A substation energy storage configuration scale distribution map generation method and system

Abstract

The application is suitable for the technical field of power system, and provides a substation energy storage configuration scale distribution map generation method, including: dividing a power supply area into multiple power supply regions according to voltage levels; respectively calculating peak regulation profits and losses of the multiple power supply regions; respectively calculating to-be-configured energy storage scales after an energy storage power station is connected in each power supply region according to the peak regulation profits and losses, the to-be-configured energy storage scales including first to-be-configured energy storage scales and second to-be-configured energy storage scales; determining energy storage configuration scales of each power supply region by comparing the first to-be-configured energy storage scales and the second to-be-configured energy storage scales; and drawing an energy storage configuration scale distribution map of each power supply region in the power supply area based on the energy storage configuration scales of each power supply region. The application can layout total energy storage construction scales according to different voltage levels in the power supply regions and provide sufficient decision-making basis for energy storage power station grid connection examination and approval and investors.

Description

Technical Field

[0001] This application belongs to the field of power system technology, and in particular relates to a method and system for generating a scale distribution map of energy storage configuration in a substation. Background Technology

[0002] Electrochemical energy storage power stations have the dual functions of peak shaving and valley filling. They can regulate the daily load changes of the power system and are invaluable peak-shaving power sources. The construction of electrochemical energy storage power stations within the power supply area of ​​substations is an effective measure to alleviate the power supply gap in the power grid. It can alleviate the power supply pressure of the power grid during peak summer seasons. Moreover, with the increasing scarcity of urban land supply, large-scale energy storage devices will effectively shave and fill the power load, reduce the local peak-valley difference, reduce the power output to the grid, further release the transmission capacity of transmission equipment, and delay the expansion plan of substations.

[0003] The existing electrochemical energy storage planning and layout has the following problems: (1) Traditional grid peak shaving and balancing calculations consider the whole province as a unified calculation and analysis, and plan the construction of pumped storage power stations and gas-fired peak shaving power sources according to peak shaving demand. This is to centrally shave the power load of the whole province, but cannot provide local peak shaving and balancing capabilities for load fluctuations within the power supply range of each substation. (2) Provincial and municipal energy storage plans or special studies only give the total scale of energy storage construction, without effectively decomposing it into the construction scale of each region. Investors are prone to flocking to a certain region to apply for projects, resulting in waste of resources and disorderly competition. This cannot provide sufficient decision-making basis for the approval of energy storage power station access to the grid and for investors.

[0004] Therefore, there is an urgent need for a method that can allocate the total scale of energy storage construction according to the different voltage levels within the power supply area and provide sufficient decision-making basis for the approval of energy storage power station grid connection and investors, thereby reducing the cross-regional dispatch and flow of peak-shaving capacity and improving the practicality of battery energy storage station planning methods. Summary of the Invention

[0005] This application provides a method, system, and device for generating a scale distribution map of substation energy storage configuration, which can solve at least one of the above-mentioned existing problems.

[0006] In a first aspect, embodiments of this application provide a method for generating a substation energy storage configuration scale distribution map, including:

[0007] The power supply area is divided into multiple power supply zones based on voltage levels;

[0008] Calculate the peak-shaving profit and loss for each of the aforementioned power supply areas;

[0009] Based on the peak-shaving profit and loss, the energy storage capacity to be configured after connecting energy storage power stations in each power supply area is calculated. The energy storage capacity to be configured includes a first energy storage capacity to be configured and a second energy storage capacity to be configured. When calculating the first energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is the same as the voltage level of the power supply area. When calculating the second energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is lower than the voltage level of the power supply area.

[0010] The energy storage configuration scale for each power supply area is determined by comparing the first and second energy storage scales to be configured.

[0011] Based on the energy storage configuration scale of each power supply area, a distribution map of the energy storage configuration scale of each power supply area within the power supply area is drawn. The distribution map of the energy storage configuration scale includes power supply areas of different voltage levels, and each power supply area includes the energy storage configuration scale corresponding to the power supply area.

[0012] Furthermore, the power supply area is divided into multiple power supply zones according to voltage levels, including:

[0013] Obtain the geographical location data of substations at each voltage level within the power supply area, and use geographic information system technology to mark the geographical location of the substations on the map of the power supply area;

[0014] Based on the geographical location of the substations and the layout of power supply lines within the power supply area, substations of the same voltage level are connected to determine multiple power supply areas.

[0015] Furthermore, the calculation of peak-shaving surplus and deficit for each of the multiple power supply areas includes:

[0016] Obtain the maximum and minimum load values ​​for each power supply area;

[0017] Based on the maximum load value and the minimum load value, calculate the peak-shaving demand for each power supply area, wherein the peak-shaving demand = maximum load value - minimum load value;

[0018] Obtain the generator unit parameters and energy storage power station installed capacity of each power supply area. The generator unit parameters include the available output and minimum technical output of the generator unit, and the energy storage power station installed capacity is the energy storage scale of the energy storage power stations that have been connected to the power supply area.

[0019] Based on the unit parameters and the installed capacity of the energy storage power station, the peak-shaving capacity of each power supply area is calculated respectively, wherein the peak-shaving capacity = (available output - minimum technical output) + installed capacity of energy storage power station × 2.

[0020] Calculate the peak-shaving profit and loss for each power supply area, where the peak-shaving profit and loss = peak-shaving demand - peak-shaving capacity.

[0021] Furthermore, obtaining the maximum and minimum load values ​​for each power supply area includes:

[0022] Obtain all historical load data and a preset time granularity for the power supply area within a preset time period, and calculate the load utilization rate P for each day within the preset time period, where P = (p / max(p)). t -p t-1 ))×100%, p is the average load value, p=P sum / t sum P sum The total load value represents the total load data for a given day, t. sum The total time granularity value represents the number of time granularities divided within a day, max(p t -p t-1 p represents the maximum difference between load data at adjacent time granularities. t p represents the load data at a time granularity t. t-1 This represents the load data at a time granularity of t-1;

[0023] The peak load day is determined by comparing the load utilization rate values ​​for each day.

[0024] The historical load data of the peak load days are classified according to the time granularity to form load datasets under different time granularities;

[0025] Based on the load dataset, a daily load variation curve of the load dataset is plotted, and the maximum and minimum load values ​​of the daily load variation curve are obtained.

[0026] Furthermore, the step of calculating the required energy storage capacity after connecting energy storage power stations in each power supply area based on the peak-shaving profit and loss includes:

[0027] If there is a shortage of peak regulation in the power supply area, the access conditions for connecting the energy storage power station in the power supply area are obtained. The access conditions include the difficulty coefficient of the implementation of the power transmission and transformation engineering line path when connecting the energy storage power station and the number of intervals on the high-voltage side and low-voltage side of the substation in the power supply area. The difficulty coefficient includes easy, general and difficult.

[0028] If the difficulty coefficient is easy or moderate, then calculate the energy storage capacity that can be accessed within the power supply area. The energy storage capacity = number of intervals × transmission capacity, where the transmission capacity is the transmission capacity corresponding to the maximum conductor cross-section of the power transmission and transformation project line when the energy storage station is connected according to the voltage level.

[0029] If the difficulty level is "difficult", then the available energy storage capacity within the power supply area is determined to be 0.

[0030] Based on the energy storage capacity and peak shaving deficit, the energy storage capacity to be configured in the power supply area is calculated. The energy storage capacity to be configured is min{energy storage capacity, peak shaving deficit / 2}, where the peak shaving deficit is the value of peak shaving surplus or deficit.

[0031] Furthermore, the step of determining the energy storage configuration scale for each power supply area by comparing the first and second energy storage scales to be configured includes:

[0032] If the first energy storage capacity to be configured is smaller than the second energy storage capacity to be configured, then the energy storage capacity of the power supply area = K × the second energy storage capacity to be configured, where K = the first energy storage capacity to be configured / the second energy storage capacity to be configured;

[0033] If the first energy storage capacity to be configured is greater than or equal to the second energy storage capacity to be configured, then the energy storage configuration capacity of the power supply area = the second energy storage capacity to be configured.

[0034] Furthermore, the method also includes:

[0035] Electricity price information is obtained, and a data processing method is used to establish an electricity price prediction model. The electricity price prediction model is used to predict electricity price information within a preset time period.

[0036] Calculate the connection cost when connecting an energy storage power station within the power supply area;

[0037] Based on the electricity price information, the cost information, historical load data, and the planned energy storage capacity, an energy storage power station access evaluation system is established. The energy storage power station access evaluation system is used to generate energy storage power station access schemes. The planned energy storage capacity refers to the energy storage power stations in the power supply area to be connected.

[0038] Furthermore, the method also includes:

[0039] A power supply area database is set up to store energy storage configuration information for each power supply area. The energy storage configuration information includes basic data of the power supply area, basic data of the substation, and the energy storage configuration scale of the power supply area at different time granularities.

[0040] Based on data visualization methods, the energy storage configuration information of each power supply area is displayed on the energy storage configuration scale distribution map;

[0041] A time selector is set up, which is used to preset the time granularity. Based on the different preset time granularities, the energy storage configuration information is dynamically displayed on the energy storage configuration scale distribution map.

[0042] Secondly, embodiments of this application provide a substation energy storage configuration scale distribution map generation system, including:

[0043] Power supply area division module: used to divide the power supply area into multiple power supply areas according to voltage level;

[0044] Peak shaving profit and loss calculation module: used to calculate the peak shaving profit and loss of multiple power supply areas respectively;

[0045] The energy storage capacity calculation module is used to calculate the energy storage capacity to be configured after connecting energy storage power stations in each power supply area based on the peak shaving surplus and deficit. The energy storage capacity to be configured includes a first energy storage capacity to be configured and a second energy storage capacity to be configured. When calculating the first energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is the same as the voltage level of the power supply area. When calculating the second energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is lower than the voltage level of the power supply area.

[0046] Energy storage configuration scale determination module: used to determine the energy storage configuration scale of each power supply area by comparing the first energy storage scale to be configured and the second energy storage scale to be configured;

[0047] Energy storage configuration scale distribution map drawing module: used to draw an energy storage configuration scale distribution map of each power supply area based on the energy storage configuration scale of each power supply area. The energy storage configuration scale distribution map includes power supply areas of different voltage levels, and each power supply area includes the energy storage configuration scale corresponding to the power supply area.

[0048] The beneficial effects of the embodiments in this application compared with the prior art are:

[0049] Based on voltage levels, the power supply area is divided into multiple power supply zones. By calculating the energy storage configuration scale of each power supply zone, the power supply zone can provide peak shaving and balancing based on the voltage level, reducing the cross-regional scheduling and flow of energy storage power stations, and alleviating the power supply pressure on the power grid company. In addition, a corresponding energy storage configuration scale distribution map is generated based on the energy storage configuration scale, so that the energy storage configuration information of each power supply zone can be displayed dynamically and intuitively. This provides more sufficient decision-making basis for the approval of energy storage power station access and the investment of energy storage projects, guides the rational layout and orderly investment of energy storage configuration, and provides sustainable development for the healthy growth of the energy storage industry. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating a method for generating a substation energy storage configuration scale distribution map according to an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of a substation energy storage configuration scale distribution map generation system provided in an embodiment of the present invention. Detailed Implementation

[0053] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0054] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0055] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0056] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0057] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0058] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0059] Please see Figure 1 As shown, this invention provides a method for generating a substation energy storage configuration scale distribution map, comprising the following steps:

[0060] S100. Based on voltage levels, the power supply area is divided into multiple power supply zones;

[0061] In this embodiment, power supply areas are divided according to voltage levels, and substations with the same voltage level in the power supply area are assigned to the same power supply region. Typically, the voltage levels include 500kV, 220kV and 110kV.

[0062] In some embodiments, step S100 above includes:

[0063] Obtain the geographical location data of substations at each voltage level within the power supply area, and use geographic information system technology to mark the geographical location of the substations on the map of the power supply area;

[0064] Based on the geographical location of the substations and the layout of power supply lines within the power supply area, substations of the same voltage level are connected to determine multiple power supply areas.

[0065] In this embodiment, the voltage level and geographical location data of each substation within the power supply area are obtained through the power grid company. Substations with the same voltage level are divided into initial power supply areas. The geographical location data includes the longitude and latitude of the substations. A coordinate axis is established with the center of the initial power supply area as the origin. The longitude and latitude of the substations are converted into coordinates on the coordinate axis to determine the specific location of the substations within the initial power supply area. Further, the substations in the initial power supply area are connected, and the Dijkstra algorithm is used to calculate the shortest path between the substations to obtain the optimal connection scheme. Based on the optimal connection scheme, a hierarchical clustering analysis method is used to calculate the distance between the substations. The shortest distance method is used to progressively cluster the substations, ultimately dividing the initial power supply area into multiple power supply areas. For example, suppose there are five 220kV substations, whose coordinates are (0,0), (3,4), (6,2), (8,5), and (10,1). The distance matrix between the substations is calculated, and Dijkstra's algorithm is used to obtain the optimal connection scheme between the substations, with a total connection length of 22 kilometers. Based on the optimal connection scheme obtained by Dijkstra's algorithm, hierarchical clustering analysis is used to cluster the substations. The shortest distance method is used to progressively cluster the five substations. When the clustering distance threshold is set to 5 kilometers, the substations can be divided into two power supply areas. One power supply area contains three substations (0,0), (3,4), and (6,2), and the other power supply area contains two substations (8,5) and (10,1). Thus, by using the above method, the power supply area is divided into multiple power supply areas, where the voltage level is the same in each power supply area.

[0066] S200, Calculate the peak-shaving profit and loss of each of the multiple power supply areas;

[0067] In some embodiments, step S200 above includes:

[0068] Obtain the maximum and minimum load values ​​for each power supply area;

[0069] Based on the maximum load value and the minimum load value, calculate the peak-shaving demand for each power supply area, wherein the peak-shaving demand = maximum load value - minimum load value;

[0070] Obtain the generator unit parameters and energy storage power station installed capacity of each power supply area. The generator unit parameters include the available output and minimum technical output of the generator unit, and the energy storage power station installed capacity is the energy storage scale of the energy storage power stations that have been connected to the power supply area.

[0071] Based on the unit parameters and the installed capacity of the energy storage power station, the peak-shaving capacity of each power supply area is calculated respectively, wherein the peak-shaving capacity = (available output - minimum technical output) + installed capacity of energy storage power station × 2.

[0072] Calculate the peak-shaving profit and loss for each power supply area, where the peak-shaving profit and loss = peak-shaving demand - peak-shaving capacity.

[0073] In this embodiment, the available output, minimum technical output, and installed capacity of the energy storage power station are obtained through the power grid company. The installed capacity of the energy storage is the scale of energy storage already connected to the power supply area. Therefore, the peak-shaving capacity of the power supply area = (available output - minimum technical output) + installed capacity of the energy storage power station × 2. The installed capacity of the energy storage power station × 2 represents the peak-shaving capacity of the energy storage power station connected to the power supply area. Specifically, when the installed capacity of the energy storage power station is 100MW, the energy storage power station can absorb 100MW of electricity and release 100MW of electricity. Therefore, the peak-shaving capacity of the energy storage power station is 2 × 100MW.

[0074] Furthermore, by subtracting peak-shaving capacity from peak-shaving demand, the peak-shaving surplus or deficit of the power supply area can be calculated. When the calculated value of peak-shaving surplus or deficit is positive, it indicates that there is a shortage of peak-shaving capacity in the power supply area, and energy storage power stations need to be configured in the power supply area according to the amount of the shortage.

[0075] In some embodiments, obtaining the maximum and minimum load values ​​for each power supply area includes:

[0076] Obtain all historical load data and a preset time granularity for the power supply area within a preset time period, and calculate the load utilization rate for each day within the preset time period, wherein the load utilization rate... This is the average load value. P sum The total load value represents the total load data for a given day, t. sum The total time granularity value represents the number of time granularities divided within a day, max(p t -p t-1 p represents the maximum difference between load data at adjacent time granularities. t p represents the load data at a time granularity t. t-1 This represents the load data at a time granularity of t-1;

[0077] The peak load day is determined by comparing the load utilization rate values ​​for each day.

[0078] The historical load data of the peak load days are classified according to the time granularity to form load datasets under different time granularities;

[0079] Based on the load dataset, a daily load variation curve of the load dataset is plotted, and the maximum and minimum load values ​​of the daily load variation curve are obtained.

[0080] In this embodiment, historical load data is classified according to time granularity. The data can be divided into different time granularities such as 15 minutes, 30 minutes, and 1 hour. The load utilization rate obtained based on the different calculation methods under different time granularities is different, that is, the load peak day is different. Based on different load peak days and different time granularities, the maximum load value and minimum load value are also different. Based on this, different time granularities correspond to different load peak days. Users can determine the corresponding time granularity according to the needs of power grid planning and management decisions, and then determine the corresponding load peak day. Through the load peak day, the maximum load value and minimum load value are further determined, thereby improving the selectivity of power grid planning and the accuracy of decision-making.

[0081] In this embodiment, all historical load data within a preset time period are obtained from the power grid company. The load utilization rate for each day within the preset time period is calculated. By comparing the maximum value among multiple load utilization rates, the peak load day within the preset time period is determined. By plotting the daily load curve of the peak load day, the maximum and minimum load values ​​are obtained. Specifically, all historical load data within January 2024 are obtained from the power grid company. All historical load data are divided by the number of days to obtain the historical load data for each day in January. Then, the load utilization rate for each day is calculated. Specifically, the historical load data is visualized to obtain the total load value of 1200MW on January 1st. With a preset time granularity of 15 minutes, the total time granularity value t is... sum = (1 × 24 × 60) / 15 = 96, then the average load value Furthermore, historical load data from January 1st shows that the maximum difference in load data occurred between 18:00 and 18:15, reaching 17.5MW. This indicates that max(p t -p t-1 If the load factor is 17.5, then the load utilization rate on January 1st is P = 12.5 / 17.5 = 71.4%. Similarly, calculate the load utilization rate for each day in January using the same method. By comparing the values ​​of the load utilization rates for each day, obtain the maximum load utilization rate. The date corresponding to the maximum load utilization rate is the peak load day. By plotting the daily load curve for the peak load day, obtain the maximum and minimum load values.

[0082] In this embodiment, all historical load data acquired within a preset time period are visualized according to a preset time granularity. Specifically, assuming the preset time granularity is 15 minutes, the daily historical load data is segmented according to the 15-minute time granularity to obtain a load dataset at the 15-minute time granularity. This load dataset includes load data for the power supply area every 15 minutes. Based on this load dataset, a load curve is plotted with a 15-minute time granularity. Specifically, time points are marked on the horizontal axis, from 00:00 to 23:45, with each 15-minute interval as a coordinate interval. Load values ​​are marked on the vertical axis. This load curve allows for the rapid acquisition of the maximum and minimum load values ​​in the load dataset. Similarly, assuming the user selects 30 minutes as the time granularity, a load curve is plotted with a 30-minute time granularity, where the load dataset includes load data for the power supply area every 30 minutes.

[0083] In some embodiments, obtaining the maximum and minimum load values ​​for each power supply area further includes:

[0084] Obtain the daily load curve of the power supply area within a preset time period, classify the daily load curve, and obtain different typical day types;

[0085] Time series analysis and cluster analysis were performed on the daily load curves of the same typical day type to obtain the electricity consumption characteristics of different time periods;

[0086] The decision tree algorithm is used to divide different time periods into time granularities based on the electricity consumption characteristics, and a preliminary time granularity division scheme is generated.

[0087] Simulation tests were conducted to generate preset time granularity strategies based on different time granularity division schemes for different typical day types.

[0088] In this embodiment, all historical load data within a preset time period are obtained from the power grid company. Data preprocessing techniques are used to clean and normalize the daily load data within the preset time period, resulting in standardized daily load curves. Clustering analysis algorithms, such as K-means and hierarchical clustering, are then used to classify these standardized daily load curves. Based on the clustering results, different typical daily load curve types are obtained. Methods such as the elbow rule or silhouette coefficient are used to evaluate the clustering effect under different numbers of clusters, determining the optimal number of clusters and obtaining the final typical day types, such as weekdays, weekends, and holidays. Visualization techniques are used to plot load curves for different typical day types, intuitively displaying the characteristics and differences of each type of load, providing a reference for subsequent load forecasting and power dispatching. The information of the typical day types is stored in a database, including type number, load curve characteristics, and corresponding date, facilitating subsequent queries and applications to obtain complete typical daily load curve classification results.

[0089] For different typical day types, based on load data within 24 hours, the k-means clustering algorithm is used to cluster the load data for different time periods to obtain the electricity consumption characteristics of different time periods. Specifically, by setting the number of clusters k and the number of iterations, k cluster centers are initialized. The distance from each load data point to the cluster center is calculated, and each load data point is assigned to the category corresponding to the nearest cluster center, obtaining the initial clustering result. Based on the initial clustering result, the mean of all load data points within each cluster is calculated, and the coordinates of the center point of each cluster are updated. Using the new cluster center coordinates, the distance from each data point to the cluster center is recalculated, and the category of the load data is reassigned according to the distance. Through multiple iterations to update the cluster centers and load data categories, the cluster centers no longer change significantly or the maximum number of iterations is reached, obtaining the final clustering result. Based on the results of the cluster analysis, load data points within different cluster categories are obtained. By analyzing the temporal distribution characteristics of load data points within each category, the main electricity consumption periods corresponding to different categories are determined, along with typical electricity consumption patterns during peak, off-peak, and low-peak periods. This allows the 24-hour load data to be divided into different time periods. By analyzing the trends and characteristics of electricity load changes within each time period, load curves and electricity consumption patterns for different time periods are obtained.

[0090] Based on electricity consumption characteristic data from different time periods, time boundaries with significant differences in electricity consumption behavior are identified, initially dividing the time into various time granularities. Based on these initial time granularities, a clustering algorithm is used to cluster user electricity consumption behavior within each time granularity, resulting in user groups with different electricity consumption behavior patterns at each time granularity. By analyzing the differences among user groups with different electricity consumption behavior patterns within each time granularity, the rationality assessment results of the time granularity are obtained. Based on the rationality assessment results of the time granularity, the initial time granularity division scheme is adjusted to obtain a more refined and reasonable time granularity division. Using a decision tree algorithm, with the adjusted time granularity as a feature and combined with other user electricity consumption characteristics, an electricity consumption behavior prediction model is established. Through the electricity consumption behavior prediction model, the electricity consumption behavior trend of users at different time granularities is determined, and the prediction results of electricity load data at each time granularity are obtained.

[0091] A simulation test method was used to acquire load data at different time granularities for each typical day type. By comparing and analyzing the differences in load data at different time granularities, the impact of different time granularity division schemes on the accuracy of load data was obtained. Based on the simulation test results, the optimal time granularity division scheme for each typical day type was determined. Using the optimal time granularity division scheme, the load data for each typical day type was processed. Through data transformation operations such as aggregation and discretization, a normalized time-granularity load dataset was obtained. Based on the normalized time-granularity load dataset, combined with the constraints and optimization objectives of power grid dispatching, an intelligent optimization algorithm was used to obtain the optimal time granularity preset strategy for each typical day type. The optimal time granularity preset strategies for each typical day type were combined, and by analyzing the correlation and transformation patterns between different typical day types, a set of time granularity preset strategies for a preset time period was obtained. Simulation testing is used to verify the set of time-granularity preset strategies within a preset time period. By comparing and analyzing the actual and predicted power load data of the preset time granularity in the set of time-granularity preset strategies, the feasibility and effectiveness of the time-granularity preset strategies are judged, thereby obtaining reliable time-granularity preset strategies to provide corresponding guidance for the power grid planning and management decision-making process. When setting the time granularity, users can select the appropriate time granularity according to the time-granularity preset strategy.

[0092] S300. Based on the peak-shaving surplus and deficit, calculate the energy storage capacity to be configured after connecting the energy storage power station in each power supply area. The energy storage capacity to be configured includes a first energy storage capacity to be configured and a second energy storage capacity to be configured. When calculating the first energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is the same as the voltage level of the power supply area. When calculating the second energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is lower than the voltage level of the power supply area.

[0093] In one embodiment, step S300 includes:

[0094] If there is a shortage of peak regulation in the power supply area, the access conditions for connecting the energy storage power station in the power supply area are obtained. The access conditions include the difficulty coefficient of the implementation of the power transmission and transformation engineering line path when connecting the energy storage power station and the number of intervals on the high-voltage side and low-voltage side of the substation in the power supply area. The difficulty coefficient includes easy, general and difficult.

[0095] If the difficulty coefficient is easy or moderate, then calculate the energy storage capacity that can be accessed within the power supply area. The energy storage capacity = number of intervals × transmission capacity, where the transmission capacity is the transmission capacity corresponding to the maximum conductor cross-section of the power transmission and transformation project line when the energy storage station is connected according to the voltage level.

[0096] If the difficulty level is "difficult", then the available energy storage capacity within the power supply area is 0.

[0097] Based on the energy storage capacity and peak shaving deficit, the energy storage capacity to be configured in the power supply area is calculated. The energy storage capacity to be configured is min{energy storage capacity, peak shaving deficit / 2}, where the peak shaving deficit is the value of peak shaving surplus or deficit.

[0098] In this embodiment, determining the scale of energy storage configuration within the power supply area requires consideration of two factors: the required installed energy storage capacity within the power supply area and the energy storage capacity that the substation transmission lines can deliver to the power supply area. If the energy storage capacity is small, the energy storage output to the power supply area will be constrained, requiring an increase in energy storage capacity. Therefore, the energy storage configuration scale must be configured according to the energy storage capacity that the substation transmission lines can deliver to the power supply area. If the energy storage capacity is large, it indicates that the energy storage output to the power supply area is not constrained, and the energy storage configuration scale is configured according to the required installed energy storage capacity within the power supply area. Thus, the scale of energy storage configuration within the power supply area is the minimum of the required installed energy storage capacity and the required installed energy storage capacity. The required installed energy storage capacity for the power supply area is calculated as: peak shaving deficit / 2. The peak shaving deficit represents the peak shaving surplus or deficit, indicating the peak shaving capacity of the power supply area. For example, if the power supply area can absorb 100MW of electricity, it can also release 100MW of electricity. Therefore, the installed energy storage capacity = peak shaving deficit / 2.

[0099] In this embodiment, the energy storage capacity that the substation transmission and transformation project lines can transmit to the power supply area needs to take into account the access conditions for connecting the energy storage power station within the power supply area. The access conditions include the difficulty coefficient of the transmission and transformation project line path when connecting the energy storage power station and the number of intervals on the high-voltage side and low-voltage side of the substation within the power supply area.

[0100] Specifically, the difficulty coefficient of the transmission and transformation project route when connecting to the energy storage power station is judged by the proportion of dense areas within the power supply area. Dense areas include basic farmland protection areas, nature reserves, cultural relic protection areas, mining subsidence areas and military close-in areas, densely populated residential areas, etc. If the proportion of dense areas within the power supply area is greater than 70%, the implementation difficulty coefficient of the power supply area is judged as difficult. If the proportion of dense areas within the power supply area is 30%-70%, the implementation difficulty coefficient of the power supply area is judged as moderate. If the proportion of dense areas within the power supply area is less than 30%, the implementation difficulty coefficient of the power supply area is judged as easy.

[0101] Specifically, the number of high-voltage and low-voltage bays in the substations within the power supply area is obtained from the power grid company. In a power system, a substation consists of multiple bays, each containing corresponding electrical equipment such as transformers, switches, and control and protection devices. The number of bays in a substation depends on its capacity and design. Generally, there are dozens to hundreds of bays between the high-voltage and low-voltage sides of a substation. These bays are used to differentiate and control different input and output lines, enabling the conversion and transmission of electrical energy from high to low voltage. Therefore, when connecting an energy storage power station, the number of high-voltage and low-voltage bays in the substations within the power supply area must be determined. The transmission capacity of the substation's transmission lines also affects the connection of the energy storage power station. Furthermore, different voltage levels of the connected energy storage power station require different conductor cross-sections, which correspond to different transmission cross-sections. For example, when connecting a 220kV energy storage power station, it is recommended to use 2×630mm² conductors. 2 The cross-section corresponds to a transmission capacity of approximately 773 MVA; when connecting to a 110 kV energy storage power station, a 630 mm² cross-section is recommended. 2 The cross-section corresponds to a conveying capacity of approximately 193 MVA.

[0102] In this embodiment, the impact of the difficulty coefficient of connecting the power supply area to the energy storage station on the energy storage capacity is comprehensively considered. If the difficulty coefficient is easy or moderate, the energy storage capacity = number of intervals × transmission capacity. If the difficulty coefficient is difficult, it means that it is difficult to connect the energy storage station in the power supply area. Therefore, it is not recommended to configure the energy storage station in the power supply area, and its energy storage capacity is 0.

[0103] S400. By comparing the first energy storage capacity to be configured and the second energy storage capacity to be configured, the energy storage configuration capacity of each power supply area is determined.

[0104] In some embodiments, step S400 above includes:

[0105] If the first energy storage capacity to be configured is smaller than the second energy storage capacity to be configured, then the energy storage capacity of the power supply area = K × the second energy storage capacity to be configured, where K = the first energy storage capacity to be configured / the second energy storage capacity to be configured;

[0106] If the first energy storage capacity to be configured is greater than or equal to the second energy storage capacity to be configured, then the energy storage configuration capacity of the power supply area = the second energy storage capacity to be configured.

[0107] In this embodiment, the energy storage configuration scale of the power supply area can be determined by comparing the first energy storage scale to be configured and the second energy storage scale to be configured. When the first energy storage scale to be configured is smaller than the second energy storage scale to be configured, the energy storage configuration scale is determined by the ratio between the first energy storage scale to be configured and the second energy storage scale to be configured, so that the sum of the energy storage configuration scales of the connected energy storage power stations is less than the energy storage configuration scale of the power supply area. This makes the maximum accessible energy storage configuration scale in the power supply area greater than the energy storage configuration scale of the finally connected energy storage power stations, thereby ensuring the safety of the energy storage power station connection process and the power grid usage process.

[0108] Specifically, a power supply area is typically divided into multiple sub-power supply areas. The energy storage capacity of the main power supply area must be greater than the sum of the energy storage capacities of the sub-power supply areas. The voltage level of each sub-power supply area is lower than that of the main power supply area; that is, the energy storage capacity of the energy storage stations that can be connected to the main power supply area is greater than the energy storage capacity of the final connected energy storage station. For example, a 500kV power supply area can typically be divided into multiple 220kV power supply areas, and the sum of the energy storage capacities within the 220kV power supply areas must be less than the energy storage capacity of the 500kV power supply area. Therefore, in this embodiment, the first energy storage capacity to be configured and the second energy storage capacity to be configured represent the energy storage capacity to be configured when the voltage level of the connected energy storage power station is the same as the voltage level of the power supply area and the energy storage capacity to be configured when the voltage level of the connected energy storage power station is lower than the voltage level of the power supply area, respectively. By calculating the first energy storage capacity to be configured, the maximum accessible energy storage configuration capacity in the power supply area is determined. By calculating the second energy storage capacity to be configured, the energy storage configuration capacity of the power supply area formed when the connected energy storage power station is connected at a voltage level lower than that of the power supply area is determined. By comparing the values ​​of the first energy storage capacity to be configured and the second energy storage capacity to be configured, it is ensured that the energy storage configuration capacity of the power supply area formed by the connected energy storage power station is less than the maximum accessible energy storage configuration capacity in the power supply area, wherein the voltage level of the connected energy storage power station is lower than the voltage level of the power supply area. Specifically, when the first energy storage capacity to be configured is greater than or equal to the second energy storage capacity to be configured, it means that the energy storage capacity of the power supply area formed by the connected energy storage power station is less than the energy storage capacity of the power supply area. In this case, the energy storage capacity of the power supply area = the second energy storage capacity to be configured. When the first energy storage capacity to be configured is less than the second energy storage capacity to be configured, it means that the energy storage capacity of the power supply area formed by the connected energy storage power station is greater than the energy storage capacity of the power supply area. In this case, the ratio between the first energy storage capacity to be configured and the second energy storage capacity to be configured needs to be taken to calculate the energy storage capacity of the power supply area, that is, the energy storage capacity of the power supply area = K × the second energy storage capacity to be configured, where K = the first energy storage capacity to be configured / the second energy storage capacity to be configured.

[0109] Specifically, for a 500kV power supply area, the required energy storage capacity when connected to a 500kV energy storage substation is calculated, denoted as the first required energy storage capacity. The required energy storage capacity when connected to a 220kV energy storage substation is calculated, denoted as the second required energy storage capacity. The values ​​of the first and second required energy storage capacities are compared. If the first required energy storage capacity is greater than or equal to the second required energy storage capacity, then the energy storage capacity of the 500kV power supply area equals the second required energy storage capacity. If the first required energy storage capacity is less than the second required energy storage capacity, then the ratio between the first and second required energy storage capacities is calculated. In this case, the energy storage capacity of the 500kV power supply area equals the second required energy storage capacity multiplied by the ratio. The calculation method for the energy storage capacity of a 220kV or 110kV power supply area is the same and will not be repeated here.

[0110] S500. Based on the energy storage configuration scale of each power supply area, draw a distribution map of the energy storage configuration scale of each power supply area within the power supply area.

[0111] In this embodiment, GIS software is used to generate an energy storage configuration scale distribution map of the power supply area divided according to step S100. Furthermore, different color schemes are used for different voltage levels, such as red for 500kV, green for 220kV, and blue for 110kV. Subsequently, the energy storage configuration scale is classified, with each level corresponding to a different color scheme depth. For example, the energy storage configuration scale is divided into high-level, medium-level, and low-level, where high-level energy storage configuration scale is greater than 250MW, medium-level energy storage configuration scale is greater than 50MW and less than or equal to 250MW, and low-level energy storage configuration scale is less than or equal to 50MW. Based on the above classification results, the energy storage configuration scales of low, medium, and high levels correspond to light, medium, and dark color depths, respectively. For example, for the blue color scheme corresponding to 110kV, the three corresponding color depths are: light blue (#ADD8E6), medium blue (#6495ED), and dark blue (#00008B). Each power supply area is configured in this manner to form a color attribute set. An energy storage configuration scale distribution map with color differences is then generated based on the color attribute set. This ensures that the energy storage configuration scale of each power supply area is clearly represented in the energy storage configuration scale distribution map, allowing users to intuitively identify the voltage level of the power supply area and the required energy storage configuration scale level.

[0112] In some embodiments, the method for generating a substation energy storage configuration scale distribution map further includes:

[0113] Electricity price information is obtained, and a data processing method is used to establish an electricity price prediction model. The electricity price prediction model is used to predict electricity price information within a preset time period.

[0114] Calculate the connection cost when connecting an energy storage power station within the power supply area;

[0115] Based on the electricity price information, the connection cost, historical load data, and the planned energy storage capacity, an energy storage power station access evaluation system is established. The energy storage power station access evaluation system is used to generate energy storage power station access schemes. The planned energy storage capacity refers to the energy storage power stations in the power supply area to be connected.

[0116] In this embodiment, historical electricity price information is obtained, and data processing algorithms are used to clean and normalize the electricity price information to obtain a standardized electricity price dataset. Feature engineering is performed on the electricity price dataset to extract feature vectors of electricity price changes. A time series analysis algorithm is used to establish an electricity price prediction model based on the feature vectors, thereby obtaining the time pattern and trend of electricity price changes, and thus obtaining electricity price information within a preset time period.

[0117] In this embodiment, the spatial distribution data of substations is obtained by acquiring the geographical coordinates of all substations within the power supply area. The straight-line distance between the energy storage power station to be connected and each substation is calculated. Based on the straight-line distance and the price parameters when connecting the energy storage power station, the connection cost is calculated. Specifically, a cost-benefit analysis method is used to calculate the investment payback period T, where T = total investment cost / (annual revenue - annual operating cost). Furthermore, the expected lifespan L of the project is preset, and the expected internal rate of return of the project is obtained using a financial model calculation method. Further, a discount rate and the net cash inflow of the project in each future period are preset to calculate the net present value (NPV) of the project each year, where NPV = ∑(future cash flow / (1 + discount rate)^years) - initial investment cost, where the initial investment cost is the connection cost. Then, the total revenue M of the project over its expected lifespan is calculated, M = NPV × L. The life-cycle cost method is used to calculate the overall life-cycle cost of the project, where the total project cost C = initial investment cost + annual operation and maintenance cost + final scrap cost. By comparing the total revenue M and the total project cost C, economic indicators for connecting energy storage power stations in each power supply area are obtained, thus obtaining feasibility assessment results for connecting energy storage power stations in different power supply areas. Furthermore, through power grid flow calculations and stability analysis, the impact of connecting energy storage power stations in power supply areas on grid parameters such as voltage, current, and power is obtained. It is determined whether connecting energy storage power stations in each power supply area will cause problems such as voltage exceeding limits and line overload within the power supply area, thus obtaining stability assessment results for connecting energy storage power stations in different power supply areas. An objective weighting method is adopted to establish an evaluation system for energy storage power station access. Experts score the impact of various factors within the power supply area to obtain a comprehensive score for each area. These factors include economic indicators, stability, historical load data, and the scale of energy storage configuration. Based on the comprehensive score of each area, the percentage of each area's score among all power supply areas is calculated. This percentage is then used to generate an allocation scheme for the planned energy storage capacity, i.e., the energy storage power station access scheme. Specifically, if the power supply area has three areas, A, B, and C, and their comprehensive scores are 30, 50, and 80 respectively, then the percentage of A is 30 / (30+50+80) = 0.18. Similarly, the percentage of B is 0.32, and the percentage of C is 0.5. Therefore, when allocating the planned energy storage capacity, the allocation is based on the percentage of scores for each of the three areas to determine the allocated planned energy storage capacity for each area. When allocating planned energy storage capacity, it can be configured according to the energy storage power station access plan. This method can provide more sufficient decision-making basis for the approval of energy storage power station access and the investment of energy storage projects, guide the rational layout and orderly investment of energy storage, and provide sustainable development for the healthy growth of the energy storage industry.

[0118] In some embodiments, the method for generating a substation energy storage configuration scale distribution map further includes:

[0119] A power supply area database is set up to store energy storage configuration information for each power supply area. The energy storage configuration information includes basic data of the power supply area and basic data of the substation.

[0120] Based on data visualization methods, the energy storage configuration information of each power supply area is displayed on the energy storage configuration scale distribution map;

[0121] A time selector is set up, which is used to preset the time granularity. Based on the different preset time granularities, the energy storage configuration information is dynamically displayed on the energy storage configuration scale distribution map.

[0122] In this embodiment, the power supply areas within the power supply zone are numbered according to voltage levels. For example, A represents a 500kV power supply area, B represents a 220kV power supply area, and C represents a 110kV power supply area. Different power supply areas at the same voltage level are numbered according to numbers, such as A1, A2, B1, B2, etc., thereby obtaining the numbering information for each power supply area. The numbering information is stored in the power supply area database. The basic information of the power supply area includes the numbering information, the number of substations, the load utilization rate, and the energy storage configuration scale of the power supply area at different time granularities. The basic data of the substations includes the available output, the minimum technical output, and the installed capacity of the energy storage station. The above data is displayed on the energy storage configuration scale map using a data visualization method.

[0123] In some embodiments, the energy storage configuration scale distribution map is a dynamic interactive map. When a user clicks on a power supply area, a pop-up window appears, displaying the basic information of the aforementioned power supply area, providing intuitive information support for power grid planning and management decisions.

[0124] In some embodiments, a time selector is also provided. The time selector has a preset time granularity. The time selector is connected to the power supply area database. When the user selects a different time granularity, the data related to the time granularity in the power supply area database is dynamically displayed in a pop-up window, thereby obtaining an energy storage configuration scale distribution map with good human-computer interaction.

[0125] Please see Figure 2 The present invention also provides a system for generating a distribution map of substation energy storage configuration scale, the system comprising:

[0126] Power supply area division module 201: used to divide the power supply area into multiple power supply areas according to the voltage level;

[0127] Peak shaving profit and loss calculation module 202: used to calculate the peak shaving profit and loss of multiple power supply areas respectively;

[0128] The energy storage capacity calculation module 203 is used to calculate the energy storage capacity to be configured after connecting energy storage power stations in each power supply area according to the peak shaving surplus and deficit. The energy storage capacity to be configured includes a first energy storage capacity to be configured and a second energy storage capacity to be configured. When calculating the first energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is the same as the voltage level of the power supply area. When calculating the second energy storage capacity to be configured, the voltage level of the energy storage power station connected in the power supply area is lower than the voltage level of the power supply area.

[0129] Energy storage configuration scale determination module 204: used to determine the energy storage configuration scale of each power supply area by comparing the first energy storage scale to be configured and the second energy storage scale to be configured;

[0130] Energy storage configuration scale distribution map drawing module 205: used to draw an energy storage configuration scale distribution map of each power supply area based on the energy storage configuration scale of each power supply area. The energy storage configuration scale distribution map includes power supply areas of different voltage levels, and each power supply area includes the energy storage configuration scale corresponding to the power supply area.

[0131] It is understandable that, such as Figure 1 The content of the substation energy storage configuration scale distribution map generation method embodiment shown is applicable to the substation energy storage configuration scale distribution map generation system embodiment. The specific functions implemented by the substation energy storage configuration scale distribution map generation system embodiment are as follows: Figure 1 The method for generating the substation energy storage configuration scale distribution map shown is the same as the embodiment, and the beneficial effects achieved are the same as those described above. Figure 1 The beneficial effects achieved by the embodiment of the method for generating the scale distribution map of substation energy storage configuration are also the same.

[0132] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A substation energy storage configuration scale distribution map generation method, characterized by, The method comprises the following steps: According to the voltage level, the power supply area is divided into multiple power supply areas; Obtain the maximum load value and the minimum load value of each power supply area, and calculate the peak regulation profit and loss of multiple power supply areas respectively, wherein the peak regulation profit and loss = peak regulation demand - peak regulation capacity, the peak regulation demand = maximum load value - minimum load value, and the peak regulation capacity = (available output - minimum technical output) + energy storage power station installed capacity × 2; According to the peak regulation profit and loss, the to-be-configured energy storage scale after the energy storage power station is connected in each power supply area is calculated, which includes the first to-be-configured energy storage scale and the second to-be-configured energy storage scale, wherein when the first to-be-configured energy storage scale is calculated, the voltage level of the energy storage power station connected in the power supply area is the same as the voltage level of the power supply area, and when the second to-be-configured energy storage scale is calculated, the voltage level of the energy storage power station connected in the power supply area is lower than the voltage level of the power supply area, and the to-be-configured energy storage scale = min{energy storage capacity, peak regulation shortage amount / 2}, wherein the peak regulation shortage amount is the numerical value of the peak regulation profit and loss; Compare the first to-be-configured energy storage scale and the second to-be-configured energy storage scale, and take the smaller one as the energy storage configuration scale of each power supply area; Based on the energy storage configuration scale of each power supply area, draw the energy storage configuration scale distribution diagram of each power supply area in the power supply area, which includes power supply areas of different voltage levels, and each power supply area includes the energy storage configuration scale corresponding to the power supply area; Wherein, obtaining the maximum load value and the minimum load value of each power supply area comprises: Obtain all historical load data in the power supply area within a preset time and a preset time granularity, and calculate the load utilization rate P of each day within the preset time, wherein, , is an average load value, , P sum is a total load value, representing the total value of load data within a day, t sum is a total time granularity value, representing the number of time granularities divided within a day, max(p t -p t-1 ) represents the maximum value of the difference of load data between adjacent time granularities, p t represents the load data at time granularity t, p t-1 represents the load data at time granularity t-1; Determine the load peak day by comparing the numerical value of the load utilization rate of each day; Classify the historical load data of the load peak day according to the time granularity to form a load data set under different time granularities; Based on the load data set, draw the daily load change curve of the load data set, and obtain the maximum load value and the minimum load value of the daily load change curve.

2. The method of claim 1, wherein, According to the voltage level, the power supply area is divided into multiple power supply areas, which comprises: Obtain the geographical position data of the transformer substation under each voltage level in the power supply area, and mark the geographical position of the transformer substation on the map of the power supply area by using geographic information system technology; Based on the geographical position of the transformer substation and the layout of the power supply line in the power supply area, connect the transformer substations of the same voltage level, and determine multiple power supply areas.

3. The method of claim 1, wherein, Obtain the unit parameters of the generator set and the installed capacity of the energy storage power station in each power supply area, wherein the unit parameters include the available output and the minimum technical output of the generator set, and the installed capacity of the energy storage power station is the energy storage scale of the energy storage power station that has been connected to the power supply area.

4. The method of claim 3, wherein, The method further comprises the following steps: Obtain the daily load curve in the preset time of the power supply area, classify the daily load curve, and obtain different typical day types; Perform time series analysis and clustering analysis on the daily load curves of the same typical day type to obtain the electricity consumption characteristics of different time periods; The decision tree algorithm is adopted to perform time granularity division on different time periods based on the power consumption features, and a preliminary time granularity division scheme is generated. Analog testing is adopted to generate time granularity preset strategies based on different time granularity division schemes for different typical day types.

5. The method of claim 1, wherein, The to-be-configured energy storage scale after the energy storage power station is connected in each power supply area is calculated according to the peak regulation profit and loss, including: If there is a shortage in the peak regulation of the power supply area, the connection condition of the energy storage power station connected in the power supply area is obtained, wherein the connection condition includes a difficulty coefficient of the implementation of the transmission and transformation engineering line path when the energy storage power station is connected and the interval number of the high-voltage side and the low-voltage side of the transformer substation in the power supply area, and the difficulty coefficient includes easy, general and difficult; If the difficulty coefficient is easy or general, the connectable energy storage capacity in the power supply area is calculated, and the energy storage capacity = interval number × transmission capacity, wherein the transmission capacity is the transmission capacity corresponding to the maximum conductor cross section of the transmission and transformation engineering line when the energy storage power station is connected according to the voltage level; If the difficulty coefficient is difficult, it is determined that the connectable energy storage capacity in the power supply area is 0; The to-be-configured energy storage scale of the power supply area is calculated based on the energy storage capacity and the peak regulation shortage amount.

6. The method of claim 1, wherein, The method further includes: Obtaining electricity price information, using a data processing method to establish an electricity price prediction model, and the electricity price prediction model is used to predict the electricity price information within a preset time; Calculating the connection cost when the energy storage power station is connected in the power supply area; Based on the electricity price information, the cost information, the historical load data and the to-be-planned energy storage installed capacity, an energy storage power station connection evaluation system is established, and the energy storage power station connection evaluation system is used to generate an energy storage power station connection scheme, wherein the to-be-planned energy storage installed capacity is the energy storage power station to be connected in the power supply area.

7. The method of claim 1, wherein, The method further includes: Setting a power supply area database, which is used to store the energy storage configuration information of each power supply area, wherein the energy storage configuration information includes power supply area basic data, transformer substation basic data and energy storage configuration scale of the power supply area under different time granularity; Based on a data visualization method, the energy storage configuration information of each power supply area is displayed in the energy storage configuration scale distribution diagram; Setting a time selector, which is used to preset the time granularity, and dynamically displaying the energy storage configuration information in the energy storage configuration scale distribution diagram according to different preset time granularities.

8. A substation energy storage configuration scale profile generation system, characterized by, It includes: A power supply area division module is used to divide the power supply area into multiple power supply areas according to the voltage level; A peak regulation profit and loss calculation module is used to obtain the maximum load value and the minimum load value of each power supply area, and to calculate the peak regulation profit and loss of multiple power supply areas, wherein the peak regulation profit and loss = peak regulation demand - peak regulation capacity, the peak regulation demand = maximum load value - minimum load value, and the peak regulation capacity = (utilizable output - minimum technical output) + energy storage power station installed capacity × 2; The to-be-configured energy storage scale calculation module is configured to calculate a to-be-configured energy storage scale of each power supply area after an energy storage power station is connected to the power supply area according to the peak regulation profit and loss, the to-be-configured energy storage scale including a first to-be-configured energy storage scale and a second to-be-configured energy storage scale, wherein the first to-be-configured energy storage scale is calculated when the voltage level of the energy storage power station connected to the power supply area is the same as the voltage level of the power supply area, the second to-be-configured energy storage scale is calculated when the voltage level of the energy storage power station connected to the power supply area is lower than the voltage level of the power supply area, and the to-be-configured energy storage scale is min{energy storage capacity, peak regulation shortage amount / 2}, wherein the peak regulation shortage amount is a numerical value of the peak regulation profit and loss; The energy storage configuration scale determination module is configured to compare the first to-be-configured energy storage scale and the second to-be-configured energy storage scale, and determine the smaller one as the energy storage configuration scale of each power supply area; The energy storage configuration scale distribution map drawing module is configured to draw an energy storage configuration scale distribution map of each power supply area in the power supply area based on the energy storage configuration scale of each power supply area, the energy storage configuration scale distribution map including power supply areas of different voltage levels, and each power supply area including an energy storage configuration scale corresponding to the power supply area; The maximum load value and the minimum load value of each power supply area are obtained by: Obtain all historical load data in the power supply area within a preset time and a preset time granularity, and calculate the load utilization rate P of each day within the preset time, wherein, , is an average load value, , P sum is a total load value, representing the total value of load data within a day, t sum is a total time granularity value, representing the number of time granularities divided within a day, max(p t -p t-1 ) represents the maximum value of the difference of load data between adjacent time granularities, p t represents the load data at time granularity t, and p t-1 represents the load data at time granularity t-1. determining a load peak day by comparing the numerical values of the load utilization rates of each day; classifying the historical load data of the load peak day according to the time granularity to form load data sets under different time granularities; drawing a daily load change curve of the load data set based on the load data set, and obtaining the maximum load value and the minimum load value of the daily load change curve.

Images