A method of energy distribution management based on regional resources

Abstract

The present application discloses an energy distribution management method based on regional resources, which relates to the technical field of distributed energy management, including: obtaining a two-dimensional map of a region, establishing a plane rectangular coordinate system, scaling the two-dimensional map, preliminarily dividing the region into a number of cells of equal size according to a preset area and calculating the area of ​​the scaled map, and dividing the region into a number of macro sub-regions according to a macro standard; dividing each macro sub-region into a plurality of micro regions according to a micro standard and setting a unique identification code for marking; setting an initial power supply terminal according to the macro sub-region and the micro region; through macro and micro regional division, achieving fine management of regional resources, improving the rationality and efficiency of energy distribution, and through precise regional division and power supply terminal setting, achieving the effect of accurately matching needs, improving overall energy utilization efficiency, and reducing energy loss.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy management, and in particular to an energy distribution management method based on regional resources. Background Art

[0002] Energy distribution management refers to the system that arranges the power generation system near the user in a small-scale, small-capacity, modular and decentralized manner, relative to the traditional centralized power supply method, and can independently output electricity, heat and cold energy.

[0003] The existing Chinese invention patent with application number 202311797020.1 discloses an energy distribution management method and system based on regional resources. The cells are divided according to the two-dimensional map of the area; the maximum load number is obtained according to the average sunshine duration; the transportation distance is calculated, and the power supply end of the load coordinates is selected based on the transportation distance and the maximum load number; by optimizing the power supply end selection, the energy loss during the transmission process is reduced.

[0004] However, the existing technology only determines the energy transmission loss by calculating the transportation distance and the maximum load number, and divides the map based on fixed unit grids and thresholds. It cannot accurately reflect the actual energy demand and power generation potential, resulting in losses in the overall regional energy distribution. Summary of the invention

[0005] This application solves the problem that the existing technology cannot accurately capture the energy supply and demand characteristics and power generation potential of each region by providing an energy distribution management method based on regional resources, and achieves the technical effect of refined regional division and improved energy allocation accuracy.

[0006] This application provides an energy distribution management method based on regional resources, including:

[0007] S100: obtaining a two-dimensional map of the region, establishing a plane rectangular coordinate system, scaling the two-dimensional map, preliminarily dividing the region into a number of cells of equal size according to a preset area, calculating the area of ​​the scaled map, and dividing the region into a number of macro sub-regions according to a macro standard;

[0008] S200: Divide each macro sub-region into multiple micro-regions according to micro-standards and set unique identification codes for marking; the micro-standards include two division standards: load density and photovoltaic power generation potential; calculate the load density and photovoltaic power generation potential of all cells in each macro sub-region to form a load potential distribution map; use a cluster analysis method to divide the load potential into several levels, divide it into multiple micro-regions according to different levels, and adjust the boundaries;

[0009] S300: setting the initial power supply end according to the macro sub-area and the micro area; calculating the total number of power supply ends required according to the load density of each micro area and the coverage of the power supply end, selecting the micro area for setting the power supply end, calculating the power supply distance, and generating an initial setting report;

[0010] Among them, in the process of dividing cells of equal size, the area located on the edge that is less than the preset cell size is delineated as a regional boundary, marked as an edge grid, the area of ​​the edge grid is determined, and then the similarity of each edge grid with all existing clusters is calculated in turn, and it is classified into the most similar cluster; the unique identification code includes the macro sub-region position of the micro region, its level and its position in the macro sub-region.

[0011] Furthermore, the macro standard includes two division standards: geographical location and illumination duration. The illumination similarity threshold and the location similarity threshold are pre-set, and a cell is selected as the initial cluster center. Cells similar to the cell in illumination duration and geographical location are grouped into the same cluster to form a macro sub-region.

[0012] Furthermore, S300 also includes: sorting all micro areas from high to low according to load density, setting a selection ratio, wherein the selection ratio is set according to the total number of required power supply terminals and the specific load density, and determining the number of micro areas in order according to the selection ratio; using the center position of the initially selected micro area as the potential power supply terminal position, and using the Euclidean distance formula to calculate the distance from the farthest boundary point of each micro area to the potential power supply terminal position.

[0013] Furthermore, the initial setup report includes generating an initial site selection map, marking the preliminarily selected power supply terminal location on the two-dimensional map, marking the load center and road information of each micro-area, and marking the coverage range and maximum transmission distance at each power supply terminal location.

[0014] Furthermore, the method also includes: S400: establishing a transmission loss model, calculating the actual loss values ​​under different transmission paths according to the coordinate positions of different boundary points, comparing and analyzing the transmission losses of different boundary points to obtain loss differences, setting a difference threshold, marking boundary points whose loss differences are greater than the difference threshold, adjusting the micro-area boundaries and re-evaluating the position of the power supply end.

[0015] Furthermore, the transmission loss model calculates the resistance loss and transformer loss generated when the uniform conductor is used as the transmission line. During the establishment process, the influence of external factors on the transmission loss is ignored, and a constant resistivity and conductor cross-sectional area are set.

[0016] Furthermore, the method also includes: S500: obtaining a loss stability value based on the actual loss values ​​of all micro areas in the macro sub-area, and setting a secondary identification label to distinguish the micro areas; comparing the actual loss value of each micro area based on the loss stability value to obtain a loss level characteristic value, and setting a classification standard based on the loss level characteristic value, loss level and load characteristics to obtain a secondary identification label.

[0017] Furthermore, step S500 also includes: establishing a prediction model for predicting photovoltaic power generation and load power consumption in a micro area in the future, and forming a three-level identification tag based on the prediction result; and designing a redundant backup mechanism based on the three-level identification tag.

[0018] Furthermore, the redundant backup mechanism is based on comparative evaluation of the three-level identification tags of each micro-area, and micro-areas are selected to form a complementary group as alternative power supply terminals.

[0019] Furthermore, step S500 also includes: obtaining the actual loss value of each micro-area in the complementary group at different boundary points, pre-setting a critical threshold, marking the boundary points where the actual loss value is greater than the critical threshold, connecting the marked points to form a critical area, transferring part of the load in the critical area to the adjacent non-critical area, and redefining the boundaries of the micro-area according to the results of the load transfer, and dynamically adjusting the boundaries of the micro-area according to real-time monitoring data.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] Through macro and micro regional divisions, we can achieve fine management of regional resources, improve the rationality and efficiency of energy distribution, and through precise regional division and power supply setting, we can accurately match demand, improve overall energy utilization efficiency and reduce energy loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure is a flow chart of an energy distribution management method based on regional resources in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0025] Embodiment 1: Figure 1 As shown, a method for energy distribution management based on regional resources, the method comprising:

[0026] S100: Obtain a two-dimensional map of the area, establish a plane rectangular coordinate system, scale the two-dimensional map, preliminarily divide the area into a number of equal-sized cells according to a preset area, calculate the area of ​​the scaled map, and divide the area into a number of macro sub-areas according to a macro standard.

[0027] The macro standard includes two division standards: geographical location and illumination duration. The illumination similarity threshold and the location similarity threshold are pre-set, and a cell is selected as the initial cluster center. Cells similar to the cell in terms of illumination duration and geographical location are grouped into the same cluster to form a macro sub-region.

[0028] In the process of dividing cells of equal size, the area on the edge that is less than the preset cell size is delineated as an edge grid, and the area of ​​the edge grid is determined. Then, the similarity between each edge grid and all existing clusters is calculated in turn, and they are classified into the most similar cluster.

[0029] According to the factors such as the duration of sunlight and geographical location in the data obtained by the GIS (Geographic Information System), the area is divided into several macro sub-areas, each of which has similar lighting and geographical location characteristics. The specific division process is as follows: Establish a plane rectangular coordinate system on the two-dimensional map, with the lower left corner of the map as the origin (0, 0), the X-axis horizontally to the right, and the Y-axis vertically upward. Scale the obtained two-dimensional map according to the preset ratio. For example, scale the map at a ratio of 1:1000, that is, every 1 unit length on the map represents an actual 1000 unit length. Suppose the maximum value of the scaled two-dimensional map on the X-axis is , the minimum value is ; The maximum value on the Y axis is , the minimum value is . Then the area of ​​the zoomed map is It can be calculated by the following formula: First, the scaled map is divided into cells of equal size, and the area of ​​the cell division is preset. Taking the origin as the starting point, the area on the edge that is less than the preset cell size is divided into regional boundaries and marked as edge grids. The size of each edge grid is calculated and compared with the preset error threshold. The error threshold is an area value. If the area of ​​the edge grid is less than the error threshold, it is directly included in the cluster closest to it; if the area of ​​the edge grid is not less than the error threshold, the similarity between the edge grid and all existing clusters is calculated and compared with the preset edge similarity threshold. If there is a similarity not less than the edge similarity threshold, it is included in the cluster with the highest similarity; if they are all less than the edge similarity threshold, a new cluster is created.

[0030] Using the sunshine duration information in GIS data, calculate the average sunshine duration for each cell , where i and j represent the index of the cell on the X-axis and Y-axis respectively; directly obtain the geographical location of each cell, that is, the longitude and latitude data information, and set the cells of equal size to facilitate comparison and then divide the macro sub-area, select the cell closest to the origin as the starting cell, take the starting cell as the starting point, compare all cells pairwise, and obtain the similarity of illumination duration and geographical location based on the preset illumination similarity threshold and location similarity threshold. If the difference in average sunshine duration between two cells is less than the illumination similarity threshold, they are considered to be similar in illumination conditions; if the difference in latitude and longitude between two cells are both less than the location similarity threshold, they are considered to be similar in geographical location.

[0031] S200: Divide each macro sub-region into multiple micro regions according to micro standards and set unique identification codes for marking; the micro standards include two division standards: load density and photovoltaic power generation potential. The load density and photovoltaic power generation potential of all cells in each macro sub-region are calculated to form a load potential distribution map. The load potential is divided into several levels using a cluster analysis method, and divided into multiple micro regions according to different levels and the boundaries are adjusted.

[0032] The unique identification code includes the position of the macro sub-region where the micro region is located, the grade to which it belongs, and the position in the macro sub-region.

[0033] In some embodiments, historical electricity consumption data in each macro sub-region is collected, including electricity consumption in different time periods (such as day, week, month, year), peak hours of electricity consumption, etc., and the light duration data in each macro sub-region is collected, including the light conditions in different seasons and under different weather conditions, and the rated power and power generation efficiency of the photovoltaic generator are obtained. The collected data is cleaned to remove outliers and missing values, and the data is normalized to ensure the comparability between different data. The load density is obtained by using the historical electricity consumption data, which is specifically obtained by calculating the electricity consumption per unit area or per unit population. According to the distribution of load density, the macro sub-region is divided into different load density levels; specifically, the historical electricity consumption data is sorted according to the spatial position, and for each cell in the macro sub-region, its load density, that is, the electricity consumption per unit area or per unit population, is calculated to form a load potential distribution map. According to the distribution of load potential, a cluster analysis method is used to divide the load potential into several levels, for example, the load potential is divided from low to high into low density area, medium density area, high density area and extremely high density area.

[0034] In some embodiments, the photovoltaic power generation potential in each macro sub-region is evaluated by combining the illumination duration data and the rated power of the photovoltaic generator; the macro sub-region is preliminarily divided into several micro regions according to the calculation results of the load density and the photovoltaic power generation potential. Regions with high load density are divided into smaller micro regions, and regions with low load density appropriately expand the scope of the micro regions. After the preliminary division, the boundaries of each micro region are identified, the location and shape of the boundaries are determined, the geographical factors (such as topography, landform, regional planning, etc.) and actual conditions (such as building distribution, road direction, etc.) of the boundaries are obtained, and the boundaries are adjusted. The purpose is to maintain the clarity and continuity of the boundaries and to be as consistent as possible with the geographical features and actual conditions; for places where the boundaries are unclear or unreasonable, GIS tools are used for smoothing, and the boundaries are locally adjusted according to the actual conditions to ensure the rationality and practicality of the micro regions. Finally, the micro-region division scheme within each macro-sub-region is determined, and each micro-region is uniquely marked. A unique identification code is set for marking according to the characteristics of the macro-sub-region, micro-region and the location, for example, Aa-1, where A represents the macro-sub-region, a represents the load density and photovoltaic power generation potential of the micro-region, and 1 represents the location of the micro-region.

[0035] S300: setting the initial power supply end according to the macro sub-area and the micro area; calculating the total number of power supply ends required according to the load density of each micro area and the coverage of the power supply end, selecting the micro area for setting the power supply end, calculating the power supply distance, and generating an initial setting report;

[0036] In some embodiments, the load density of each micro area can be obtained according to the load power consumption of each micro area and the area size of the micro area, and then the total load demand of the micro area is calculated according to the load power consumption, and the total number of power supply terminals required is calculated; the specific formula is: ,in, represents the load density, represents the load power consumption of each micro-area i, represents the area of ​​microscopic region i; ,in, represents the total load demand, represents the total number of microscopic regions; ,in, Indicates that each power supply end can provide a fixed maximum load. Indicates the total number of power supply terminals required, Indicates rounding up.

[0037] In some embodiments, S300 also includes: sorting all microscopic areas from high to low according to load density, setting a selection ratio, the selection ratio is set according to the total number of required power supply terminals and the specific load density, and determining the number of microscopic areas in order according to the selection ratio; using the center position of the initially selected microscopic area as the potential power supply terminal position, and using the Euclidean distance formula to calculate the distance from the farthest boundary point of each microscopic area to the potential power supply terminal position, the specific formula is: ,in, represents the coordinates of the farthest boundary point of microscopic region i, Represents the coordinates of potential power supply terminal j.

[0038] In some embodiments, the specific location of the power supply end is determined based on the calculated load density, power supply distance and construction cost accounting, and an initial setting report is generated. The initial setting report includes generating an initial site selection map, marking the preliminary selected power supply end location on the two-dimensional map, marking the load center and road information of each micro area, and marking the coverage range and maximum transmission distance at each power supply end location.

[0039] In this embodiment, the illumination duration and geographic location data obtained by the GIS geographic information system are used to set the illumination similarity threshold and the location similarity threshold, and the region is divided into macro sub-regions with similar illumination and geographic location characteristics through a clustering method; on the basis of the macro sub-regions, the two micro standards of load density and photovoltaic power generation potential are further subdivided into micro regions, and unique identification codes are set for marking; based on the load density and power supply terminal coverage of the micro region, the total number of required power supply terminals is calculated, and the potential location of the power supply terminal is determined by sorting and selecting the ratio, and the final power supply terminal location is further determined by calculating the power supply distance and construction cost accounting.

[0040] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0041] This application achieves fine management of regional resources through macro and micro regional divisions, improves the rationality and efficiency of energy distribution, and achieves accurate matching of needs, improved overall energy utilization efficiency, and reduced energy loss through precise regional division and power supply end settings.

[0042] Embodiment 2: In embodiment 1, fine management of regional resources is achieved through macro and micro regional divisions. This embodiment makes further improvements on the above basis.

[0043] The method also includes: S400: establishing a transmission loss model, calculating actual loss values ​​under different transmission paths according to the coordinate positions of different boundary points, comparing and analyzing the transmission losses of different boundary points to obtain loss differences, setting a difference threshold, marking boundary points whose loss differences are greater than the difference threshold, adjusting the micro-area boundaries and re-evaluating the position of the power supply end.

[0044] The transmission loss model calculates the resistance loss and transformer loss generated when a uniform conductor is used as a transmission line. During the establishment process, the influence of external factors on the transmission loss is ignored, and a constant resistivity and conductor cross-sectional area are set.

[0045] In some embodiments, the actual loss value obtained by the transmission loss model ignores the influence of external factors on the transmission loss, and is established under the assumption that the transmission line is a uniform conductor with a constant resistivity and conductor cross-sectional area. In the process of calculating the transmission loss, the line resistance loss and possible transformer loss are mainly included, and the calculation formula of the line resistance loss is: ,in, represents the line resistance loss, is the transmission current, is the line resistance per unit length, It is the actual transmission distance from the power supply end to the boundary point of the microscopic area; ,in, is the resistivity of the wire, is the cross-sectional area of ​​the conductor; the transformer loss is calculated as: ,in, is the transformer loss, and is the empirical coefficient, is the transmission current, is the transformer capacity; ,in, is the load demand of the micro area, is the output voltage at the power supply end; the total transmission loss is calculated as: ,in, It represents the error factor, which is set according to historical experimental data. To save calculation amount, it can be ignored.

[0046] In some embodiments, the coordinates of boundary points at several different positions in a microscopic area are randomly selected, and the actual transmission distance between the boundary point and the power supply end is calculated. According to the transmission loss model, the actual loss values ​​under different transmission paths are calculated, and the transmission losses of different boundary points are compared and analyzed to obtain the loss difference. A difference threshold is set in advance, and the boundary points with loss differences greater than the difference threshold are marked, and the coordinate position is recorded. The actual transmission loss of the coordinate position and the remaining power supply ends is calculated, and the coordinate position is divided into the microscopic area of ​​the power supply end with the lowest transmission loss. The boundary of the microscopic area is adjusted and the position of the power supply end is re-evaluated according to the actual transmission loss conditions of several different boundary points, and a position with smaller loss and wider coverage is selected as the new power supply end.

[0047] In some embodiments, the transmission losses of different boundary points are compared to find the coordinate points with significant loss differences. At the same time, the reasons for the loss differences are also analyzed. According to the loss analysis results, the boundaries of the micro-regions are adjusted. For example, the areas with larger losses are divided into adjacent micro-regions, or the boundaries are narrowed to reduce the transmission distance. Repeat the above iterations to optimize the boundaries of the micro-regions and the power supply end positions, and pre-set stop conditions, such as the maximum number of iterations, loss reduction ratio, etc. When the conditions are met, the final micro-region division and power supply end position plan is output.

[0048] In this embodiment, a transmission loss model is established to calculate the actual transmission loss, the coordinates of several boundary points at different positions in the micro area are randomly selected, and the actual transmission distances between these boundary points and the power supply end are calculated. The transmission loss model is used to calculate the actual loss values ​​under different transmission paths, and the transmission losses of different boundary points are compared and analyzed to obtain the loss difference. A difference threshold is set to mark those boundary points whose loss differences are greater than the difference threshold. According to the analysis results of the loss difference, the boundaries of the micro area are adjusted, the position of the power supply end is re-evaluated, and a position with smaller loss and wider coverage is selected as the new power supply end.

[0049] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0050] This application accurately calculates the actual loss values ​​under different transmission paths, compares and analyzes the loss differences, and adjusts the boundaries of micro-areas based on the analysis results of the loss differences, thereby achieving the effect of further reducing transmission losses. At the same time, the boundaries of micro-areas are re-evaluated and delineated, achieving the effect of reasonably dividing the areas and dynamically adjusting the power supply ends.

[0051] Embodiment 3: In the above embodiment, a transmission loss model is established to calculate the actual transmission loss, the boundary of the microscopic area is adjusted, the position of the power supply end is re-evaluated, and a position with smaller loss and wider coverage is selected as the new power supply end. This embodiment makes further improvements on the above basis.

[0052] The method also includes: S500: obtaining a loss stability value based on the actual loss values ​​of all microscopic areas in the macroscopic sub-area, and setting a secondary identification label to distinguish and mark the microscopic areas; comparing the actual loss value of each microscopic area based on the loss stability value to obtain a loss level characteristic value, and setting a classification standard based on the loss level characteristic value, loss level and load characteristics to obtain a secondary identification label.

[0053] In some embodiments, step S500 also includes: establishing a prediction model for predicting photovoltaic power generation and load power consumption in a micro-area in the future, and forming a three-level identification tag based on the prediction results; designing a redundant backup mechanism based on the three-level identification tag; the redundant backup mechanism is based on a comparative evaluation of the three-level identification tags of each micro-area, and selecting micro-areas to form a complementary group as alternative power supply ends.

[0054] In some embodiments, a loss stability value is obtained based on all actual loss values ​​of the micro-regions. The loss stability value adopts a weighted average of the actual loss values ​​of all micro-regions in each macro sub-region and is set in advance based on the importance of the area, load, and location of the micro-region. For example, if a micro-region is a critical load region, a higher weight is given. Assuming there are n micro-regions, the weight of each region is For each microscopic area i, the actual loss value is , weighted average The calculation formula is as follows:

[0055]

[0056] in, is the sum of all weights.

[0057] In some embodiments, the loss stabilization value can also directly use the median. After sorting all actual loss values ​​by size, select the number in the middle position. If the number of loss values ​​is an odd number, the median is the middle number; if it is an even number, the median is the average of the two middle numbers; the median is not sensitive to outliers, and the median can be used as a simpler method when selecting a loss stabilization value.

[0058] In some embodiments, the loss stability value refers to a standard stability value within a macro sub-region. The actual loss value of each micro region is compared with the loss stability value of the macro sub-region to obtain a loss level characteristic value, that is, the loss level characteristic value is the ratio of the actual loss value to the loss stability value. According to the loss level characteristic value, loss level and load characteristics, a classification standard is set to obtain a secondary identification label, and the loss level and load characteristics are divided into three levels of high, medium and low to form nine combinations. The secondary identification label is a string or digital code containing the loss level and load characteristics. For example, for a micro region with high loss and high load, its secondary identification label is set to AGG-1, where A represents the macro sub-region, GG represents high loss and high load, and 1 represents the location of the micro region.

[0059] In some embodiments, the prediction model is established to predict the photovoltaic power generation and load power consumption in the future, and then redundant backup is designed on the corresponding key nodes and paths to enhance the flexibility and scalability of the power supply end to cope with future changes and demand growth; collect historical data, meteorological data and seasonal change data, clean the collected data, remove outliers and missing values, and perform normalization to make data of different dimensions comparable and fusible. The historical data can be collected from the energy management system for each micro-region. The historical photovoltaic power generation and load power consumption data, the time span should be long enough, at least more than one year, to capture seasonal and cyclical changes; the meteorological data can be collected from the meteorological data API. Weather reports for the past and future period of time, including factors such as sunshine intensity, temperature, humidity, etc. that affect photovoltaic power generation; the seasonal data is to identify the impact of different seasons on photovoltaic power generation and load power consumption by sorting out the seasonal change patterns in historical data. Based on domain knowledge and correlation analysis, select features that are highly correlated with photovoltaic power generation and load power consumption, such as time (hours, days, weeks, months), weather conditions (sunshine intensity, temperature, humidity), season, etc. According to the content of the above embodiment, construct features that reflect the loss level and load characteristics of the micro-region, such as loss stability value, load fluctuation rate, etc. Select the long short-term memory network (LSTM) machine learning model, divide the preprocessed data into a training set and a test set, use the training set data to train the model, and adjust the model parameters to optimize the prediction performance. Use the test set data to evaluate the performance of the trained model, and the evaluation indicators include mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), etc. According to the evaluation results, tune the model, such as adjusting model parameters, adding features, changing model structure, etc., to improve the prediction accuracy. Use the trained model to predict photovoltaic power generation and load power consumption in the future.

[0060] In some embodiments, the prediction results of each micro-area are analyzed, and the predicted values ​​of photovoltaic power generation and load power consumption in the future are extracted. Based on the predicted values, the expected loss level and load characteristics of each micro-area are calculated, and the expected loss level and load characteristics are evaluated. Clear evaluation standards are pre-established, such as loss rate threshold and load stability index, etc., which are set according to actual conditions and historical data. Each micro-area is evaluated according to the evaluation standards. According to the evaluation results, a third-level identification tag is generated in combination with the original second-level identification tag. The third-level identification tag identifies the prediction results of the micro-area on the basis of the second-level identification tag, whether there is a significant increase or decrease in photovoltaic power generation and load power consumption, and whether there is a significant change in loss level and load characteristics. Through evaluation, problems such as excessive loss and unstable load in the micro-area can be discovered in time, and optimization can be carried out in advance, such as adjusting the equipment operation strategy, improving line conditions, and adding energy storage equipment; by reducing losses and stabilizing loads, the operating efficiency and reliability of the entire system can be improved.

[0061] In some embodiments, the redundant backup mechanism is based on the comparative evaluation of the three-level identification tags of each micro-area. The generated three-level identification tags can clearly identify the specific changes in the power consumption and power generation of the micro-area in the future. Micro-areas with similar geographical locations are preferably selected for pairwise comparison to evaluate the complementarity of the two micro-areas. Complementarity refers to: the degree of complementarity between power consumption and power generation, the matching degree of load characteristics, etc. If the power consumption and power generation trends of the two micro-areas are opposite (such as one increase and the other decrease), and the load characteristics can match each other, that is, the amount of increase is sufficient to meet the amount of decrease, it is considered to have high complementarity, and the micro-areas are selected to form a complementary group, and the micro-area that grows in the complementary group is used as an alternative power supply end; if the two micro-areas cannot be completely complementary, continue to look for a third nearby micro-area to form a more complete complementary group. The prediction results are evaluated regularly, and the members and configuration of the complementary group are dynamically adjusted according to the actual changes in power consumption and power generation.

[0062] In some embodiments, in each complementary group, one or more key nodes are selected as alternative power supply terminals. The alternative power supply terminals should have a certain redundant capacity to cope with emergencies. For complementary groups with strong complementarity, shared energy storage devices (such as batteries, pumped storage power stations, etc.) can be set up in the group to further improve the reliability and flexibility of energy supply. At the same time, an automatic switching mechanism is designed to quickly transfer the load to the alternative power supply terminal or shared energy storage device when the main power supply terminal increases or decreases. The possibility of future growth can be considered to ensure that the demand for energy supply can be met for a long period of time.

[0063] In this embodiment, the loss stability value is calculated based on the actual loss values ​​of all micro-regions in the macro sub-region, and the actual loss value of each micro-region is compared using the loss stability value to obtain the loss level characteristic value; the classification standard is set in combination with the loss level characteristic value, the loss level and the load characteristics, and a secondary identification label is assigned to the micro-region; a prediction model is established to predict the photovoltaic power generation and load power consumption of the micro-region in the future, and based on the prediction results, a tertiary identification label is assigned to the micro-region to identify the change trend of the predicted photovoltaic power generation, load power consumption and loss level; based on the tertiary identification label, the complementarity of the micro-region is evaluated to form a complementary group, an alternative power supply end is selected within the complementary group, and a shared energy storage device and an automatic switching mechanism are set.

[0064] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0065] This application achieves the effect of refined management of micro areas by introducing secondary and tertiary identification tags; by designing redundant backup mechanisms and automatic switching mechanisms, it enhances the flexibility and reliability of the power supply end, and dynamically adjusts the members and configuration of the complementary group according to the prediction results and complementarity evaluation, thereby achieving the effect of optimizing energy configuration.

[0066] Embodiment 4: In the above embodiment, the prediction model can predict the load changes in the future period in advance, and generate complementary groups through evaluation and comparison. However, the load characteristics of the critical area between the two areas will be greatly different. Therefore, this embodiment makes further improvements on the basis of the above embodiment.

[0067] Step S500 also includes: obtaining the actual loss value of each micro-area in the complementary group at different boundary points, pre-setting a critical threshold, marking the boundary points where the actual loss value is greater than the critical threshold, connecting the marked points to form a critical area, transferring part of the load of the critical area to the adjacent non-critical area, and redefining the boundaries of the micro-area according to the results of the load transfer, and dynamically adjusting the boundaries of the micro-area according to real-time monitoring data.

[0068] In some embodiments, the load data, voltage level, energy loss, etc. of each micro-area are collected, and through data analysis, areas with poor performance or overload are identified, and critical thresholds are set (such as load rate exceeding 80%, voltage fluctuation exceeding 5%, etc.), and boundary points where the actual loss value is greater than the critical threshold are marked, and the marked points are connected to form a critical area, and part of the load in the critical area is transferred to the adjacent non-critical area, or a new power supply terminal is added to share the load, and real-time monitoring is established to evaluate the readjusted area. According to the results of the load transfer, the boundaries of the micro-areas are redefined. Through multiple iterations, the boundaries are gradually optimized until the predetermined load balance and energy efficiency goals are achieved, with the goal of minimizing energy loss and load balance. The boundaries of the micro-areas are dynamically adjusted according to real-time monitoring data. For example, when the load in a certain area increases rapidly and approaches the critical value, part of the load is automatically transferred to the adjacent area.

[0069] In this embodiment, by obtaining the actual loss value of each micro-area at different boundary points within the complementary group, pre-setting critical thresholds (such as load rate exceeding 80%, voltage fluctuation exceeding 5%, etc.), marking the boundary points where the actual loss value is greater than the critical threshold, connecting the marked points to form a critical area, clarifying the area range with large differences in load characteristics, transferring part of the load in the critical area to the adjacent non-critical area, or adding a new power supply end to share the load, and redefining the boundaries of the micro-area based on the results of the load transfer to achieve a more balanced load distribution, establishing a real-time monitoring mechanism, collecting load data, voltage level, energy loss and other information of each micro-area, and dynamically adjusting the boundaries of the micro-area based on the real-time monitoring data to cope with changes in load and energy demand.

[0070] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0071] This application analyzes and compares complementary groups, marks boundary points, identifies boundary areas, and then realizes load transfer and boundary adjustment, thereby achieving the effect of dynamic boundaries and balanced loads. At the same time, it dynamically redefines boundaries and adjusts the power supply end, thereby further reducing the overall energy loss.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for energy distribution management based on regional resources, characterized in that: include: S100: obtaining a two-dimensional map of the region, establishing a plane rectangular coordinate system, scaling the two-dimensional map, preliminarily dividing the region into a number of cells of equal size according to a preset area, calculating the area of ​​the scaled map, and dividing the region into a number of macro sub-regions according to a macro standard; S200: Divide each macro sub-region into multiple micro-regions according to micro-standards and set unique identification codes for marking; the micro-standards include two division standards: load density and photovoltaic power generation potential; calculate the load density and photovoltaic power generation potential of all cells in each macro sub-region to form a load potential distribution map; use a cluster analysis method to divide the load potential into several levels, divide it into multiple micro-regions according to different levels, and adjust the boundaries; S300: setting the initial power supply end according to the macro sub-area and the micro area; calculating the total number of power supply ends required according to the load density of each micro area and the coverage of the power supply end, selecting the micro area for setting the power supply end, calculating the power supply distance, and generating an initial setting report; In the process of dividing cells of equal size, the region boundaries are demarcated for the regions located at the edge that are less than the preset cell size, and marked as edge grids, and the area of ​​the edge grids is determined, and then the similarity of each edge grid with all existing clusters is calculated in turn, and they are classified into the most similar clusters; the unique identification code includes the macro sub-region position of the micro region, the level to which it belongs, and the position in the macro sub-region; The macro standard includes two division standards: geographical location and illumination duration. The illumination similarity threshold and the location similarity threshold are pre-set, and a cell is selected as the initial cluster center. Cells similar to the cell in terms of illumination duration and geographical location are grouped into the same cluster to form a macro sub-region.

2. The method for energy distribution management based on regional resources according to claim 1, characterized in that: S300 also includes: sorting all micro areas from high to low according to load density, setting a selection ratio, wherein the selection ratio is set according to the total number of required power supply terminals and the specific load density, and determining the number of micro areas in order according to the selection ratio; using the center position of the initially selected micro area as the potential power supply terminal position, and using the Euclidean distance formula to calculate the distance from the farthest boundary point of each micro area to the potential power supply terminal position.

3. The method for energy distribution management based on regional resources according to claim 1, characterized in that: The initial setup report includes generating an initial site selection map, marking the preliminarily selected power supply terminal location on a two-dimensional map, marking the load center and road information of each micro-area, and marking the coverage range and maximum transmission distance at each power supply terminal location.

4. The method for energy distribution management based on regional resources according to claim 1, characterized in that: The method also includes: S400: establishing a transmission loss model, calculating actual loss values ​​under different transmission paths according to the coordinate positions of different boundary points, comparing and analyzing the transmission losses of different boundary points to obtain loss differences, setting a difference threshold, marking boundary points whose loss differences are greater than the difference threshold, adjusting the micro-area boundaries and re-evaluating the position of the power supply end.

5. The method for energy distribution management based on regional resources as claimed in claim 4, characterized in that: The transmission loss model calculates the resistance loss and transformer loss generated when a uniform conductor is used as a transmission line. During the establishment process, the influence of external factors on the transmission loss is ignored, and a constant resistivity and conductor cross-sectional area are set.

6. The method for energy distribution management based on regional resources according to claim 1, characterized in that: The method also includes: S500: obtaining a loss stability value based on the actual loss values ​​of all microscopic areas in the macroscopic sub-area, and setting a secondary identification label to distinguish and mark the microscopic areas; comparing the actual loss value of each microscopic area based on the loss stability value to obtain a loss level characteristic value, and setting a classification standard based on the loss level characteristic value, loss level and load characteristics to obtain a secondary identification label.

7. The method for energy distribution management based on regional resources according to claim 6, characterized in that: Step S500 also includes: establishing a prediction model for predicting photovoltaic power generation and load power consumption in a micro area in the future, forming a three-level identification tag based on the prediction results; and designing a redundant backup mechanism based on the three-level identification tag.

8. The method for energy distribution management based on regional resources as claimed in claim 7, characterized in that: The redundant backup mechanism is based on comparative evaluation of the three-level identification tags of each micro-area, and selects micro-areas to form a complementary group as alternative power supply terminals.

9. The method for energy distribution management based on regional resources according to claim 6, characterized in that: Step S500 also includes: obtaining the actual loss value of each micro-area in the complementary group at different boundary points, pre-setting a critical threshold, marking the boundary points where the actual loss value is greater than the critical threshold, connecting the marked points to form a critical area, transferring part of the load of the critical area to the adjacent non-critical area, and redefining the boundaries of the micro-area according to the results of the load transfer, and dynamically adjusting the boundaries of the micro-area according to real-time monitoring data.

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