Energy storage configuration method, device and medium for new energy storage access system

Abstract

This application relates to the technical field of new energy energy storage systems, and specifically relates to an energy storage configuration method, device, and medium for new energy energy storage access systems, which specifically include: analyzing the power flow path between any two nodes in the power grid through a power flow diagram; partitioning the power grid through the similarity of parameter changes at each node and the degree of chaos of the node voltage deviation values in the power flow path; analyzing the potential line blocking probability and line overload rate of each power line at each moment to calculate the partition blocking evaluation value of each power grid partition; obtaining the total new energy generation and new energy penetration rate of each power grid partition, combining the partition blocking evaluation value, and using an evaluation algorithm to determine the evaluation score of each power grid partition; performing energy storage configuration for each power grid partition based on the evaluation score; being able to avoid the line blocking problem in the new energy-rich power grid partition, promote new energy consumption, and avoid the problem of local power grid overload causing cascading failures.

Description

Technical Field

[0001] This application relates to the technical field of new energy energy storage systems, and specifically relates to an energy storage configuration method, device, and medium for new energy energy storage access systems. Background Art

[0002] In recent years, new energy in China has still maintained a high-speed development trend, and the proportion of renewable energy such as solar energy and wind energy in the power system has been continuously increasing. However, new energy power generation has the characteristics of intermittency and volatility. Through the new energy energy storage access system, the new energy power generation can be stored and released, improving the peak regulation ability of the power grid, reducing the phenomenon of abandoned wind and light, and optimizing the utilization efficiency of new energy and the stability of the power grid.

[0003] In the actual power system, new energy-rich areas and power load concentration areas are distributed in a reverse manner. A large amount of new energy power needs to be consumed in different places, and the transmission lines in new energy-rich areas are prone to congestion during peak new energy output periods. The traditional solution is to expand and transform the lines to increase the capacity of the power grid lines, which will inevitably cause waste of overall resources. The new energy energy storage access system can store and release the electric energy of the power grid. Due to the "sporadic" nature of line congestion, it is difficult to identify potential line congestion, which is likely to increase the light abandonment rate and even cause power grid chain failures. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an energy storage configuration method, device, and medium for new energy energy storage access systems. The specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of this application provides an energy storage configuration method for a new energy energy storage access system. The method includes the following steps:

[0006] Construct a topological model through the connection relationships and parameters of each component in the new energy power grid, and obtain the power flow diagram of the topological model at each moment;

[0007] Calculate the line overload rate of each power grid line at each moment based on the active power flow and the per-unit value of the line transmission capacity of each power grid line in the power flow diagram at each moment; analyze the power flow path between any two nodes in the power grid through the power flow direction and line overload rate of the power grid lines in the power flow diagram at the moment of maximum photovoltaic output;

[0008] Record the difference between the voltage value of each node at each moment and the rated voltage as the voltage deviation value; determine the electrical distance between any two nodes based on the similarity between the data changes of the voltage deviation values of different nodes and the degree of chaos of the voltage deviation values of all nodes in the power flow path, and partition the power grid based on the electrical distance;

[0009] Within each power grid partition, the line capacity of each power injection line passing through any power grid line is used to perform weighted analysis on the changing trend of the power flow of each power injection line, calculate the line potential blocking probability of each power line at each moment; calculate the line blocking risk degree of each power grid line at each moment based on the line overload rate and line potential blocking probability of each power grid line at each moment, and determine the partition blocking evaluation value of each power grid partition based on the line blocking risk degree of all power grid lines in each power grid partition;

[0010] Based on the new energy penetration data and partition blocking evaluation value of each power grid partition, determine the evaluation score of each power grid partition; perform energy storage configuration for each power grid partition based on the evaluation score.

[0011] In one embodiment, the line overload rate of each power grid line at each moment is: the ratio of the active power flow of each power grid line in the power flow diagram at each moment to the per-unit value of the line transmission capacity of each power grid line.

[0012] In one embodiment, the process of obtaining the power flow path between any two nodes in the power grid is as follows:

[0013] Take the power flow direction of each power grid line in the power flow diagram at the moment of maximum PV output as the direction of the corresponding edge in the topological model, and the reciprocal of the line overload rate as the corresponding edge weight in the topological model to obtain a directed weighted graph. Use the directed weighted graph as the input of the shortest path algorithm, and the output is the shortest path between any two nodes, denoted as the power flow path.

[0014] In one embodiment, the process of obtaining the electrical distance between any two nodes is as follows:

[0015] Denote the sequence composed of the voltage deviation values of each node at all moments within a preset time period as the voltage deviation sequence of each node; denote the similarity between the voltage deviation sequences of any two nodes as the first similarity;

[0016] In the power flow path between any two nodes, calculate the degree of chaos of the voltage deviation values of all nodes, denoted as the first chaos degree;

[0017] Take the normalized value of the ratio of the first chaos degree to the first similarity as the electrical distance between any two nodes.

[0018] In one embodiment, the process of obtaining the line potential blocking probability of each power line at each moment is as follows:

[0019] Obtain the sequence composed of the power flow powers at all times within a preset time period before any moment of each power injection line, denoted as the power flow power sequence, input it into the trend test algorithm, and obtain the trend statistic of the power flow power sequence as the short-term trend eigenvalue of each power injection line at the said any moment; Denote the line potential blocking probability of power line i at time t as , The expression of

[0020] ;

[0021] In the formula, n is the total number of power injection lines of power grid line i, is the power flow weight of the jth power injection line of power grid line i to power grid line i, is the short-term trend eigenvalue of the jth power injection line of power grid line i at time t; where, , in the formula, are the line capacities of power grid line i and its jth power injection line respectively.

[0022] In one embodiment, the process of obtaining the partition blocking evaluation value of each power grid partition is as follows:

[0023] Take the sum of the line overload rate and the line potential blocking probability of each power grid line at each moment as the line blocking risk degree of each power grid line at each moment;

[0024] Denote the partition blocking evaluation value of the xth power grid partition as , The expression of , in the formula, is the total number of power grid lines in the xth power grid partition, is the line blocking risk degree of the th power grid line in the xth power grid partition at the moment of maximum PV output.

[0025] In one embodiment, the process of obtaining the evaluation score of each power grid partition is as follows:

[0026] Denote the vector composed of the total new energy generation, new energy penetration rate and partition blocking evaluation value of each power grid partition as the evaluation vector, take the evaluation vectors of all power grid partitions as the input of the evaluation algorithm, and the output is the evaluation score of each power grid partition.

[0027] In one embodiment, the energy storage configuration of each power grid partition based on the evaluation score is specifically as follows:

[0028] The segmentation threshold is obtained from the evaluation scores of all power grid partitions; the power grid partitions with evaluation scores less than the segmentation threshold are used as energy storage configuration partitions; among the energy storage configuration partitions at the moment of maximum photovoltaic output, the power grid line with the highest line congestion risk degree is selected as the access point of the energy storage system, the energy storage system configuration address is determined, and energy storage is configured.

[0029] Second, the embodiment of the present application also provides an energy storage configuration device for a new energy energy storage access system, including:

[0030] Model construction module: construct a topological model through the connection relationships between components and component parameters in the new energy power grid, and obtain the power flow diagrams at each moment of the topological model;

[0031] Path analysis module: calculate the line overload rate of each power grid line at each moment based on the active power flow of each power grid line and the per-unit value of the line transmission capacity in the power flow diagrams at each moment; analyze the power flow paths between any two nodes in the power grid through the power flow direction and line overload rate of the power grid lines in the power flow diagram at the moment of maximum photovoltaic output;

[0032] Power grid partition module: record the difference between the voltage value of each node and the rated voltage at each moment as the voltage deviation value; determine the electrical distance between any two nodes based on the similarity between the data changes of the voltage deviation values of different nodes and the degree of chaos of the voltage deviation values of all nodes in the power flow path, and perform power grid partitioning based on the electrical distance;

[0033] Partition congestion assessment module: within each power grid partition, perform weighted analysis on the change trend of the power flow power of each power injection line through the line capacity of each power injection line of any power grid line, and calculate the line potential congestion probability of each power line at each moment; calculate the line congestion risk degree of each power grid line at each moment according to the line overload rate and line potential congestion probability of the power grid lines at each moment, and determine the partition congestion assessment value of each power grid partition based on the line congestion risk degree of all power grid lines in each power grid partition;

[0034] Energy storage configuration module: determine the evaluation scores of each power grid partition according to the new energy penetration data and partition congestion assessment values of each power grid partition; perform energy storage configuration for each power grid partition based on the evaluation scores.

[0035] Third, the embodiment of the present application also provides an energy storage configuration medium for a new energy energy storage access system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in the first aspect above are implemented.

[0036] The embodiment of the present application has at least the following beneficial effects:

[0037] This application can avoid the line congestion problem in the new energy-rich power grid partition, identify potential line congestion situations, promote the consumption of new energy, enhance cross-regional power trading, consider the safety constraints of power grid lines and power flow, timely configure the energy storage system for power storage, avoid the problem of local power grid overload causing cascading failures, and improve the emergency response ability of the new energy energy storage access system;

[0038] This application uses the electrical distance as the reachable distance for power grid partitioning, which can avoid grid nodes that are physically adjacent but have little power exchange being assigned to the same power grid partition, divide the complex power system into multiple relatively independent power grid regions, and improve the accuracy of power grid partitioning; according to the power grid partitioning results for energy storage configuration, it can avoid the energy storage system configuration falling into "global optimum", prevent local areas of the power grid from overloading, improve the stability of power grid operation, facilitate more accurate identification of the bottleneck of new energy consumption in the future, and enhance the flexibility of energy storage configuration; the constructed line congestion risk degree of the power grid line can accurately identify potential line congestion situations in scenarios of large scale, high complexity, high uncertainty of new energy power generation, and rapid development of new energy power generation, and realize the risk quantification of "sporadic" line congestion; through the energy storage configuration method, it promotes the consumption of new energy, enhances cross-regional power trading, considers the safety constraints of power grid lines and power flow, timely configures the energy storage system for power storage, avoids the problem of local power grid overload causing cascading failures, and improves the emergency response ability of the new energy energy storage access system. Description of the Drawings

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 It is a flowchart of the steps of the energy storage configuration method for the new energy energy storage access system provided by an embodiment of the present application;

[0041] Figure 2 It is a schematic diagram of the acquisition process of the electrical distance;

[0042] Figure 3 It is a schematic diagram of the structure of the energy storage configuration device for the new energy energy storage access system. Detailed Embodiments

[0043] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on the energy storage configuration method, device, and medium for a new energy storage access system proposed according to this application, its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0045] The following will specifically describe the specific solutions of the energy storage configuration method, device, and medium for a new energy storage access system provided by this application in conjunction with the accompanying drawings.

[0046] Please refer to Figure 1 , which shows the step flowchart of the energy storage configuration method for a new energy storage access system provided by an embodiment of this application. The method includes the following steps:

[0047] Step S1, construct a topological model through the connection relationships and parameters of each component in the new energy power grid, and obtain the power flow diagrams of the topological model at each moment.

[0048] Obtain the network wiring topological diagram of the new energy power grid area to determine the connection relationships of each component in the power grid, and obtain the detailed parameters of each component. In the embodiments of this application, the components include lines, generators, and transformers. The detailed parameters of the components include the length, model, resistance, and reactance of the lines, the rated power, rated voltage, and synchronous reactance of the generators, and the rated voltage, rated capacity, and short-circuit loss of the transformers. For new energy power generation equipment, collect the installed capacity and power generation characteristic curves of the new energy power generation equipment.

[0049] According to the collected component parameters, define various components in the power grid and new energy power generation equipment in the component definition module of the PSASP software in sequence, and connect each component in the network wiring module of the PSASP through the connection relationships of the components to construct a complete topological model of the new energy power system. The new energy power generation equipment generates electricity at full capacity, and use the PSASP software to perform power flow calculations to obtain the power flow diagrams of the new energy power system at each acquisition moment. Among them, in the embodiments of this application, the acquisition time interval is set to 15 minutes, that is, a power flow diagram is obtained every 15 minutes. As other embodiments of this application, the implementer can set the acquisition time interval according to the actual situation.

[0050] Step S2: Calculate the line overload rate of each grid line at each moment based on the active power flow and the per-unit value of the line transmission capacity in the power flow diagram at each moment; analyze the power flow path between any two nodes in the power grid through the power flow direction and the line overload rate of the grid lines in the power flow diagram at the moment of maximum PV output.

[0051] In the complete topological model of a new energy power system, there are a large number of various components. If energy storage is configured uniformly throughout the power grid, the complexity is high, and it is easy to fall into "global optimality", resulting in overload operation in local areas of the power grid and significantly reducing the emergency response ability of the local power grid. The coupling between nodes and lines in a relatively independent power grid partition is strong, providing similar voltage support capabilities for nodes. By adjusting the power control amount of only one line in the power grid partition, the accommodation ability and emergency response ability of the local area of the power grid can be improved. The power grid partition is more suitable for energy storage configuration.

[0052] For the power flow diagram at each moment, obtain the active power flow of each grid line in the power flow diagram, and at the same time obtain the per-unit value of the line transmission capacity of each grid line. Take the ratio of the active power flow to the per-unit value of the line transmission capacity as the line overload rate of each grid line at this moment. The higher the line overload rate of a grid line, the more frequent the power exchange between the nodes on both sides of the line. To prevent overload in the local power grid and cause cascading failures, they should be assigned to the same power grid partition. Among them, the calculation of the per-unit value of the line transmission capacity is a well-known technology, and the specific process will not be elaborated.

[0053] New energy power systems mostly use photovoltaic power generation equipment. At the moment of maximum PV output, the power system most needs to accommodate PV power. In the embodiments of the present application, the moment of maximum PV output is set to 13:00. As other embodiments of the present application, the implementer can set the moment of maximum PV output according to the actual situation.

[0054] Take the power flow direction of each grid line in the power flow diagram at the moment of maximum PV output as the direction of the corresponding edge in the topological model of the power system to obtain an unweighted topological graph with power flow direction. Further, take the reciprocal of the line overload rate of each grid line as the edge weight corresponding to the unweighted topological graph to generate a directed weighted graph based on the line overload rate. Use this directed weighted graph as the input of the Dijkstra algorithm, and the output is the shortest path between any two nodes, which is used as the power flow path between these two nodes. The more frequent the power exchange between adjacent nodes within the shortest path, the closer it is to the actual power flow state. Among them, the Dijkstra algorithm is a well-known technology, and the specific process will not be elaborated.

[0055] It should be noted that for obtaining the shortest path between nodes, this application only provides one method for finding the shortest path. There are many existing methods for finding the shortest path, and implementers can also use other shortest path algorithms to calculate the shortest path between nodes. This application does not make specific restrictions.

[0056] Step S3: Record the difference between the voltage value of each node at each moment and the rated voltage as the voltage deviation value; determine the electrical distance between any two nodes based on the similarity between the data changes of the voltage deviation values of different nodes and the degree of chaos of the voltage deviation values of all nodes in the power flow path, and perform power grid zoning based on the electrical distance.

[0057] Record the difference between the voltage value of each node at each moment and the rated voltage as the voltage deviation value of each node at each moment. Obtain the voltage deviation values of all collection moments within 24 hours for each node and arrange them in ascending order of time to form the voltage deviation sequence of each node. Calculate the similarity between the voltage deviation sequences of any two nodes, denoted as the first similarity. Preferably, in the embodiments of this application, the first similarity can be the sum of the Pearson correlation coefficient between the voltage deviation sequences of any two nodes and a first parameter, where the first parameter is a preset small positive number greater than 1, and its function is to prevent the first similarity from being 0. Preferably, in the embodiments of this application, the first parameter is set to 1.1. Among them, the Pearson correlation coefficient is a well-known technology, and the specific process will not be elaborated. In other embodiments of this application, implementers can also use other similarity algorithms to calculate the similarity between the voltage deviation sequences of any two nodes.

[0058] For the power flow path between any two nodes, at the moment of maximum photovoltaic output, arrange the voltage deviation values of all nodes in the power flow path in the order in which they appear in the path to form the path voltage support sequence of the power flow path. Calculate the degree of chaos of all elements in the path voltage support sequence, denoted as the first chaos degree. Preferably, in the embodiments of this application, the first chaos degree can be the information entropy of all elements in the path voltage support sequence. In other embodiments of this application, the first chaos degree can also be the variance of all elements in the path voltage support sequence.

[0059] Based on the above analysis, this application obtains the electrical distance between any two nodes in the power system through the following formula, and the expression is:

[0060] ;

[0061] In the formula, is the electrical distance between node a and node b, is the first chaos degree of the path voltage support sequence between node a and node b, is the first similarity between node a and node b is a normalization function.

[0062] Relatively independent power grid partitions provide similar voltage support for internal nodes, and nodes within the power flow path have similar voltage levels. The smaller it is, the more likely it is to be a power grid partition; at the same time, when the synchronicity of voltage deviation fluctuations between two nodes is higher, that is The larger it is, the stronger the coordination of voltage changes between the two nodes. Only by adjusting one node can the accommodation capacity of the power grid partition be improved, and the electrical distance between the two nodes is closer.

[0063] Furthermore, since new energy is usually connected to the middle or end of the power grid, the topological structure is relatively sparse. Moreover, due to reasons such as spare lines and isolated operation of microgrids, there are cases where two power grid lines are connected, but there is actually not much power exchange. To prevent the problem that power grid nodes with low power exchange between adjacent lines are assigned to the same power grid partition, this application partitions the power grid using the OPTICS algorithm based on the electrical distance of any node combination. Among them, the neighborhood radius in the OPTICS algorithm is set to 0.2, and the minimum sample number is set to 5. For any node, the number of natural neighbors within its neighborhood radius is counted. If the number of natural neighbors is greater than or equal to the minimum sample number, it is marked as a core point. The electrical distance is used as the reachable distance between two core points, and the output is the power grid partition result. Among them, the OPTICS algorithm is a well-known technology, and the specific process will not be elaborated here. It should be noted that for power grid partitioning, this application only provides a density clustering method. There are many existing density clustering methods, and implementers can also use other density clustering algorithms to partition the power grid. This application does not make specific restrictions.

[0064] Through the power grid partitioning technology, this application can avoid power grid nodes that are physically adjacent but have little power exchange being assigned to the same power grid partition, divide the complex power system into multiple relatively independent power grid regions, facilitate subsequent energy storage configuration according to the new energy access situation and power grid carrying capacity of each power grid region, can more accurately identify the bottleneck of new energy accommodation, and improve the flexibility of energy storage configuration.

[0065] Step S4, within each power grid partition, perform weighted analysis on the change trend of the power flow power of each power injection line through the line capacity of each power injection line of any power grid line, calculate the line potential blocking probability of each power line at each moment; calculate the line blocking risk degree of each power grid line at each moment according to the line overload rate and line potential blocking probability of each power grid line at each moment, and determine the partition blocking evaluation value of each power grid partition based on the line blocking risk degree of all power grid lines in each power grid partition.

[0066] The development of the power grid on the source-grid-load side shows a significant imbalance. In the actual power system, a pattern of reverse distribution between the new energy-rich areas and the power load concentration areas is formed. Due to the randomness and volatility of new energy power generation, the transmission lines in the new energy-rich areas are prone to congestion, increasing the operating cost of the system and reducing the reliability of the power grid. This application avoids complex unit conversions through the line overload rate, simplifies the analysis of the power system, and can intuitively judge the operating state of the line on the surface. However, the existing power grid topology has a high complexity, and line congestion belongs to "sporadic" congestion. Simply identifying line congestion based on the line overload rate, there is still a potential congestion risk for the power grid lines, laying a hidden danger for the stable operation of the power grid.

[0067] (1) Arbitrarily select a power grid line i within the power grid partition, and obtain all the power grid lines whose power flows into the power grid line i within the power grid partition. The set formed is denoted as the power injection line set , and Any one of the power grid lines in is used as the power injection line of the power grid line i. Since the line capacity of the power grid line directly affects the power transmission effect and is an important cause of line congestion, this application calculates the power flow weight of each power injection line on the power grid line i through the difference between the line capacity of each power injection line of the power grid line i and the line capacity of the power grid line i. The expression is:

[0068] ;

[0069] In the formula, is the power flow weight of the jth power injection line of the power grid line i on the power grid line i, is the line capacity of the power grid line i, is the line capacity of the jth power injection line of the power grid line i.

[0070] The larger the line capacity of the power injection line, the more power it can provide for the power grid line in the direction of its power flow, and the greater the power flow weight. When the power flow of the power injection line changes, it can bring a greater power flow impact to the corresponding power grid line.

[0071] (2) Taking the jth power injection line of the power grid line i as an example in this application, for the power flow power of this power injection line at any moment, obtain the power flow powers at all moments within 2 hours before the any moment, arrange them in ascending order of time, and the obtained sequence is denoted as the power flow power sequence of this power injection line at the any moment. Use this power flow power sequence as the input of the Mann-Kendall method, and obtain the trend statistic of this power flow power sequence as the short-term trend eigenvalue of this power injection line at the any moment. Among them, the Mann-Kendall method is a well-known technology, and the specific process will not be elaborated.

[0072] It should be noted that for the trend test of the power flow power sequence, this application only provides one trend test method. There are many existing trend test methods, and implementers can also use other trend test algorithms to perform trend tests on the power flow power sequence.

[0073] Furthermore, the line potential blocking probability of power grid line i at each moment is obtained through the short-term trend eigenvalue, and the expression is:

[0074] ;

[0075] In the formula, is the line potential blocking probability of power grid line i at time t, n is the total number of power injection lines of power grid line i, is the power flow weight of the j-th power injection line of power grid line i to power grid line i, is the short-term trend eigenvalue of the j-th power injection line of power grid line i at time t.

[0076] This application reflects the power flow power growth trend of the power injection line through the short-term trend eigenvalue. The larger it is, the stronger the power supply capacity of the power grid partition to power grid line i at time t. At the same time, when is larger, it means that the power grid partition injects more power into power grid line i, and the potential blocking probability of the power grid line is higher, that is, is larger.

[0077] So far, the line overload rate and line potential blocking probability of the power grid line at each moment are obtained. The line overload rate is used to reflect the real operation state of the power grid line, and the line potential blocking probability is used to reflect the potential blocking risk of the power grid line. The sum of the line overload rate and the line potential blocking probability of the power grid line at each moment is used as the line blocking risk degree of the power grid line at each moment, which reflects the possibility of line blocking occurring in the current moment and in the short term of the power grid line, and realizes the risk quantification of "sporadic" line blocking.

[0078] If an energy storage system is configured in the power grid partition rich in new energy, when the power grid is blocked or reaches the line blocking warning, the energy storage system stores the electric energy that cannot be transmitted, greatly reducing the possibility of line blocking, and releasing it again after the line blocking is lifted, which can effectively reduce the blocking degree of the transmission line, thereby promoting the consumption of new energy.

[0079] At the moment of maximum photovoltaic output, the partition blocking evaluation value of the power grid partition is calculated through the line blocking risk degree of all power grid lines in the power grid partition, and the expression is:

[0080] , in the formula, is the partition congestion assessment value of the x-th power grid partition, is the total number of power grid lines within the x-th power grid partition, is the line congestion risk degree of the -th power grid line within the x-th power grid partition at the moment of maximum PV output.

[0081] The higher the possibility of line congestion occurring in the power grid lines within a power grid partition, the easier it is for the power grid partition to form a congested partition, and the greater the probability of power failures.

[0082] Step S5: Determine the evaluation scores of each power grid partition based on the new energy penetration data and the partition congestion assessment values of each power grid partition; configure energy storage for each power grid partition based on the evaluation scores.

[0083] Calculate the cumulative sum of the installed capacities of all new energy power generation equipment within each power grid partition as the total new energy power generation of each power grid partition.

[0084] Furthermore, obtain the new energy penetration rate of each power grid partition. Among them, the calculation of the new energy penetration rate is a well-known technology, and the specific process will not be elaborated here.

[0085] Use the total new energy power generation to reflect the utilization rate of renewable resources in the power grid partition, use the new energy penetration rate to reflect the new energy acceptance capacity of the power grid partition, and use the partition congestion assessment value to reflect the possibility of power flow congestion in the power grid partition. Denote the vector composed of the total new energy power generation, new energy penetration rate, and partition congestion assessment value of each power grid partition as the evaluation vector, and use the evaluation vectors of all power grid partitions as the input of the TOPSIS algorithm (technique for order preference by similarity to an ideal solution). Among them, regard the total new energy power generation, new energy penetration rate, and partition congestion assessment value as extremely small indicators, and the output is the evaluation score of each power grid partition, which is used as the congestion margin of each power grid partition, comprehensively reflecting the safety margin of power flow congestion in the power grid partition. Among them, the TOPSIS algorithm is a well-known technology, and the specific process will not be elaborated here.

[0086] It should be noted that for the evaluation of all power grid partitions, this application only provides one evaluation method. There are many existing evaluation methods, and implementers can also use other evaluation algorithms to evaluate all power grid partitions. This application does not make specific restrictions.

[0087] Take the congestion margin of all power grid partitions as the input of the maximum cumulative variance, obtain the segmentation threshold, regard the power grid partitions with a congestion margin less than the segmentation threshold as the energy storage configuration partitions, and do not regard the power grid partitions with a congestion margin greater than or equal to the segmentation threshold as the energy storage configuration partitions. Select the power grid line with the highest line congestion risk in each energy storage configuration partition at the moment of maximum PV output as the access point of the energy storage system, and determine the configuration address of the energy storage system. And adopt the energy storage configuration method disclosed in the "Energy Storage System Capacity Configuration Method Considering the Uncertainty of Distributed Energy in Microgrid" with the publication number of "CN108599138B" to obtain the installed capacity of the energy storage system in each energy storage configuration partition.

[0088] The schematic diagram of the acquisition process of electrical distance is as Figure 2 shown.

[0089] Please refer to Figure 3 , Figure 3 which is the schematic diagram of the structure of the energy storage configuration device for the new energy storage access system provided by the embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in the corresponding embodiment of the energy storage configuration method for the new energy storage access system. Refer to Figure 3 , the energy storage configuration device includes:

[0090] Model construction module: construct a topological model through the connection relationship between each component in the new energy power grid and the parameters of each component, and obtain the power flow diagram of the topological model at each moment;

[0091] Path analysis module: calculate the line overload rate of each power grid line at each moment based on the active power flow of each power grid line and the per-unit value of the line transmission capacity in the power flow diagram at each moment; analyze the power flow path between any two nodes in the power grid through the power flow direction and line overload rate of the power grid line in the power flow diagram at the moment of maximum PV output;

[0092] Power grid partition module: record the difference between the voltage value of each node and the rated voltage at each moment as the voltage deviation value; determine the electrical distance between any two nodes based on the similarity between the data changes of different node voltage deviation values and the degree of chaos of the voltage deviation values of all nodes in the power flow path, and perform power grid partition based on the electrical distance;

[0093] Partition congestion evaluation module: within each power grid partition, perform weighted analysis on the change trend of the power flow power of each power injection line through the line capacity of each power injection line of any power grid line, and calculate the line potential congestion probability of each power line at each moment; calculate the line congestion risk degree of each power grid line at each moment according to the line overload rate and line potential congestion probability of each power grid line at each moment, and determine the partition congestion evaluation value of each power grid partition based on the line congestion risk degree of all power grid lines in each power grid partition;

[0094] Energy storage configuration module: Determine the evaluation scores of each power grid partition according to the new energy penetration data and partition congestion evaluation values of each power grid partition; perform energy storage configuration for each power grid partition based on the evaluation scores.

[0095] Based on the same inventive concept as the above method, an embodiment of the present application also provides an energy storage configuration medium for a new energy storage access system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above energy storage configuration methods for a new energy storage access system are implemented.

[0096] In summary, the embodiment of the present application provides an energy storage configuration method for a new energy storage access system, which can avoid the line congestion problem in the power grid partitions with rich new energy, identify potential line congestion situations, promote new energy consumption, enhance cross-regional power trading, consider the safety constraints of power grid lines and power flow, timely configure an energy storage system for power storage, avoid the problem of local power grid overload causing cascading failures, and improve the emergency response ability of the new energy storage access system;

[0097] Using the electrical distance as the reachable distance for power grid partitioning can avoid power grid nodes that are physically adjacent but have little power exchange being assigned to the same power grid partition, divide the complex power system into multiple relatively independent power grid regions, and improve the accuracy of power grid partitioning; performing energy storage configuration according to the power grid partition results can avoid the energy storage system configuration falling into "global optimum", prevent local areas of the power grid from overloading, improve the stability of power grid operation, and facilitate more accurate identification of new energy consumption bottlenecks in the future, and enhance the flexibility of energy storage configuration; the constructed line congestion risk degree of the power grid line can accurately identify potential line congestion situations in scenarios with large scale, high complexity, high uncertainty of new energy power generation, and high-speed development of new energy power generation, and realize the risk quantification of "sporadic" line congestion; through the energy storage configuration method, it promotes new energy consumption, enhances cross-regional power trading, considers the safety constraints of power grid lines and power flow, timely configures an energy storage system for power storage, avoids the problem of local power grid overload causing cascading failures, and improves the emergency response ability of the new energy storage access system.

[0098] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] Each embodiment in the present application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized.

[0100] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. Energy storage configuration method for new energy storage access system, characterized in that, The method includes the following steps: Construct a topological model based on the connection relationships and parameters of each component in the new energy power grid, and obtain the power flow diagrams of the topological model at each moment; Calculate the line overload rate of each power grid line at each moment based on the active power flow and the per-unit value of the line transmission capacity of each power grid line in the power flow diagrams at each moment; analyze the power flow path between any two nodes in the power grid through the power flow direction and line overload rate of the power grid lines in the power flow diagram at the moment of maximum photovoltaic output; Record the difference between the voltage value of each node at each moment and the rated voltage as the voltage deviation value; determine the electrical distance between any two nodes based on the similarity between the data changes of the voltage deviation values of different nodes and the degree of chaos of the voltage deviation values of all nodes in the power flow path, and perform power grid partitioning based on the electrical distance; Within each power grid partition, perform weighted analysis on the change trend of the power flow power of each power injection line through the line capacity of each power injection line of any power grid line, and calculate the line potential blocking probability of each power line at each moment; calculate the line blocking risk degree of each power grid line at each moment according to the line overload rate and line potential blocking probability of each power grid line at each moment, and determine the partition blocking evaluation value of each power grid partition based on the line blocking risk degrees of all power grid lines in each power grid partition; Determine the evaluation score of each power grid partition according to the new energy penetration data and partition blocking evaluation value of each power grid partition; perform energy storage configuration for each power grid partition based on the evaluation score; The process of obtaining the line potential blocking probability of each power line at each moment is as follows: Obtain the sequence composed of the power flow powers at all times within a preset time period before any moment on each power injection line, denoted as the power flow power sequence, input it into the trend test algorithm, and obtain the trend statistic of the power flow power sequence as the short-term trend eigenvalue of each power injection line at the said moment; Denote the line potential blocking probability of power line i at time t as , The expression of is: ； where n is the total number of power injection lines of grid line i, is the power flow weight of the j-th power injection line of grid line i to grid line i, is the short-term trend eigenvalue of the j-th power injection line of grid line i at time t; where, , in the formula, and are the line capacities of grid line i and its j-th power injection line, respectively.

2. The energy storage configuration method for a new energy energy storage access system according to claim 1, wherein The line overload rate of each power grid line at each moment is: the ratio of the active power flow of each power grid line in the power flow diagram at each moment to the per-unit value of the line transmission capacity of each power grid line.

3. The energy storage configuration method for a new energy storage access system according to claim 1, characterized in that The process of obtaining the power flow path between any two nodes in the power grid is as follows: Take the power flow direction of each power grid line in the power flow diagram at the moment of maximum photovoltaic output as the direction of the corresponding edge in the topological model, and the reciprocal of the line overload rate as the corresponding edge weight in the topological model to obtain a directed weighted graph. Take the directed weighted graph as the input of the shortest path algorithm, and the output is the shortest path between any two nodes, denoted as the power flow path.

4. The energy storage configuration method for a new energy energy storage access system according to claim 1, characterized in that The process of obtaining the electrical distance between any two nodes is as follows: Record the sequence composed of the voltage deviation values of each node at all moments within a preset time period as the voltage deviation sequence of each node; record the similarity between the voltage deviation sequences of any two nodes as the first similarity; Calculate the degree of chaos of the voltage deviation values of all nodes in the power flow path between any two nodes, denoted as the first chaos degree; Take the normalized value of the ratio of the first chaos degree to the first similarity as the electrical distance between any two nodes.

5. The energy storage configuration method for a new energy storage access system according to claim 1, characterized in that The process of obtaining the partition blocking evaluation value of each power grid partition is as follows: Take the sum of the line overload rate and line potential blocking probability of each power grid line at each moment as the line blocking risk degree of each power grid line at each moment; Let the partition congestion evaluation value of the xth power grid partition be denoted as , The expression of which is: In the formula, is the total number of power grid lines in the xth power grid partition, is the line congestion risk degree of the th power grid line in the xth power grid partition at the moment of maximum PV output.

6. The energy storage configuration method for a new energy energy storage access system according to claim 1, characterized in that The process of obtaining the evaluation score of each power grid partition is as follows: The new energy penetration data includes the total new energy power generation and the new energy penetration rate. Denote the vector composed of the total new energy power generation, the new energy penetration rate and the partition congestion evaluation value of each power grid partition as the evaluation vector. Take the evaluation vectors of all power grid partitions as the input of the evaluation algorithm, and the output is the evaluation score of each power grid partition.

7. The energy storage configuration method for a new energy energy storage access system according to claim 1, characterized in that, Based on the evaluation scores, the energy storage configuration of each power grid partition is carried out as follows: Obtain the segmentation threshold through the evaluation scores of all power grid partitions; regard the power grid partitions with evaluation scores less than the segmentation threshold as the energy storage configuration partitions; in each energy storage configuration partition at the moment of maximum photovoltaic output, select the power grid line with the highest line congestion risk degree as the access point of the energy storage system, determine the energy storage system configuration address, and carry out the energy storage configuration.

8. An energy storage configuration device for a new energy storage access system, which implements the method described in claim 1, characterized in that, The device includes: Model construction module: construct a topology model through the connection relationship between components in the new energy power grid and the parameters of each component, and obtain the power flow diagram of the topology model at each moment; Path analysis module: calculate the line overload rate of each power grid line at each moment based on the active power flow of each power grid line and the per-unit value of the line transmission capacity in the power flow diagram at each moment; analyze the power flow path between any two nodes in the power grid through the power flow direction and line overload rate of the power grid line in the power flow diagram at the moment of maximum photovoltaic output; Power grid partition module: record the difference between the voltage value of each node at each moment and the rated voltage as the voltage deviation value; determine the electrical distance between any two nodes based on the similarity between the data changes of the voltage deviation values of different nodes and the degree of chaos of the voltage deviation values of all nodes in the power flow path, and carry out power grid partitioning based on the electrical distance; Partition congestion evaluation module: within each power grid partition, conduct weighted analysis on the change trend of the power flow power of each power injection line through the line capacity of each power injection line of any power grid line, and calculate the line potential congestion probability of each power line at each moment; calculate the line congestion risk degree of each power grid line at each moment according to the line overload rate and line potential congestion probability of each power grid line at each moment, and determine the partition congestion evaluation value of each power grid partition based on the line congestion risk degrees of all power grid lines in each power grid partition; Energy storage configuration module: determine the evaluation score of each power grid partition according to the new energy penetration data and the partition congestion evaluation value of each power grid partition; based on the evaluation score, carry out the energy storage configuration of each power grid partition.

9. Energy storage configuration medium for a new energy storage access system, including a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

Images