des -Q s,i Similarly, considering the impact of load fluctuations at the minute level on the voltage stability and power balance of the power grid, the minute-level active power regulation demand ΔP is calculated. s,i,req and reactive power regulation demand ΔQ s,i,req ; Consider the peak and valley changes of load, the adjustment of power generation plans in different periods of time, and the power regulation measures required to maintain the long-term stable operation of the power grid, and calculate the hourly active power ΔP s,i,req and reactive power regulation demand ΔQ s,i,req .

[0090] The above is a schematic scheme of a multi-scale regulation demand assessment method adapted to mountainous area load characteristics of this embodiment. It should be noted that the technical scheme of the multi-scale regulation demand assessment system adapted to mountainous area load characteristics and the technical scheme of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics described above belong to the same concept. For details not described in detail in the technical scheme of the multi-scale regulation demand assessment system adapted to mountainous area load characteristics in this embodiment, please refer to the description of the technical scheme of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics described above.

[0091] In this embodiment, a multi-scale regulation demand assessment system adapted to mountainous area load characteristics includes:

[0092] The data acquisition and processing module is used to obtain the voltage data of the mountain power grid, pre-process the voltage data at different time scales, and obtain different types of load data samples at multiple time scales;

[0093] Model building module, used to build load characteristic description models of various types of load units based on load data samples, and integrate power system data to build a distribution network structure topology model;

[0094] The evaluation module is used to combine the load characteristic description model and the network structure topology model to evaluate the grid regulation needs at different time scales.

[0095] This embodiment further provides an electronic device, which is applicable to the multi-scale regulation demand assessment method adapted to mountainous area load characteristics, and includes:

[0096] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the multi-scale regulation demand assessment method adapted to mountain load characteristics as proposed in the above embodiment.

[0097] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the multi-scale regulation demand assessment method for adapting to mountain load characteristics as proposed in the above embodiment is implemented.

[0098] The storage medium proposed in this embodiment and the multi-scale adjustment demand assessment method proposed in the above embodiment to achieve adaptation to mountain load characteristics belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0099] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0100] Example 2

[0101] Reference Figure 1-2 , is an embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a multi-scale regulation demand assessment device adapted to mountainous area load characteristics and a multi-scale regulation demand assessment method adapted to mountainous area load characteristics.

[0102] Since mountain power grids have the characteristics of long transmission lines, large line reactance values, and complex and changeable load characteristics, mountain power grids have significant line loss problems. When the load suddenly increases or decreases, it is very easy to cause node voltage fluctuations, posing a serious threat to the safe, stable and reliable operation of the power system. Based on this, the device of the present application uses a high-precision power system operation data detection device to simultaneously construct a variety of power grid data information description models. In particular, according to the line characteristics of mountain power grids, a multi-time scale power regulation demand assessment model that comprehensively considers the power grid loss and voltage optimization results is constructed. It can accurately give the power regulation demand assessment results of each node in the power grid within a multi-time scale range, lay a solid and reliable data foundation for the subsequent efficient operation and adjustment of the power grid, and effectively promote the smoothing of power grid fluctuations and ensure the stability of power system operation.

[0103] The core points of the device of the present application focus on the accurate detection of multiple types of loads in mountainous areas and the in-depth analysis and evaluation of power regulation requirements. On the one hand, in view of the diversity and complexity of the load of the power grid in mountainous areas, the device of the present application uses a multi-time scale power detection device to accurately collect the load data of each node of the power grid within the time scale of seconds, minutes, and hours, and uses a clustering algorithm to classify the data according to the similarity between the data, so as to construct a load characteristic description model of each type of load unit, thereby achieving an accurate characterization of the complex load characteristics of mountainous areas. On the other hand, in view of the characteristics of long transmission lines and large reactance values ​​of mountainous power grids, the device of the present application comprehensively considers the power grid flow characteristics and voltage over-limit risk constraints, and implements an accurate evaluation of power regulation requirements based on the load data of each node within the time scale of seconds, minutes, and hours, so as to effectively smooth the node voltage fluctuations, effectively ensure the stability and safety of power grid operation, and provide a solid technical support and guarantee mechanism for the intelligent and efficient operation of mountain power grids.

[0104] Specifically, the device of the present application includes a high-precision voltage monitoring module, a multi-time scale power detection device, a load characteristic description model of various types of load units, a network structure topology model, and a multi-time scale power regulation demand assessment model. The high-precision voltage monitoring module monitors and stably transmits voltage data in real time by installing high-precision voltage sensors and LoRa communication modules at key locations, providing an accurate data basis for subsequent evaluation. The multi-time scale power detection device can perform power detection at the second, minute, and hour levels, and uses methods such as mean frequency reduction to update the data window and accurately capture the characteristics of load changes. Among them, it detects rapid changes in load at the second level, analyzes power fluctuation trends at the minute level, and grasps long-term change trends at the hour level.

[0105] Based on the detection data, a load characteristic description model is constructed through a clustering algorithm, and a network structure topology model is established by integrating system data. Combining these models, the multi-time scale power regulation demand assessment model can evaluate the power regulation demand at different time scales based on accurate network topology and load data, taking into account voltage fluctuation constraints, and using a variable step-size power flow calculation method based on the characteristics of power grid regulation demand, including evaluating regulation demand based on voltage impact at the second level, analyzing the impact of load changes on voltage to determine the range of regulation parameters at the minute level, and evaluating power adjustment at the hour level based on comprehensive multi-factors, etc., effectively improving the power grid's ability to cope with complex working conditions and load changes, and providing key technical support for the intelligent and efficient operation of mountain power grids.

[0106] Example 3

[0107] Reference Figure 3 to Figure 5 , is an embodiment of the present invention, and this embodiment verifies the feasibility and beneficial effects of the solution through exemplary examples.

[0108] This embodiment divides the power collection of the power grid into three time scales: seconds, minutes, and hours. High-precision voltage transformers (PT), current transformers (CT), smart meters and other sensor devices are deployed at key locations of the power grid, such as substations, transmission line branch points, and important load access points. At the same time, high-speed sampling rate sensors and data acquisition cards are used to ensure that rapidly changing signals can be accurately captured. For data collected in seconds, according to the instantaneous sampling values ​​of voltage and current, the instantaneous power formula is used for calculation and expression:

[0109] p(t)=u(t)i(t)

[0110] At the same time, by the finite difference method:

[0111]

[0112] Approximately calculate the rate of change of power, monitor the rapid fluctuation of power in real time, and determine whether there is a sudden change in load or a fault event. At the minute level, the collected data is processed by sliding average or by using methods such as mean frequency reduction to obtain a minute-level power data series. By analyzing the statistical characteristics of these data series, such as calculating the average, standard deviation, maximum, and minimum values ​​of power, the fluctuation characteristics and change trends of power within the minute time range are evaluated. Finally, the overall power of the power grid is systematically summarized and analyzed in hours to explore the daily and weekly changes in load and seasonal trends.

[0113] Reference Figures 3 to 5 The load curves of a load point at three time scales, namely, second, minute, and hour, are presented on a certain day. The minute-level curve focuses on the load changes at the load point from 12:00 to 13:00, while the second-level curve shows the load dynamics at the load point from 12:10 to 12:14 in detail. Figure 3 The load curve at the hourly time scale shows that the load demand is low from 1 a.m. to 7 a.m. At this time, an energy storage system can be set up as a load to store electricity to raise the load curve. In the period from 9 a.m. to 6 p.m., there is a peak in electricity consumption. In order to ensure the stability and reliability of power supply, it is necessary to set up active power compensation measures to balance the power supply and demand relationship. Figure 4The load curve at the minute time scale can be observed to have a sudden increase in load between 12:11 and 12:13, while the load changes were relatively stable in the rest of the time. In view of this situation, in order to maintain the stable operation of the power system, it is necessary to increase the output of the generator set in time at the load sudden increase point to meet the instantaneous increase in power demand, prevent voltage fluctuations, frequency drops and other adverse consequences caused by insufficient power supply, and ensure that the power system can continue to operate stably under different load conditions. Figure 5 From the load curve depicted on a time scale of seconds, it can be seen that an impact load occurs between 12:11:00 and 12:11:10, with a load peak of 89MW and a power change rate of 5MW / s. The power supply capacity on the generation side needs to be optimized to achieve a rapid response mechanism.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-scale regulation demand assessment method adapted to mountainous area load characteristics, characterized in that: include: Acquire voltage data of a mountain power grid, pre-process the voltage data at different time scales, and obtain different types of load data samples at multiple time scales; Based on the load data samples, load characteristic description models of various types of load units are constructed, and power system data are integrated to establish a distribution network structure topology model; The load characteristic description model and the network structure topology model are combined to evaluate the power grid regulation requirements at different time scales.

2. The multi-scale regulation demand assessment method adapted to mountainous area load characteristics according to claim 1, characterized in that: The voltage data is preprocessed at different time scales to obtain different types of load data samples at multiple time scales, including: Performing power detection on the voltage data based on a first time scale to extract current impact characteristic information to monitor power changes in the power system; The second time scale power data window is updated by using the mean frequency reduction method, the second time scale power detection is performed, and the power fluctuation characteristics and change trends are extracted; Based on the detection data of the first time scale and the second time scale, the overall power of the distribution network is analyzed in units of the third time scale to obtain the change trend characteristics of the load within a preset time range, so as to obtain different types of load data samples under multiple time scales.

3. The multi-scale regulation demand assessment method adapted to mountainous area load characteristics as claimed in claim 2, characterized in that: Constructing a load characteristic description model of each type of load unit based on the load data samples includes: based on the power data samples of the different types of loads at multiple time scales, using a clustering algorithm to classify the load data and obtain characteristic parameters of the power data samples; A characteristic vector of each cluster is constructed according to the characteristic parameters, and a load characteristic description model of each type of load unit is constructed based on the characteristic vector. The input of the model is the characteristic vector, and the output is the load type.

4. The multi-scale regulation demand assessment method adapted to mountainous area load characteristics as claimed in claim 3, characterized in that: Integrating power system data to establish a distribution network network structure topology model includes: integrating power grid electrical parameter data, operating status and spatial data to establish a distribution network network structure topology model; The distribution network network structure topology model updates the network topology information according to the key factors of the distribution network to ensure that the distribution network network structure topology model is consistent with the actual operating state of the distribution network.

5. The multi-scale regulation demand assessment method adapted to mountainous area load characteristics as claimed in claim 4, characterized in that: Combining the load characteristic description model and the network structure topology model to evaluate the grid regulation requirements at different time scales includes: presetting voltage fluctuation upper and lower limit constraints according to the grid voltage fluctuation coefficient based on the network topology information and the load data of each node; If the voltage does not meet the constraint conditions of the node, the required reactive compensation amount or active power adjustment amount is calculated to determine the reactive compensation demand and active power regulation demand of the power grid; Based on the regulation demand, by changing the load data, repeating the flow calculation and regulation demand analysis, the regulation characteristics of the power grid under different working conditions are obtained.

6. The multi-scale regulation demand assessment method adapted to mountainous area load characteristics as claimed in claim 5, characterized in that: Also includes: Calculate the data obtained by power detection at the first time scale to obtain a sensitivity coefficient between the load power change and the voltage change; Extract load change data, calculate voltage change rate and voltage amplitude, and obtain the required reactive power compensation or active power adjustment to determine the second-level reactive power compensation demand and active power regulation demand of the power grid.

7. The multi-scale regulation demand assessment method adapted to mountainous area load characteristics according to claim 6, characterized in that: Also includes: Based on the historical data of the second time scale and the real-time monitoring data, according to the load characteristics of each type of unit, the load fluctuation rate and the load peak-to-valley difference rate of adjacent second time scales are calculated to determine the short-term load change trend; The power-voltage sensitivity coefficient of the second time scale is calculated based on the short-term load change trend, the voltage change is calculated according to the load change, and the power regulation demand required in the second time scale is calculated in combination with the fast power flow analysis method; Obtain the load data of each node at the third scale to predict the load peak and valley conditions in different time periods, and evaluate the regulation demand at the third time scale based on the power flow distribution and dynamic change characteristics of the transmission line under different load conditions.

8. A multi-scale regulation demand assessment system adapted to mountainous area load characteristics, characterized in that: include: A data acquisition and processing module is used to acquire voltage data of the mountain power grid, pre-process the voltage data at different time scales, and obtain different types of load data samples at multiple time scales; A model building module, used to build load characteristic description models of various types of load units based on the load data samples, and integrate power system data to build a distribution network network structure topology model; An evaluation module is used to combine the load characteristic description model and the network structure topology model to evaluate the power grid regulation requirements at different time scales.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-scale regulation demand assessment method adapted to mountain load characteristics as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-scale regulation demand assessment method adapted to mountainous area load characteristics as described in any one of claims 1 to 7.

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