A method for identifying weak links in distribution networks with renewable energy access

Abstract

The present application provides a method for identifying weak links in a distribution network with renewable energy access, which relates to data processing technology. The method obtains the power input data of each power input node in the power input node set through a management platform to form a power input data set, then obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set, and then uses a preset distribution network fluctuation model to determine a sensitive monitoring node set according to the power input data set and the power grid operation monitoring data set, and then determines a distribution network weak link set from the distribution network topology structure according to the sensitive monitoring node set, so that the sensitive monitoring nodes in the distribution network can be accurately identified, and then the weak links of the distribution network can be determined, so that the power grid resources can be more reasonably configured.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a method for identifying weak links in a distribution network for access to renewable energy. Background Art

[0002] As the global demand for sustainable energy continues to increase, renewable energy such as solar and wind energy are increasingly being used in power systems. However, the high proportion of renewable energy access to distribution networks has brought many challenges. Due to the intermittent and volatile nature of renewable energy generation, its large-scale access to distribution networks will increase the instability of grid operation, easily causing voltage fluctuations, frequency deviations and other problems, which in turn affects the overall performance of the grid and the reliability of power supply.

[0003] Traditional distribution network analysis methods are often unable to adapt to the complex changes brought about by the access of a high proportion of renewable energy. They lack the ability to accurately model and analyze the fluctuation characteristics of renewable energy, and it is difficult to accurately identify the weak links in the distribution network, thus failing to provide strong guarantees for the safe and stable operation of the power grid. Therefore, it is particularly urgent to develop a distribution network weak link analysis method that can effectively cope with the access of a high proportion of renewable energy. Summary of the invention

[0004] The present application provides a method for identifying weak links in a distribution network with renewable energy access, so as to solve the technical problem of how to identify and locate weak links in the distribution network caused by the intermittent and volatile nature of renewable energy when a high proportion of renewable energy is accessed to the distribution network.

[0005] In a first aspect, the present application provides a method for identifying weak links in a distribution network for renewable energy access, which is applied to a distribution network management system, wherein the distribution network management system includes a management platform and a distribution network, wherein the distribution network includes a distribution network topology structure, a set of electric energy input nodes arranged at the end of the distribution network topology structure, and a set of monitoring nodes arranged in the distribution network topology structure, wherein each electric energy input node in the set of electric energy input nodes is used to access external renewable energy; the method includes:

[0006] The management platform acquires the electric energy input data of each electric energy input node in the electric energy input node set to form an electric energy input data set;

[0007] The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set;

[0008] The management platform uses a preset power distribution network fluctuation model and determines a sensitive monitoring node set according to the power input data set and the power grid operation monitoring data set;

[0009] The management platform determines a set of weak links in the distribution network from the distribution network topology according to the set of sensitive monitoring nodes.

[0010] In the above scheme, the power input data and grid operation monitoring data are obtained and analyzed through the management platform. Combined with the preset distribution network fluctuation model, the sensitive monitoring nodes in the distribution network can be accurately identified, and then the weak links of the distribution network can be determined. These weak links are often the key points for the stable operation of the power grid. Accurate identification of them will help with subsequent maintenance and optimization. By identifying and paying attention to the weak links in the distribution network, corresponding measures can be taken in time to improve and optimize them, thereby reducing the risk of grid fluctuations and failures caused by the access of renewable energy, and enhancing the overall stability and reliability of the distribution network. In addition, by accurately identifying the weak links in the distribution network, grid resources can be more reasonably allocated, such as adding monitoring nodes, strengthening equipment maintenance, etc., to improve the overall operation efficiency and economy of the power grid. In addition, it can also provide strong decision-making support for grid planning, construction and operation and maintenance.

[0011] Optionally, the monitoring nodes in the monitoring node set are arranged on power transmission equipment nodes and / or transmission lines in the distribution network topology structure;

[0012] The distribution network weak links in the distribution network weak link set include power transmission equipment nodes and / or power transmission lines set by sensitive monitoring nodes in the sensitive monitoring node set.

[0013] Optionally, the monitoring node set includes the sensitive monitoring node set, and the preset distribution network fluctuation model is used to determine the correlation between the fluctuation characteristics of the electric energy data in the electric energy input data set and the fluctuation characteristics of the power grid operation monitoring data in the power grid operation monitoring data set.

[0014] Optionally, the distribution network further includes a power output node set, at least some of which are used to access external power-consuming devices; correspondingly, before the management platform acquires the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set, it also includes:

[0015] The management platform obtains the power output data of each power output node in the power output node set to form a power output data set, and determines a characteristic energy proportion according to the power input data set and the power output data set, wherein the characteristic energy proportion is used to characterize the power input ratio of the external renewable energy to the distribution network;

[0016] The management platform determines that the characteristic energy proportion is greater than a preset characteristic energy proportion threshold.

[0017] In the above scheme, by obtaining the power output data of the power output node set, we can have a more comprehensive understanding of the power flow of the distribution network. Combined with the power input data, we can calculate the characteristic energy proportion, that is, the proportion of power input of external renewable energy to the distribution network, so as to determine whether the current distribution network is in a working state with a high proportion of renewable energy access, making subsequent analysis more targeted and accurate.

[0018] Specifically, before analyzing the weak links of the distribution network, first determine whether the characteristic energy ratio is greater than the preset threshold. This step ensures that in-depth weak link analysis is only conducted when the proportion of renewable energy access is high and may have a significant impact on the stable operation of the distribution network, thereby avoiding unnecessary calculations and resource waste, while ensuring the validity of the analysis results. When a high proportion of renewable energy is connected, the operational safety of the power grid faces greater challenges. By identifying the weak links of the distribution network and combining the analysis of the characteristic energy ratio, a strong guarantee is provided for improving the operational safety of the power grid.

[0019] Optionally, determining the characteristic energy proportion according to the electric energy input data set and the electric energy output data set includes:

[0020] The management platform generates an electric energy input power sequence according to the electric energy input power of each time node in the electric energy input data set. ,in, is the number of time nodes in the electric energy input data set, The first The electrical energy input power at each time node;

[0021] The management platform generates an electric energy output power sequence according to the electric energy output power of each time node in the electric energy output data set. ,in, The first The power output at each time node;

[0022] The management platform uses formula 1 and inputs the power sequence according to the electric energy And the electric energy output power sequence is confirmed Determine the characteristic energy ratio, the formula 1 is:

[0023] ,

[0024] in, is the proportion of the characteristic energy.

[0025] In the above scheme, the power input data and the power output data are arranged according to the time nodes to form the power input power sequence and the power output power sequence, so as to more accurately reflect the relationship between the input of renewable energy and the power output of the system at each time point. Then, the characteristic energy proportion is calculated using Formula 1, which can reflect the proportion of renewable energy in the total power input by solving the maximum value of the ratio of input power to output power at each time point.

[0026] Optionally, the management platform uses a preset distribution network fluctuation model and determines a sensitive monitoring node set according to the electric energy input data set and the power grid operation monitoring data set, including:

[0027] The management platform uses formula 2 and inputs the power sequence according to the electric energy Determine the characteristic value of power input fluctuation , the formula 2 is:

[0028] ,

[0029] The management platform is based on the power input power fluctuation characteristic value In the electric energy input power sequence The target time range corresponding to the grid operation monitoring data set is used to determine the grid current fluctuation amplitude set ,in, is the number of monitoring nodes in the monitoring node set, is the first node in the monitoring node set The absolute value of the difference between the maximum current and the minimum current of each monitoring node within the target time range;

[0030] The management platform is based on The electric energy output power sequence determines the electric energy output power fluctuation characteristic value corresponding to the target time range;

[0031] If the management platform determines that the power output power fluctuation characteristic value is less than the preset power output power fluctuation threshold, and the The monitoring nodes correspond to If the current fluctuation amplitude is greater than the preset threshold, the A monitoring node is determined as a sensitive monitoring node in the sensitive monitoring node set.

[0032] In the above scheme, the electric energy input power fluctuation characteristic value is calculated by formula 2, which takes into account the ratio of the change rate of input power between adjacent time nodes to the output power at the corresponding time points. It can reflect the fluctuation characteristics of renewable energy input power and its impact on the power grid, and thus helps to capture potential fluctuation risks and provide a basis for subsequent analysis.

[0033] In addition, by using the grid operation monitoring data set to determine the grid current fluctuation amplitude set, the current fluctuation of each monitoring node within the target time range can also be dynamically monitored, which helps to grasp the operation status of the grid under the fluctuation of renewable energy input in real time. On the basis of determining the characteristic value of electric energy input power fluctuation and the grid current fluctuation amplitude, by comparing the relationship between the characteristic value of electric energy output power fluctuation and the preset threshold and the grid current fluctuation amplitude and the preset threshold, a set of sensitive monitoring nodes is screened out, so that the areas in the grid that are greatly affected by the fluctuation of renewable energy input can be accurately identified. Furthermore, by timely identifying and locating the weak links in the distribution network, that is, the transmission equipment nodes and / or transmission lines represented by the sensitive monitoring nodes, targeted optimization measures can be taken, such as strengthening monitoring, adjusting operation strategies or upgrading equipment, etc., thereby effectively improving the safety and stability of the power grid.

[0034] Optionally, the management platform is based on the power input power fluctuation characteristic value In the electric energy input power sequence The target time range corresponding to the grid operation monitoring data set is used to determine the grid current fluctuation amplitude set Previously, it also included:

[0035] The management platform is based on the power input power fluctuation characteristic value Determine the electrical energy input power sequence The corresponding characteristic time range is determined, and the target time range is determined according to the characteristic time range, and the target time range is the time range determined by adding a preset time length to the characteristic time range.

[0036] In the above scheme, the characteristic time range corresponding to the electric energy input power sequence is determined according to the characteristic value of the electric energy input power fluctuation, and the target time range is further determined on this basis (i.e., the preset time length is added to the characteristic time range), so that the key time period of the electric energy input fluctuation can be captured more accurately, and then the correlation between the power grid operation data and the fluctuation characteristics of this period can be more accurately evaluated in the subsequent analysis.

[0037] Among them, the input power of renewable energy sources such as solar energy and wind energy often fluctuates, and these fluctuations may have a delayed impact on the power grid. By adding a preset time length to the characteristic time range to determine the target time range, this fluctuation delay characteristic is taken into account, making the analysis process more in line with the actual power grid operation and improving the practicality and accuracy of the analysis.

[0038] It is worth noting that directly using the time range corresponding to the characteristic value of power input power fluctuation as the basis for analysis may lead to false alarms or omissions of sensitive monitoring nodes due to failure to fully consider the fluctuation delay. By determining the target time range, the possible fluctuation impact period can be more comprehensively covered, reducing false alarms and omissions caused by fluctuation delays.

[0039] After accurately determining the target time range, the operating status and weak links of the power grid during that period can be more accurately evaluated, thus providing a stronger basis for optimizing resource allocation. For example, the charging and discharging strategies of energy storage equipment and the operating parameters of transmission equipment can be adjusted according to the analysis results to improve the flexibility and stability of the power grid.

[0040] Optionally, the management platform determines a set of weak links in the distribution network from the distribution network topology structure according to the set of sensitive monitoring nodes, including:

[0041] The management platform determines an adjacent power grid characteristic node according to a target sensitive monitoring node in the sensitive monitoring node set, wherein the power grid characteristic node is a power transmission device node and / or a power input node adjacent to the target sensitive monitoring node;

[0042] The management platform determines a target transmission line set according to the grid characteristic node, wherein the target transmission lines in the target transmission line set are used to connect the grid characteristic node with other nodes in the distribution network topology structure;

[0043] The management platform generates a distribution network weak link in the distribution network weak link set according to the target sensitive monitoring node, the power grid characteristic node and the target transmission line set.

[0044] In the above scheme, by taking the sensitive monitoring node as the starting point, the adjacent grid characteristic nodes (including transmission equipment nodes and / or power input nodes) are determined, so that the weak links of the distribution network can be located more accurately. After determining the grid characteristic nodes, the target transmission line set connecting these characteristic nodes is further determined. This step takes into account the connection relationship and mutual influence between the transmission equipment. By comprehensively analyzing the multiple factors of the target sensitive monitoring nodes, grid characteristic nodes and target transmission line set, the weak links of the distribution network can be evaluated more comprehensively to avoid missing important information.

[0045] Compared with the traditional method of calculating the relevant scores of each node to determine the weak links of the distribution network, the above scheme directly associates the sensitive monitoring nodes with the grid characteristic nodes, reducing unnecessary intermediate steps and calculations, thereby improving the analysis efficiency. This is of great significance for quickly responding to grid changes and adjusting operation strategies in a timely manner.

[0046] Optionally, the distribution network topology structure is further provided with an energy storage device node set, and the energy storage device nodes in the energy storage device node set are connected to the power input nodes in the power input node set; correspondingly, after the management platform determines the distribution network weak link set from the distribution network topology structure according to the sensitive monitoring node set, it also includes:

[0047] The management platform determines the corresponding target energy storage device node according to the target power input node in the weak link set of the distribution network, and the target power input node is used to access the photovoltaic device;

[0048] The management platform obtains weather forecast data for the location of the target power input node, the weather forecast data includes a light intensity prediction curve, and determines a maximum rate of change of light intensity within a future preset time range according to the light intensity prediction curve, the maximum rate of change being the maximum slope of the light intensity prediction curve in the future preset time range;

[0049] If the management platform determines that the maximum change rate is greater than a preset change rate threshold, the target power input node is controlled to only supply power to the target energy storage device node within the future preset time range.

[0050] In the above scheme, by setting up energy storage device nodes on the distribution network topology and connecting them to power input nodes (especially nodes connected to photovoltaic devices), energy storage devices can be used to balance the volatility and intermittency of renewable energy when weak links appear in the power grid. When the management platform predicts that the light intensity will change significantly within a preset time range in the future (that is, the maximum change rate is greater than the preset threshold), the target power input node is controlled to supply power only to the energy storage device node, effectively avoiding the voltage fluctuation and frequency instability of the power grid caused by sudden changes in light intensity, thereby enhancing the overall stability of the power grid.

[0051] In addition, the use of energy storage equipment to store excess electricity when the light intensity changes greatly and release electricity when the light is insufficient not only ensures the maximum utilization of renewable energy, but also reduces the energy waste caused by the abandonment of light and wind. Then, through the real-time analysis and processing of the light intensity prediction data, the management platform can respond in advance and adjust the operation strategy of the power grid. When it is predicted that the light intensity will change significantly, the power supply direction of the target power input node is controlled in time, avoiding the chain reaction and failure expansion caused by the weak links of the power grid, and improving the response ability and reliability of the power grid. It can be seen that in the above scheme, the energy storage equipment is combined with the intelligent management of the distribution network to realize the refined scheduling of renewable energy access to the power grid. According to the real-time data and prediction results, the management platform automatically adjusts the charging and discharging strategy of the energy storage equipment and the operation mode of the power grid, realizing the intelligent scheduling and autonomous optimization of the power grid.

[0052] In a second aspect, the present application provides a distribution network management system, comprising: a management platform and a distribution network, wherein the distribution network comprises a distribution network topology structure, a set of electric energy input nodes arranged at the end of the distribution network topology structure, and a set of monitoring nodes arranged in the distribution network topology structure, wherein each electric energy input node in the set of electric energy input nodes is used to access external renewable energy;

[0053] The management platform acquires the electric energy input data of each electric energy input node in the electric energy input node set to form an electric energy input data set;

[0054] The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set;

[0055] The management platform uses a preset power distribution network fluctuation model and determines a sensitive monitoring node set according to the power input data set and the power grid operation monitoring data set;

[0056] The management platform determines a set of weak links in the distribution network from the distribution network topology according to the set of sensitive monitoring nodes.

[0057] Optionally, the monitoring nodes in the monitoring node set are arranged on power transmission equipment nodes and / or transmission lines in the distribution network topology structure;

[0058] The distribution network weak links in the distribution network weak link set include power transmission equipment nodes and / or power transmission lines set by sensitive monitoring nodes in the sensitive monitoring node set.

[0059] Optionally, the monitoring node set includes the sensitive monitoring node set, and the preset distribution network fluctuation model is used to determine the correlation between the fluctuation characteristics of the electric energy data in the electric energy input data set and the fluctuation characteristics of the power grid operation monitoring data in the power grid operation monitoring data set.

[0060] Optionally, the power distribution network further includes a set of power output nodes, at least some of which are used to access external power-consuming devices;

[0061] The management platform obtains the power output data of each power output node in the power output node set to form a power output data set, and determines a characteristic energy proportion according to the power input data set and the power output data set, wherein the characteristic energy proportion is used to characterize the power input ratio of the external renewable energy to the distribution network;

[0062] The management platform determines that the characteristic energy proportion is greater than a preset characteristic energy proportion threshold.

[0063] Optionally, the management platform generates an electric energy input power sequence according to the electric energy input power at each time node in the electric energy input data set. ,in, is the number of time nodes in the electric energy input data set, The first The electrical energy input power at each time node;

[0064] The management platform generates an electric energy output power sequence according to the electric energy output power of each time node in the electric energy output data set. ,in, The first The power output at each time node;

[0065] The management platform uses formula 1 and inputs the power sequence according to the electric energy And the electric energy output power sequence To determine the characteristic energy proportion, the formula 1 is:

[0066] ,

[0067] in, is the proportion of the characteristic energy.

[0068] Optionally, the management platform uses formula 2 and inputs power sequence according to the electric energy Determine the characteristic value of power input fluctuation , the formula 2 is:

[0069] ,

[0070] The management platform is based on the power input power fluctuation characteristic value In the electric energy input power sequence The target time range corresponding to the grid operation monitoring data set is used to determine the grid current fluctuation amplitude set ,in, is the number of monitoring nodes in the monitoring node set, is the first node in the monitoring node set The absolute value of the difference between the maximum current and the minimum current of each monitoring node within the target time range;

[0071] The management platform outputs power according to the electric energy sequence Determining a power output fluctuation characteristic value corresponding to the target time range;

[0072] If the management platform determines that the power output power fluctuation characteristic value is less than the preset power output power fluctuation threshold, and the The monitoring nodes correspond to If the current fluctuation amplitude is greater than the preset threshold, the A monitoring node is determined as a sensitive monitoring node in the sensitive monitoring node set.

[0073] Optionally, the management platform is configured to generate a power fluctuation characteristic value according to the power input power fluctuation characteristic value. Determine the electrical energy input power sequence The corresponding characteristic time range is determined, and the target time range is determined according to the characteristic time range, and the target time range is the time range determined by adding a preset time length to the characteristic time range.

[0074] Optionally, the management platform determines an adjacent power grid characteristic node according to a target sensitive monitoring node in the sensitive monitoring node set, wherein the power grid characteristic node is a power transmission device node and / or a power input node adjacent to the target sensitive monitoring node;

[0075] The management platform determines a target transmission line set according to the grid characteristic node, wherein the target transmission lines in the target transmission line set are used to connect the grid characteristic node with other nodes in the distribution network topology structure;

[0076] The management platform generates a distribution network weak link in the distribution network weak link set according to the power grid characteristic node and the target transmission line set.

[0077] Optionally, an energy storage device node set is further provided on the distribution network topology structure, and the energy storage device nodes in the energy storage device node set are connected to the power input nodes in the power input node set;

[0078] The management platform determines the corresponding target energy storage device node according to the target power input node in the weak link set of the distribution network, and the target power input node is used to access the photovoltaic device;

[0079] The management platform obtains weather forecast data for the location of the target power input node, the weather forecast data includes a light intensity prediction curve, and determines a maximum rate of change of light intensity within a future preset time range according to the light intensity prediction curve, the maximum rate of change being the maximum slope of the light intensity prediction curve in the future preset time range;

[0080] If the management platform determines that the maximum change rate is greater than a preset change rate threshold, the target power input node is controlled to only supply power to the target energy storage device node within the future preset time range.

[0081] In a third aspect, the present application provides an electronic device, including:

[0082] A processor; and a memory for storing executable instructions of the processor;

[0083] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0084] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0085] The method for identifying weak links in a distribution network with renewable energy access provided in the present application obtains electric energy input data of each electric energy input node in a set of electric energy input nodes through a management platform to form an electric energy input data set, then obtains power grid operation monitoring data of each monitoring node in a set of monitoring nodes to form a set of power grid operation monitoring data, and then uses a preset distribution network fluctuation model to determine a set of sensitive monitoring nodes according to the set of electric energy input data and the set of power grid operation monitoring data, and then determines a set of weak links in the distribution network from the topological structure of the distribution network according to the set of sensitive monitoring nodes, thereby being able to accurately identify sensitive monitoring nodes in the distribution network, and then determine the weak links in the distribution network, so that power grid resources can be more reasonably allocated. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0087] Figure 1 It is a flowchart of a method for identifying weak links in a distribution network for renewable energy access according to an exemplary embodiment of the present application;

[0088] Figure 2 It is a flowchart of a method for identifying weak links in a distribution network for renewable energy access according to another exemplary embodiment of the present application;

[0089] Figure 3 is a structural schematic diagram of a power distribution network management system according to an exemplary embodiment of the present application;

[0090] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0091] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0092] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0093] With the widespread application of renewable energy (such as solar energy, wind energy, etc.), the proportion of renewable energy access in distribution networks is increasing. However, the volatility and intermittency of renewable energy have brought great challenges to the stable operation of distribution networks. Traditional distribution network analysis methods are often unable to effectively deal with the complex problems brought about by the high proportion of renewable energy access, such as grid fluctuations and increased failure risks. Therefore, there is an urgent need for a method that can accurately identify the weak links of the distribution network and optimize and improve them accordingly to improve the overall stability and reliability of the distribution network.

[0094] For distribution networks with a high proportion of renewable energy access, the existing technologies have the following main problems:

[0095] It is impossible to accurately identify sensitive monitoring nodes in the distribution network, making it difficult to determine the weak links in the distribution network.

[0096] The lack of effective analysis and prediction of the fluctuating characteristics of renewable energy input has led to delayed adjustments in grid operation strategies.

[0097] The embodiment provided in this application proposes a method for identifying weak links in a distribution network for renewable energy access, which is implemented by the following steps:

[0098] Data acquisition: The management platform acquires the power input data from the power input node set to form a power input data set; at the same time, it acquires the power grid operation monitoring data from the monitoring node set to form a power grid operation monitoring data set.

[0099] Feature analysis: Using the preset distribution network fluctuation model, combined with the power input data set and the power grid operation monitoring data set, the sensitive monitoring node set is determined. The model can analyze the correlation between the power data fluctuation characteristics and the power grid operation monitoring data fluctuation characteristics, so as to accurately identify the sensitive monitoring nodes that are greatly affected by the fluctuation of renewable energy input.

[0100] Weak link determination: Based on the set of sensitive monitoring nodes, the set of weak links in the distribution network is determined from the distribution network topology. These weak links include the transmission equipment nodes and / or transmission lines set by the sensitive monitoring nodes, which are the key points for the stable operation of the power grid.

[0101] Optimization measures: Take corresponding optimization measures for the identified weak links in the distribution network, such as adding monitoring nodes, strengthening equipment maintenance, adjusting operation strategies or upgrading equipment, so as to improve the security and stability of the power grid.

[0102] In addition, the embodiments provided in this application further propose the following optional solutions:

[0103] Before analysis, determine whether the characteristic energy ratio (i.e., the proportion of electric energy input from external renewable energy to the distribution network) is greater than the preset threshold to ensure the pertinence and accuracy of the analysis.

[0104] The power input power fluctuation characteristic value and grid current fluctuation amplitude are calculated to more accurately reflect the impact of renewable energy input fluctuations on the grid.

[0105] Determine the target time range to more accurately capture the critical period of power input fluctuations and assess the operating status and weak links of the power grid during this period.

[0106] Energy storage device nodes are set up on the distribution network topology and connected to the power input nodes. Energy storage devices are used to balance the volatility and intermittency of renewable energy and improve the overall stability of the power grid.

[0107] The embodiments provided in this application have the following technical effects:

[0108] Accurate identification: By acquiring and analyzing power input data and grid operation monitoring data through the management platform, combined with the preset distribution network fluctuation model, sensitive monitoring nodes and weak links in the distribution network can be accurately identified.

[0109] Optimize resource allocation: Based on the identified weak links, grid resources can be more reasonably allocated, such as adding monitoring nodes, strengthening equipment maintenance, etc., to improve the overall operating efficiency and economy of the grid.

[0110] Improve stability: By timely identifying and locating weak links in the distribution network and taking corresponding optimization measures, the risk of grid fluctuations and failures caused by renewable energy access can be effectively reduced, and the overall stability and reliability of the distribution network can be enhanced.

[0111] Smart dispatch: Combining energy storage equipment with the intelligent management of distribution networks enables refined dispatch of renewable energy access to the grid, improving the grid’s response capability and reliability.

[0112] In summary, the embodiment provided in this application proposes a method for identifying weak links in a distribution network with renewable energy access. This method effectively improves the overall stability and reliability of the distribution network by accurately identifying sensitive monitoring nodes and weak links in the distribution network, and optimizes and improves them accordingly, thereby providing strong decision-making support for grid planning, construction, and operation and maintenance.

[0113] Figure 1 FIG. 1 is a flow chart of a method for identifying weak links in a distribution network for renewable energy access according to an exemplary embodiment of the present application. Figure 1 As shown, the method provided in this embodiment includes:

[0114] S101. A management platform obtains power input data of each power input node in a power input node set to form a power input data set.

[0115] The method provided in this embodiment can be applied to a distribution network management system, which includes: a management platform and a distribution network, the distribution network including a distribution network topology, a set of power input nodes arranged at the end of the distribution network topology, and a set of monitoring nodes arranged in the distribution network topology, wherein each power input node in the above-mentioned power input node set is used to access external renewable energy. Specifically, the above-mentioned distribution network management system is mainly composed of two parts: a management platform and a distribution network. As the center of the system, the management platform is responsible for functions such as data processing, analysis, decision-making and control; the distribution network includes specific physical structures and equipment, and is the basis for realizing power transmission and distribution.

[0116] It is worth noting that the above-mentioned distribution network topology refers to the connection mode and layout between various devices in the distribution network (such as transformers, lines, switches, etc.). In actual implementation, it is necessary to first design and build the topology of the distribution network to ensure that the connection between various devices is reasonable and reliable. The design of the topology should take into account the demand for future renewable energy access and reserve sufficient interfaces and capacity.

[0117] The power input node set refers to the set of equipment set at the end of the distribution network topology for accessing external renewable energy. These nodes include but are not limited to renewable energy generation equipment such as photovoltaic panels and wind turbines. In specific implementation, these input nodes need to be reasonably arranged according to the local renewable energy resources. Each input node should be equipped with corresponding data acquisition and communication equipment to send power input data to the management platform in real time.

[0118] The monitoring node set is set on the transmission equipment nodes and / or transmission lines in the distribution network topology to monitor the operation status of the power grid in real time. These nodes collect key parameters such as current, voltage, power factor, etc. of the power grid in real time by installing current transformers, voltage transformers and other equipment.

[0119] In this step, the management platform obtains the power input data of each power input node in the power input node set to form a power input data set. Specifically, the management platform collects power input data from each power input node (such as photovoltaic panels, wind turbines and other access points) regularly or in real time. These data may include parameters such as input power, voltage, and current. The management platform interacts with the power input nodes through communication protocols (such as Modbus, IEC 61850, etc.), stores the collected data in the database, and forms a power input data set, which will be used for subsequent analysis of the impact of renewable energy input on the distribution network.

[0120] S102: The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set.

[0121] In this step, the management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set. The monitoring nodes in the monitoring node set are set on the transmission equipment nodes and / or transmission lines in the distribution network topology.

[0122] Specifically, the management platform collects grid operation monitoring data in real time or regularly through monitoring nodes (such as current transformers, voltage transformers, etc.) distributed in the distribution network topology. These monitoring nodes are set on transmission equipment nodes and / or transmission lines, and can monitor key parameters such as current, voltage, and power factor. The management platform organizes the collected data into a grid operation monitoring data set for analyzing the actual operation status of the grid.

[0123] S103: The management platform uses a preset distribution network fluctuation model and determines a sensitive monitoring node set according to a power input data set and a power grid operation monitoring data set.

[0124] In this step, the management platform uses a preset distribution network fluctuation model and determines a sensitive monitoring node set based on the electric energy input data set and the power grid operation monitoring data set. The monitoring node set includes a sensitive monitoring node set. The preset distribution network fluctuation model is used to determine the correlation between the fluctuation characteristics of the electric energy data in the electric energy input data set and the fluctuation characteristics of the power grid operation monitoring data in the power grid operation monitoring data set.

[0125] Specifically, the management platform first analyzes the fluctuation characteristics of power data based on the power input data set using a preset distribution network fluctuation model (which may be established based on statistical analysis, machine learning, or physical simulation). Then, combined with the power grid operation monitoring data set, the management platform compares the correlation between the fluctuation characteristics of power data and the fluctuation characteristics of power grid operation monitoring data. Through calculation and analysis, it is determined which monitoring nodes have a significant correlation with the fluctuation of power input data. These nodes are sensitive monitoring nodes and are classified into the sensitive monitoring node set.

[0126] S104. The management platform determines a set of weak links in the distribution network from the distribution network topology structure according to the set of sensitive monitoring nodes.

[0127] In this step, the management platform determines a distribution network weak link set from the distribution network topology structure according to the sensitive monitoring node set, and the distribution network weak links in the distribution network weak link set include transmission equipment nodes and / or transmission lines set by the sensitive monitoring nodes in the sensitive monitoring node set.

[0128] Specifically, the management platform searches for the transmission equipment nodes and / or transmission lines connected to these nodes in the distribution network topology according to the node positions in the sensitive monitoring node set. These equipment nodes and / or transmission lines constitute a set of weak links in the distribution network. The management platform may also need to further analyze the electrical characteristics, load conditions and other factors of these weak links to determine the specific weak links and their potential risks. In addition, the management platform can also interact with energy storage systems, demand response systems, etc., and reduce the impact of renewable energy access on the distribution network by optimizing control strategies, thereby further improving the stability and reliability of the power grid.

[0129] In this embodiment, the power input data of each power input node in the power input node set is obtained through the management platform to form a power input data set, and then the power grid operation monitoring data of each monitoring node in the monitoring node set is obtained to form a power grid operation monitoring data set, and then the preset distribution network fluctuation model is used, and the sensitive monitoring node set is determined according to the power input data set and the power grid operation monitoring data set, and then the distribution network weak link set is determined from the distribution network topology structure according to the sensitive monitoring node set, so that the sensitive monitoring nodes in the distribution network can be accurately identified, and then the weak links of the distribution network can be determined, so that the power grid resources can be more reasonably configured.

[0130] Figure 2 FIG. 1 is a flow chart of a method for identifying weak links in a distribution network for renewable energy access according to another exemplary embodiment of the present application. Figure 2 As shown,

[0131] S201. The management platform obtains power input data of each power input node in a power input node set to form a power input data set.

[0132] In this step, the management platform obtains the power input data of each power input node in the power input node set to form a power input data set. Specifically, the management platform collects power input data from each power input node (such as photovoltaic panels, wind turbines and other access points) regularly or in real time. These data may include parameters such as input power, voltage, and current. The management platform interacts with the power input nodes through communication protocols (such as Modbus, IEC 61850, etc.), stores the collected data in the database, and forms a power input data set, which will be used for subsequent analysis of the impact of renewable energy input on the distribution network.

[0133] S202: The management platform obtains power output data of each power output node in the power output node set to form a power output data set, and determines a characteristic energy proportion according to the power input data set and the power output data set.

[0134] In this step, the distribution network may also include a set of power output nodes, at least some of which are used to access external power-consuming devices. The management platform first communicates with the set of power output nodes in the distribution network to collect power output data of these nodes within a certain period of time. These data include key parameters such as power output power and output current of each power output node at each time point.

[0135] In this step, the management platform obtains the power output data of each power output node in the power output node set to form a power output data set, and determines the characteristic energy proportion based on the power input data set and the power output data set. The characteristic energy proportion is used to characterize the proportion of power input of external renewable energy to the distribution network.

[0136] Specifically, the management platform extracts the power input power of each time node from the power input data set and arranges it in chronological order to form a power input power sequence. Similarly, the management platform extracts the power output power of each time node from the power output data set to form a power output power sequence. Then, the management platform calculates the characteristic energy proportion, that is, the proportion of power input from external renewable energy to the distribution network.

[0137] In a possible design, the management platform generates an electric energy input power sequence according to the electric energy input power at each time node in the electric energy input data set. ,in, is the number of time nodes in the electric energy input data set, The first The electrical energy input power at each time node;

[0138] The management platform generates an electric energy output power sequence based on the electric energy output power of each time node in the electric energy output data set. ,in, The first The power output at each time node;

[0139] The management platform uses formula 1 and inputs the power sequence according to the power And the power output sequence Determine the characteristic energy ratio, formula 1 is:

[0140] ,

[0141] in, is the characteristic energy proportion.

[0142] It is worth noting that the management platform first obtains the power input data of each time node from the power input node set of the distribution network to form a power input data set. Then, the management platform generates a power input power sequence based on the power input power of each time node in these data sets. The management platform then obtains the power output data of each time node from the power output node set to form a power output data set.

[0143] Then, the management platform uses the above formula 1 to calculate the characteristic energy ratio according to the electric energy input power sequence and the electric energy output power sequence. Thus, by comparing the ratio of electric energy input power to electric energy output power at each time point, and taking the maximum value as the characteristic energy ratio, the accuracy of the calculation result is ensured. This method can fully reflect the input of renewable energy in different time periods and its impact on the distribution network. In addition, the calculation results of the characteristic energy ratio provide strong support for the grid operation decision-making. When the characteristic energy ratio exceeds the preset threshold, it indicates that the proportion of renewable energy access is high, which may have a significant impact on the stable operation of the grid. At this time, the management platform can further analyze the weak links of the distribution network and provide data support for subsequent maintenance and optimization. In addition, after determining the specific proportion of renewable energy in the distribution network, it is helpful to allocate grid resources more reasonably. For example, according to the high or low proportion of characteristic energy, the management platform can adjust the charging and discharging strategy of energy storage equipment, optimize the operating parameters of transmission equipment, etc., to improve the flexibility and stability of the grid.

[0144] S203: The management platform determines that the characteristic energy ratio is greater than a preset characteristic energy ratio threshold.

[0145] Specifically, the management platform compares the calculated characteristic energy ratio with the preset characteristic energy ratio threshold. The preset threshold is determined based on factors such as the design capacity of the distribution network, historical operation data, and renewable energy access planning. If the characteristic energy ratio is greater than the preset threshold, it indicates that the current distribution network is in a state of high proportion of renewable energy access, and in-depth weak link analysis is required; otherwise, it can be considered that the current grid operation state is relatively stable and no further analysis is required.

[0146] If the characteristic energy ratio is greater than the preset threshold, the management platform will continue to execute the subsequent steps, that is, to obtain the grid operation monitoring data of the monitoring node to conduct a detailed analysis of the weak links of the distribution network. If the characteristic energy ratio does not exceed the preset threshold, the analysis process ends, and the management platform can record the current status and wait for the next analysis trigger condition.

[0147] S204: The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set.

[0148] In this step, the management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set. The monitoring nodes in the monitoring node set are set on the transmission equipment nodes and / or transmission lines in the distribution network topology.

[0149] Specifically, the management platform collects grid operation monitoring data in real time or regularly through monitoring nodes (such as current transformers, voltage transformers, etc.) distributed in the distribution network topology. These monitoring nodes are set on transmission equipment nodes and / or transmission lines, and can monitor key parameters such as current, voltage, and power factor. The management platform organizes the collected data into a grid operation monitoring data set for analyzing the actual operation status of the grid.

[0150] S205. The management platform uses a preset distribution network fluctuation model and determines a sensitive monitoring node set according to a power input data set and a power grid operation monitoring data set.

[0151] In this step, the management platform uses a preset distribution network fluctuation model and determines a sensitive monitoring node set based on the electric energy input data set and the power grid operation monitoring data set. The monitoring node set includes a sensitive monitoring node set. The preset distribution network fluctuation model is used to determine the correlation between the fluctuation characteristics of the electric energy data in the electric energy input data set and the fluctuation characteristics of the power grid operation monitoring data in the power grid operation monitoring data set.

[0152] Specifically, the management platform first analyzes the fluctuation characteristics of power data based on the power input data set using a preset distribution network fluctuation model (which may be established based on statistical analysis, machine learning, or physical simulation). Then, combined with the power grid operation monitoring data set, the management platform compares the correlation between the fluctuation characteristics of power data and the fluctuation characteristics of power grid operation monitoring data. Through calculation and analysis, it is determined which monitoring nodes have a significant correlation with the fluctuation of power input data. These nodes are sensitive monitoring nodes and are classified into the sensitive monitoring node set.

[0153] In one possible design, the management platform uses Formula 2 and inputs the power sequence according to the power Determine the characteristic value of power input fluctuation , Formula 2 is:

[0154] ,

[0155] The management platform is based on the power input power fluctuation characteristic value In the power input sequence The corresponding target time range and the power grid operation monitoring data set determine the power grid current fluctuation amplitude set ,in, is the number of monitoring nodes in the monitoring node set, is the first node in the monitoring node set The absolute value of the difference between the maximum current and the minimum current of each monitoring node within the target time range;

[0156] The management platform outputs power sequence according to the power Determine the power output fluctuation characteristic value corresponding to the target time range;

[0157] If the management platform determines that the power output power fluctuation characteristic value is less than the preset power output power fluctuation threshold, and the The monitoring nodes correspond to If the current fluctuation amplitude is greater than the preset threshold, the The monitoring nodes are determined as sensitive monitoring nodes in the sensitive monitoring node set.

[0158] In the above scheme, the electric energy input power fluctuation characteristic value is calculated by formula 2, which takes into account the ratio of the change rate of input power between adjacent time nodes to the output power at the corresponding time points. It can reflect the fluctuation characteristics of renewable energy input power and its impact on the power grid, and thus helps to capture potential fluctuation risks and provide a basis for subsequent analysis.

[0159] In addition, by using the grid operation monitoring data set to determine the grid current fluctuation amplitude set, the current fluctuation of each monitoring node within the target time range can also be dynamically monitored, which helps to grasp the operation status of the grid under the fluctuation of renewable energy input in real time. On the basis of determining the characteristic value of electric energy input power fluctuation and the grid current fluctuation amplitude, by comparing the relationship between the characteristic value of electric energy output power fluctuation and the preset threshold and the grid current fluctuation amplitude and the preset threshold, a set of sensitive monitoring nodes is screened out, so that the areas in the grid that are greatly affected by the fluctuation of renewable energy input can be accurately identified. Furthermore, by timely identifying and locating the weak links in the distribution network, that is, the transmission equipment nodes and / or transmission lines represented by the sensitive monitoring nodes, targeted optimization measures can be taken, such as strengthening monitoring, adjusting operation strategies or upgrading equipment, etc., thereby effectively improving the safety and stability of the power grid.

[0160] Furthermore, the management platform can be used to calculate the power input power fluctuation characteristic value. In the power input sequence The corresponding target time range and the power grid operation monitoring data set determine the power grid current fluctuation amplitude set Before, you can also include:

[0161] The management platform is based on the power input power fluctuation characteristic value Determine the power sequence of electrical energy input The corresponding characteristic time range is determined, and the target time range is determined based on the characteristic time range. The target time range is the time range determined by adding a preset time length to the characteristic time range.

[0162] In the above scheme, the characteristic time range corresponding to the electric energy input power sequence is determined according to the characteristic value of the electric energy input power fluctuation, and the target time range is further determined on this basis (i.e., the preset time length is added to the characteristic time range), so that the key time period of the electric energy input fluctuation can be captured more accurately, and then the correlation between the power grid operation data and the fluctuation characteristics of this period can be more accurately evaluated in the subsequent analysis.

[0163] Among them, the input power of renewable energy sources such as solar energy and wind energy often fluctuates, and these fluctuations may have a delayed impact on the power grid. By adding a preset time length to the characteristic time range to determine the target time range, this fluctuation delay characteristic is taken into account, making the analysis process more in line with the actual power grid operation and improving the practicality and accuracy of the analysis.

[0164] It is worth noting that directly using the time range corresponding to the characteristic value of power input power fluctuation as the basis for analysis may lead to false alarms or omissions of sensitive monitoring nodes due to failure to fully consider the fluctuation delay. By determining the target time range, the possible fluctuation impact period can be more comprehensively covered, reducing false alarms and omissions caused by fluctuation delays.

[0165] After accurately determining the target time range, the operating status and weak links of the power grid during that period can be more accurately evaluated, thus providing a stronger basis for optimizing resource allocation. For example, the charging and discharging strategies of energy storage equipment and the operating parameters of transmission equipment can be adjusted according to the analysis results to improve the flexibility and stability of the power grid.

[0166] S206. The management platform determines a set of weak links in the distribution network from the distribution network topology structure according to the set of sensitive monitoring nodes.

[0167] In this step, the management platform determines a distribution network weak link set from the distribution network topology structure according to the sensitive monitoring node set, and the distribution network weak links in the distribution network weak link set include transmission equipment nodes and / or transmission lines set by the sensitive monitoring nodes in the sensitive monitoring node set.

[0168] Specifically, the management platform searches for the transmission equipment nodes and / or transmission lines connected to these nodes in the distribution network topology according to the node positions in the sensitive monitoring node set. These equipment nodes and / or transmission lines constitute a set of weak links in the distribution network. The management platform may also need to further analyze the electrical characteristics, load conditions and other factors of these weak links to determine the specific weak links and their potential risks. In addition, the management platform can also interact with energy storage systems, demand response systems, etc., and reduce the impact of renewable energy access on the distribution network by optimizing control strategies, thereby further improving the stability and reliability of the power grid.

[0169] In a possible design, the management platform determines an adjacent power grid characteristic node according to a target sensitive monitoring node in the sensitive monitoring node set, where the power grid characteristic node is a power transmission device node and / or a power input node adjacent to the target sensitive monitoring node;

[0170] The management platform determines a target transmission line set according to the characteristic nodes of the power grid, and the target transmission lines in the target transmission line set are used to connect the characteristic nodes of the power grid with other nodes in the topological structure of the distribution network;

[0171] The management platform generates a distribution network weak link in the distribution network weak link set according to the target sensitive monitoring nodes, the power grid characteristic nodes and the target transmission line set.

[0172] In the above scheme, by taking the sensitive monitoring node as the starting point, the adjacent grid characteristic nodes (including transmission equipment nodes and / or power input nodes) are determined, so that the weak links of the distribution network can be located more accurately. After determining the grid characteristic nodes, the target transmission line set connecting these characteristic nodes is further determined. This step takes into account the connection relationship and mutual influence between the transmission equipment. By comprehensively analyzing the multiple factors of the target sensitive monitoring nodes, grid characteristic nodes and target transmission line set, the weak links of the distribution network can be evaluated more comprehensively to avoid missing important information.

[0173] Compared with the traditional method of calculating the relevant scores of each node to determine the weak links of the distribution network, the above scheme directly associates the sensitive monitoring nodes with the grid characteristic nodes, reducing unnecessary intermediate steps and calculations, thereby improving the analysis efficiency. This is of great significance for quickly responding to grid changes and adjusting operation strategies in a timely manner.

[0174] On the basis of the above scheme, a set of energy storage device nodes is further provided on the distribution network topology structure, and the energy storage device nodes in the energy storage device node set are connected to the power input nodes in the power input node set; correspondingly, after the management platform determines the set of weak links of the distribution network from the distribution network topology structure according to the sensitive monitoring node set, it also includes:

[0175] The management platform determines the corresponding target energy storage device node according to the target power input node in the weak link set of the distribution network. The target power input node is used to access the photovoltaic equipment.

[0176] The management platform obtains weather forecast data of the location of the target power input node, the weather forecast data includes a light intensity forecast curve, and determines the maximum rate of change of light intensity within a future preset time range according to the light intensity forecast curve, the maximum rate of change being the maximum slope of the light intensity forecast curve in the future preset time range;

[0177] If the management platform determines that the maximum change rate is greater than a preset change rate threshold, the target power input node is controlled to only supply power to the target energy storage device node within a preset time range in the future.

[0178] Specifically, the above scheme further introduces a collection of energy storage device nodes to optimize the stability and flexibility of the distribution network in the scenario of high proportion of renewable energy access. The addition of energy storage devices is intended to smooth the volatility of renewable energy (such as photovoltaic) output, especially when weather conditions change drastically, to ensure the stable operation of the distribution network.

[0179] In the distribution network topology, a set of energy storage device nodes is pre-planned and set up, and these nodes are connected to specific energy input nodes in the energy input node set (such as nodes planned to be connected to photovoltaic devices). Energy storage devices can be battery energy storage systems, flywheel energy storage, or other forms of energy storage devices. Ensure that each target energy input node is directly connected to at least one energy storage device node through a power line so that energy can flow in both directions when needed, that is, the energy storage device can be charged from the grid and discharged from the energy storage device to the grid.

[0180] The management platform first uses the data collected by the sensitive monitoring node set, combined with the distribution network topology and operating parameters, to identify the weak link set in the distribution network, including the power input nodes that may be affected by the fluctuation of renewable energy. The power input nodes planned to be connected to the photovoltaic equipment are selected from the weak link set as the target power input nodes, and the corresponding target energy storage device nodes are determined based on the pre-set correspondence between the energy storage device nodes and the power input nodes. The management platform can obtain the weather forecast data at the location of the target power input node through the meteorological service interface or the self-built meteorological monitoring station, especially the prediction curve of light intensity. The light intensity prediction curve is processed by mathematical algorithms (such as slope analysis) to calculate the maximum change rate of light intensity within a preset time range in the future (such as 10 minutes, 1 hour, 2 hours, etc.), that is, the maximum slope of the prediction curve within this time period. The calculated maximum change rate is compared with the preset change rate threshold. The threshold is set according to the stable operation requirements of the distribution network and the response capability of the energy storage device. If the maximum change rate exceeds the preset threshold, it indicates that the light conditions will change drastically, which may affect the stable output of the photovoltaic equipment. At this time, the management platform sends a control instruction to make the target power input node only supply power to the corresponding target energy storage device node within the preset time range in the future, using the buffering effect of the energy storage device to reduce the impact of renewable energy fluctuations on the power grid. Based on the real-time update of weather forecast data, the management platform continuously evaluates the changes in light intensity and dynamically adjusts the charging and discharging strategies of the energy storage device to ensure the stable operation of the distribution network.

[0181] In the above scheme, by setting up energy storage device nodes on the distribution network topology and connecting them to power input nodes (especially nodes connected to photovoltaic devices), energy storage devices can be used to balance the volatility and intermittency of renewable energy when weak links appear in the power grid. When the management platform predicts that the light intensity will change significantly within a preset time range in the future (that is, the maximum change rate is greater than the preset threshold), the target power input node is controlled to supply power only to the energy storage device node, effectively avoiding the voltage fluctuation and frequency instability of the power grid caused by sudden changes in light intensity, thereby enhancing the overall stability of the power grid.

[0182] In addition, the use of energy storage equipment to store excess electricity when the light intensity changes greatly and release electricity when the light is insufficient not only ensures the maximum utilization of renewable energy, but also reduces the energy waste caused by the abandonment of light and wind. Then, through the real-time analysis and processing of the light intensity prediction data, the management platform can respond in advance and adjust the operation strategy of the power grid. When it is predicted that the light intensity will change significantly, the power supply direction of the target power input node is controlled in time, avoiding the chain reaction and failure expansion caused by the weak links of the power grid, and improving the response ability and reliability of the power grid. It can be seen that in the above scheme, the energy storage equipment is combined with the intelligent management of the distribution network to realize the refined scheduling of renewable energy access to the power grid. According to the real-time data and prediction results, the management platform automatically adjusts the charging and discharging strategy of the energy storage equipment and the operation mode of the power grid, realizing the intelligent scheduling and autonomous optimization of the power grid.

[0183] Figure 3 FIG. 1 is a schematic diagram of a power distribution network management system according to an exemplary embodiment of the present application. Figure 3 As shown, the distribution network management system 300 provided in this embodiment includes:

[0184] A management platform 310 and a distribution network 320, wherein the distribution network 320 includes a distribution network topology structure, a set of electric energy input nodes arranged at the end of the distribution network topology structure, and a set of monitoring nodes arranged in the distribution network topology structure, wherein each electric energy input node in the set of electric energy input nodes is used to access external renewable energy;

[0185] The management platform 310 obtains the power input data of each power input node in the power input node set to form a power input data set;

[0186] The management platform 310 obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set, and the monitoring nodes in the monitoring node set are arranged on the power transmission equipment nodes and / or the power transmission lines in the distribution network topology structure;

[0187] The management platform 310 uses a preset power distribution network 320 fluctuation model and determines a sensitive monitoring node set according to the power input data set and the power grid operation monitoring data set, wherein the monitoring node set includes the sensitive monitoring node set, and the preset power distribution network 320 fluctuation model is used to determine the correlation between the power data fluctuation characteristics in the power input data set and the power grid operation monitoring data fluctuation characteristics in the power grid operation monitoring data set;

[0188] The management platform 310 determines a set of weak links in the distribution network 320 from the distribution network topology structure according to the set of sensitive monitoring nodes, wherein the weak links in the set of weak links in the distribution network 320 include power transmission equipment nodes and / or power transmission lines set by the sensitive monitoring nodes in the set of sensitive monitoring nodes.

[0189] Optionally, the monitoring nodes in the monitoring node set are arranged on power transmission equipment nodes and / or transmission lines in the distribution network topology structure;

[0190] The distribution network weak links in the distribution network weak link set include power transmission equipment nodes and / or power transmission lines set by sensitive monitoring nodes in the sensitive monitoring node set.

[0191] Optionally, the monitoring node set includes the sensitive monitoring node set, and the preset distribution network fluctuation model is used to determine the correlation between the fluctuation characteristics of the electric energy data in the electric energy input data set and the fluctuation characteristics of the power grid operation monitoring data in the power grid operation monitoring data set.

[0192] Optionally, the power distribution network 320 further includes a set of power output nodes, at least some of which are used to access external power-consuming devices;

[0193] The management platform 310 obtains the power output data of each power output node in the power output node set to form a power output data set, and determines a characteristic energy proportion according to the power input data set and the power output data set, wherein the characteristic energy proportion is used to characterize the power input ratio of the external renewable energy to the distribution network 320;

[0194] The management platform 310 determines that the characteristic energy proportion is greater than a preset characteristic energy proportion threshold.

[0195] Optionally, the management platform 310 generates an electric energy input power sequence according to the electric energy input power at each time node in the electric energy input data set. ,in, is the number of time nodes in the electric energy input data set, The first The electrical energy input power at each time node;

[0196] The management platform 310 generates an electric energy output power sequence according to the electric energy output power of each time node in the electric energy output data set. ,in, The first The power output at each time node;

[0197] The management platform 310 uses formula 1 and according to the power sequence of the electric energy input And the electric energy output power sequence To determine the characteristic energy proportion, the formula 1 is:

[0198] ,

[0199] in, is the proportion of the characteristic energy.

[0200] Optionally, the management platform 310 uses Formula 2 and according to the electric energy input power sequence Determine the characteristic value of power input fluctuation , the formula 2 is:

[0201] ,

[0202] The management platform 310 is configured to generate a power fluctuation characteristic value according to the power input power fluctuation characteristic value. In the electric energy input power sequence The target time range corresponding to the grid operation monitoring data set is used to determine the grid current fluctuation amplitude set ,in, is the number of monitoring nodes in the monitoring node set, is the first node in the monitoring node set The absolute value of the difference between the maximum current and the minimum current of each monitoring node within the target time range;

[0203] The management platform 310 outputs power according to the power sequence Determining a power output fluctuation characteristic value corresponding to the target time range;

[0204] If the management platform 310 determines that the power output power fluctuation characteristic value is less than the preset power output power fluctuation threshold, and The monitoring nodes correspond to If the current fluctuation amplitude is greater than the preset threshold, the A monitoring node is determined as a sensitive monitoring node in the sensitive monitoring node set.

[0205] Optionally, the management platform 310 can generate a power fluctuation characteristic value according to the power input power fluctuation characteristic value. Determine the electrical energy input power sequence The corresponding characteristic time range is determined, and the target time range is determined according to the characteristic time range, and the target time range is the time range determined by adding a preset time length to the characteristic time range.

[0206] Optionally, the management platform 310 determines an adjacent power grid characteristic node according to a target sensitive monitoring node in the sensitive monitoring node set, wherein the power grid characteristic node is a power transmission device node and / or a power input node adjacent to the target sensitive monitoring node;

[0207] The management platform 310 determines a target transmission line set according to the grid characteristic node, wherein the target transmission lines in the target transmission line set are used to connect the grid characteristic node with other nodes in the distribution network topology structure;

[0208] The management platform 310 generates a weak link of the distribution network 320 in the weak link set of the distribution network 320 according to the grid characteristic node and the target transmission line set.

[0209] Optionally, an energy storage device node set is further provided on the distribution network topology structure, and the energy storage device nodes in the energy storage device node set are connected to the power input nodes in the power input node set;

[0210] The management platform 310 determines the corresponding target energy storage device node according to the target power input node in the weak link set of the distribution network 320, wherein the target power input node is used to access the photovoltaic device;

[0211] The management platform 310 obtains weather forecast data for the location of the target power input node, wherein the weather forecast data includes a light intensity forecast curve, and determines a maximum rate of change of light intensity within a future preset time range according to the light intensity forecast curve, wherein the maximum rate of change is a maximum slope of the light intensity forecast curve in the future preset time range;

[0212] If the management platform 310 determines that the maximum change rate is greater than a preset change rate threshold, the target power input node is controlled to only supply power to the target energy storage device node within the future preset time range.

[0213] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0214] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0215] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0216] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0217] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0218] The bus 403 is used to connect the memory 402 and the processor 401 .

[0219] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0220] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0221] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0222] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for identifying weak links in a distribution network with renewable energy access, applied to a distribution network management system, characterized in that: The distribution network management system comprises a management platform and a distribution network, wherein the distribution network comprises a distribution network topology structure, a set of electric energy input nodes arranged at the end of the distribution network topology structure, and a set of monitoring nodes arranged in the distribution network topology structure, wherein each electric energy input node in the set of electric energy input nodes is used to access external renewable energy; the method comprises: The management platform acquires the electric energy input data of each electric energy input node in the electric energy input node set to form an electric energy input data set; The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set; The management platform uses a preset power distribution network fluctuation model and determines a sensitive monitoring node set according to the power input data set and the power grid operation monitoring data set; The management platform determines a set of weak links in the distribution network from the distribution network topology structure according to the set of sensitive monitoring nodes; The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set, including: The management platform generates an electric energy input power sequence according to the electric energy input power of each time node in the electric energy input data set. ,in, is the number of time nodes in the electric energy input data set, The first The electrical energy input power at each time node; The management platform generates an electric energy output power sequence according to the electric energy output power of each time node in the electric energy output data set. ,in, The first The power output at each time node; The management platform uses Formula 2 and determines the power input power fluctuation characteristic value according to the power input power sequence. Formula 2 is: ； The management platform is based on the power input power fluctuation characteristic value In the electric energy input power sequence The target time range corresponding to the grid operation monitoring data set is used to determine the grid current fluctuation amplitude set ,in, is the number of monitoring nodes in the monitoring node set, is the first node in the monitoring node set The absolute value of the difference between the maximum current and the minimum current of the monitoring nodes within the target time range, The management platform outputs power according to the electric energy sequence Determining a power output fluctuation characteristic value corresponding to the target time range; If the management platform determines that the power output power fluctuation characteristic value is less than the preset power output power fluctuation threshold, and the The current fluctuation amplitude corresponding to the monitoring node is greater than the preset threshold , then the monitoring node is determined as a sensitive monitoring node in the sensitive monitoring node set.

2. The method for identifying weak links in a distribution network for renewable energy access according to claim 1, characterized in that: The monitoring nodes in the monitoring node set are arranged on the power transmission equipment nodes and / or the power transmission lines in the power distribution network topology structure; The distribution network weak links in the distribution network weak link set include power transmission equipment nodes and / or power transmission lines set by sensitive monitoring nodes in the sensitive monitoring node set.

3. The method for identifying weak links in a distribution network for renewable energy access according to claim 2, characterized in that: The monitoring node set includes the sensitive monitoring node set, and the preset power distribution network fluctuation model is used to determine the correlation between the power data fluctuation characteristics in the power input data set and the power grid operation monitoring data fluctuation characteristics in the power grid operation monitoring data set.

4. The method for identifying weak links in a distribution network for renewable energy access according to claim 3, characterized in that: The distribution network further includes a power output node set, at least some of which are used to access external power-consuming devices; correspondingly, before the management platform acquires the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set, it also includes: The management platform obtains the power output data of each power output node in the power output node set to form the power output data set, and determines the characteristic energy proportion according to the power input data set and the power output data set, wherein the characteristic energy proportion is used to characterize the power input ratio of the external renewable energy to the distribution network; The management platform determines that the characteristic energy proportion is greater than a preset characteristic energy proportion threshold.

5. The method for identifying weak links in a distribution network for renewable energy access according to claim 4, characterized in that: The determining the characteristic energy proportion according to the electric energy input data set and the electric energy output data set includes: The management platform uses Formula 1 and determines the characteristic energy proportion according to the electric energy input power sequence and the electric energy output power sequence. The Formula 1 is: ； in, is the proportion of the characteristic energy.

6. The method for identifying weak links in a distribution network for renewable energy access according to claim 5, characterized in that: The management platform is based on the power input power fluctuation characteristic value In the electric energy input power sequence The target time range corresponding to the grid operation monitoring data set is used to determine the grid current fluctuation amplitude set Previously, it also included: The management platform is based on the power input power fluctuation characteristic value Determine the electrical energy input power sequence The corresponding characteristic time range is determined, and the target time range is determined according to the characteristic time range, and the target time range is the time range determined by adding a preset time length to the characteristic time range.

7. The method for identifying weak links in a distribution network for renewable energy access according to any one of claims 1 to 6, characterized in that: The management platform determines a set of weak links of the distribution network from the distribution network topology structure according to the set of sensitive monitoring nodes, including: The management platform determines an adjacent power grid characteristic node according to a target sensitive monitoring node in the sensitive monitoring node set, wherein the power grid characteristic node is a power transmission device node and / or a power input node adjacent to the target sensitive monitoring node; The management platform determines a target transmission line set according to the grid characteristic node, wherein the target transmission lines in the target transmission line set are used to connect the grid characteristic node with other nodes in the distribution network topology structure; The management platform generates a distribution network weak link in the distribution network weak link set according to the target sensitive monitoring node, the power grid characteristic node and the target transmission line set.

8. The method for identifying weak links in a distribution network for renewable energy access according to claim 7, characterized in that: The distribution network topology structure is also provided with an energy storage device node set, and the energy storage device nodes in the energy storage device node set are connected to the power input nodes in the power input node set; correspondingly, after the management platform determines the distribution network weak link set from the distribution network topology structure according to the sensitive monitoring node set, it also includes: The management platform determines the corresponding target energy storage device node according to the target power input node in the weak link set of the distribution network, and the target power input node is used to access the photovoltaic device; The management platform obtains weather forecast data for the location of the target power input node, the weather forecast data includes a light intensity prediction curve, and determines a maximum rate of change of light intensity within a future preset time range according to the light intensity prediction curve, the maximum rate of change being the maximum slope of the light intensity prediction curve in the future preset time range; If the management platform determines that the maximum change rate is greater than a preset change rate threshold, the target power input node is controlled to only supply power to the target energy storage device node within the future preset time range.

9. A distribution network management system, a management platform and a distribution network, characterized in that: include: The distribution network comprises a distribution network topology structure, a set of electric energy input nodes arranged at the end of the distribution network topology structure, and a set of monitoring nodes arranged in the distribution network topology structure, wherein each electric energy input node in the set of electric energy input nodes is used to access external renewable energy; The management platform acquires the electric energy input data of each electric energy input node in the electric energy input node set to form an electric energy input data set; The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set; The management platform uses a preset power distribution network fluctuation model and determines a sensitive monitoring node set according to the power input data set and the power grid operation monitoring data set; The management platform determines a set of weak links in the distribution network from the distribution network topology structure according to the set of sensitive monitoring nodes; The management platform obtains the power grid operation monitoring data of each monitoring node in the monitoring node set to form a power grid operation monitoring data set, including: The management platform generates an electric energy input power sequence according to the electric energy input power of each time node in the electric energy input data set. ,in, is the number of time nodes in the electric energy input data set, The first The electrical energy input power at each time node; The management platform generates an electric energy output power sequence according to the electric energy output power of each time node in the electric energy output data set. ,in, The first The power output at each time node; The management platform uses Formula 2 and determines the power input power fluctuation characteristic value according to the power input power sequence. Formula 2 is: ； The management platform is based on the power input power fluctuation characteristic value In the electric energy input power sequence The target time range corresponding to the grid operation monitoring data set is used to determine the grid current fluctuation amplitude set ,in, is the number of monitoring nodes in the monitoring node set, is the first node in the monitoring node set The absolute value of the difference between the maximum current and the minimum current of the monitoring nodes within the target time range, The management platform outputs power according to the electric energy sequence Determining a power output fluctuation characteristic value corresponding to the target time range; If the management platform determines that the power output power fluctuation characteristic value is less than the preset power output power fluctuation threshold, and the The current fluctuation amplitude corresponding to the monitoring node is greater than the preset threshold , then the monitoring node is determined as a sensitive monitoring node in the sensitive monitoring node set.

10. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 8 by executing the executable instructions.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

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