Method, apparatus, device, and storage medium for obtaining a set of distribution network operation scenarios

Abstract

The present application relates to a method, device, equipment and storage medium for obtaining a set of operating scenarios of a distribution network. First, based on the operating boundary conditions of the target distribution network, the state quantity data of the target distribution network is obtained through power flow calculation; secondly, the state quantity data is classified to obtain scenario category labels; finally, according to the scenario category labels, a set of operating scenarios of the distribution network required for training the operation analysis and decision-making of the distribution network based on artificial intelligence is obtained. This method realizes sufficient training for the operation analysis and decision-making of the distribution network based on artificial intelligence.

Description

Technical Field

[0001] The present application relates to the technical field of distribution networks, and particularly to a method, device, equipment, and storage medium for obtaining an operating scenario set of a distribution network. Background Art

[0002] In the power system, the distribution network is an important end link that directly supplies electric energy to various users. With the development of the new power system, the proportion of new energy is continuously increasing, and it has strong uncertainties, including the dynamic and time-varying characteristics of the weather, the non-linear energy conversion process, and the complex spatio-temporal correlation. It is also very difficult to achieve power balance and safe operation of the distribution network and ensure the power supply reliability and power quality of users.

[0003] With the improvement of the automation level of the distribution network operation and the development of artificial intelligence, the distribution network analysis and decision-making technology based on artificial intelligence is expected to solve the above challenges. However, currently, when making decisions and analyses based on artificial intelligence technology, a large amount of distribution network operation scenario data is required, and it is difficult to obtain the operation scenario data of the distribution network, resulting in insufficient training of the artificial intelligence decision-making method and difficulty in meeting the actual power grid production and operation requirements, which urgently needs to be improved. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, equipment, and storage medium for obtaining an operating scenario set of a distribution network for distribution network operation analysis and decision-making training based on artificial intelligence technology.

[0005] In a first aspect, the present application provides a method for obtaining an operating scenario set of a distribution network. The method includes:

[0006] Based on the operating boundary conditions of the target distribution network, obtain the state quantity data of the target distribution network through power flow calculation;

[0007] Classify the state quantity data to obtain a scenario category label;

[0008] According to the scenario category label, obtain the operating scenario set of the target distribution network.

[0009] In one embodiment, classifying the state quantity data to obtain a scenario category label includes:

[0010] According to the state quantity data, obtain the operating evaluation index of the target distribution network;

[0011] Based on the operating evaluation index of the target distribution network, classify the state quantity data to obtain a scenario category label.

[0012] In one embodiment, based on the operating evaluation index of the target distribution network, classifying the state quantity data to obtain a scenario category label includes:

[0013] Classify the status quantity data based on the operation evaluation index of the target distribution network and the preset number of classifications to obtain the scenario category labels.

[0014] In one embodiment, the method further includes:

[0015] Obtain the correspondence between different meteorological factors and different operation boundary conditions of the distribution network;

[0016] Determine the operation boundary conditions of the target distribution network according to the correspondence.

[0017] In one embodiment, determining the operation boundary conditions of the target distribution network according to the correspondence includes:

[0018] Determine the operation boundary conditions of the target distribution network according to the correspondence between wind speed and wind power output, the correspondence between light intensity and photovoltaic output, and the correspondence between temperature and load output.

[0019] In one embodiment, the operation evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and heavy load rate.

[0020] In a second aspect, the present application further provides an apparatus for obtaining a distribution network operation scenario set. The apparatus includes:

[0021] The first obtaining module is configured to obtain the status quantity data of the target distribution network through power flow calculation based on the operation boundary conditions of the target distribution network;

[0022] The second obtaining module is configured to classify the status quantity data to obtain the scenario category labels;

[0023] The third obtaining module is configured to obtain the operation scenario set of the target distribution network according to the scenario category labels.

[0024] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method provided in any one of the embodiments of the first aspect are implemented.

[0025] In a fourth aspect, the present application further provides a computer-readable storage medium. A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method provided in any one of the embodiments of the first aspect are implemented.

[0026] In a fifth aspect, the present application further provides a computer program product. It includes a computer program, and when the computer program is executed by a processor, the steps of the method provided in any one of the embodiments of the first aspect are implemented.

[0027] The method, device, equipment, and storage medium for obtaining the operating scenario set of the distribution network first obtain the state quantity data of the target distribution network through power flow calculation based on the operating boundary conditions of the target distribution network, then classify the state quantity data to obtain scenario category labels, and finally obtain the operating scenario set of the target distribution network according to the scenario category labels. In this method, the state quantity data of the distribution network operation is obtained through power flow calculation, and then the obtained state quantity data is classified to obtain the operating scenario set of the distribution network required for the operation analysis and decision-making training of the distribution network based on artificial intelligence, so as to ensure sufficient training for the operation analysis and decision-making of the distribution network based on artificial intelligence and meet the actual power grid production and operation requirements. Brief Description of the Drawings

[0028] Figure 1 It is an application environment diagram of the method for obtaining the operating scenario set of the distribution network in an embodiment;

[0029] Figure 2 It is a schematic flowchart of the method for obtaining the operating scenario set of the distribution network in an embodiment;

[0030] Figure 3 It is a schematic flowchart of the method for obtaining scenario category labels in an embodiment;

[0031] Figure 4 It is a structural block diagram of the device for obtaining the operating scenario set of the distribution network in an embodiment;

[0032] Figure 5 It is an internal structure diagram of a computer device in an embodiment. Detailed Embodiments

[0033] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] The method for obtaining the operating scenario set of the distribution network provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 In the figure, the distribution network communicates with the computer device through a network. The data storage system can store the data that the computer device needs to process. The data storage system can be integrated on the computer device, or placed in the cloud or other network servers. Among them, the computer device can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, etc.

[0035] In one embodiment, as shown in Figure 2As shown in the figure, a method for obtaining an operating scenario set of a distribution network is provided. This embodiment involves, based on the operating boundary conditions of the target distribution network, obtaining the state quantity data of the target distribution network through power flow calculation, classifying the state quantity data to obtain scenario category labels, and finally obtaining the specific process of the operating scenario set of the target distribution network according to the scenario category labels. This embodiment includes the following steps:

[0036] S201, based on the operating boundary conditions of the target distribution network, obtain the state quantity data of the target distribution network through power flow calculation.

[0037] Among them, the operating boundary conditions of the distribution network refer to, under the given distribution network topology, network parameters, and without faults and other disturbances, including renewable energy output and load output. Among them, the renewable energy output includes wind power output and photovoltaic output.

[0038] Among them, the state quantity data of the distribution network includes the active power, reactive power, voltage, and phase angle information of each node in the power grid system. It should be noted that each node in the distribution network system includes a slack node. Among them, the voltage and phase angle information of the slack node are both given. The active power and reactive power of other nodes except the slack node can be directly determined by the renewable energy output and load output, while the active power and reactive power of the slack node, as well as the voltage and phase angle information of other nodes except the slack node, can be determined through power flow calculation.

[0039] Among them, the power flow equation expression involved in power flow calculation is as follows:

[0040]

[0041]

[0042] Among them, P, Q, U, and θ are the active power, reactive power, voltage, and phase angle of the node respectively, G and B represent the admittance between each node, and the subscript n represents the system node number. On the premise of knowing the active power and reactive power of the node, the voltage and phase angle information of each node can be regarded as the solution of the power flow equation. For a system of nonlinear algebraic equations, the number of solutions is uncertain. The power flow equation is essentially a set of circle equations, and its multiple solution situations can be divided into high-voltage solutions and low-voltage solutions. For a system in the normal operation range, that is, the power flow calculation starts flat and converges, it can be considered that there is a unique solution. At this time, for a set of determined active power and reactive power of the node, there corresponds a unique set of node voltage and phase angle, that is, the node voltage and phase angle are functions of the active power and reactive power of the node, and the specific representation is as follows:

[0043] (V n , θ n ) = f PF (P n , Q n )

[0044] Among them, f PF (·) represents a mapping relationship, that is, a one-to-one correspondence relationship between the known active and reactive power of the node and the node voltage and phase angle solved by the power flow equation.

[0045] Based on the above operating boundary conditions and power flow equation expressions of the distribution network, the state quantity data of the target distribution network can be obtained.

[0046] S202. Classify the state quantity data to obtain scene category labels.

[0047] Among them, the scene category label refers to the label corresponding to the state quantity data, which can be obtained by classifying the state quantity data.

[0048] Specifically, after obtaining the state quantity data of the target distribution network, classifying the obtained state quantity data of the target distribution network can obtain multiple types of state quantity data. One type of state quantity data corresponds to one scene category label, so multiple types of state quantity data correspond to multiple scene category labels. For example, after obtaining the state quantity data of the target distribution network, classifying the voltage quality in the obtained state quantity data of the target distribution network can obtain multiple different types of voltage quality, and one type of voltage quality corresponds to one scene category label.

[0049] S203. Obtain the operating scene set of the target distribution network according to the scene category labels.

[0050] Based on the above-obtained state quantity data and scene category labels, the operating scene set of the distribution network can be obtained. One type of state quantity data and the scene category label corresponding to this type of state quantity data form an operating scene of the distribution network. Multiple types of state quantity data and the scene category labels respectively corresponding to multiple types of state quantity data form multiple operating scenes of the distribution network, and these multiple operating scenes form the operating scene set of the distribution network. For example, classifying the voltage quality in the state quantity data of the distribution network system can obtain multiple types of voltage quality, and each type of voltage quality corresponds to one scene category label. Then one type of voltage quality and the one scene category label corresponding to this type of voltage quality form an operating scene of the distribution network, and multiple types of voltage quality and the multiple scene category labels corresponding to these multiple types of voltage quality form the operating scene set of the distribution network.

[0051] In this embodiment, first, based on the operation boundary conditions of the target distribution network, the state quantity data of the target distribution network is obtained through power flow calculation. Secondly, the state quantity data is classified to obtain scene category labels. Finally, according to the scene category labels, the operation scene set of the target distribution network is obtained. In this method, the state quantity data of the distribution network operation is obtained through power flow calculation, and then the obtained state quantity data is classified to obtain the distribution network operation scene set required for the operation analysis and decision-making training of the distribution network based on artificial intelligence, so as to ensure that the distribution network can be fully trained for operation analysis and decision-making based on artificial intelligence, meeting the actual power grid production and operation requirements.

[0052] Referring to Figure 3 , Figure 3 FIG. is a schematic flowchart of a method for obtaining scene category labels provided by an embodiment of the present application. This embodiment relates to an optional implementation manner of how to classify state quantity data to obtain scene category labels. On the basis of the above embodiment, the above S202 includes the following steps:

[0053] S301. Obtain the operation evaluation indexes of the target distribution network according to the state quantity data.

[0054] Among them, the operation evaluation indexes of the distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and heavy load rate.

[0055] It can be understood that in the actual operation and analysis of the distribution network, all state quantity data in the distribution network system are not always considered simultaneously. Instead, corresponding operation evaluation indexes of the distribution network are established for analysis. Moreover, a single operation evaluation index of the distribution network often cannot well reflect the state of the distribution network system. Therefore, this application establishes multiple operation evaluation indexes of the distribution network at different levels such as safety, economy, and stability to evaluate the state of the distribution network system; among them, the operation evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and heavy load rate.

[0056] Furthermore, the specific calculation of obtaining the operation evaluation indexes of the target distribution network according to the state quantity data is specifically described.

[0057] Regarding the line loss rate, ensuring the economy of the distribution network system operation is also one of the basic requirements of the power system operation. The line loss rate (Power Loss Rate, PLR) can reflect the economy of the system operation to a certain extent. Its calculation formula is as follows:

[0058]

[0059] Among them, P Gi represents the injection power of node i, PDi Represents the out-flow power of node i. This formula represents the ratio of the difference between the total power generation and the total power consumption in the distribution network system to the total power generation.

[0060] Regarding voltage quality, from the perspective of power quality, voltage is one of the main power quality indicators. When the operating voltage exceeds the allowable deviation value, it will affect the operation of various equipment in the distribution network system. The voltage quality can be calculated using the Bus Voltage Quality Index (BVQI), and the calculation formula of the bus voltage quality index is as follows:

[0061]

[0062] Among them, V i Represents the actual voltage of node i, and V ri Represents the rated voltage of node i. This formula represents the average voltage deviation rate of all nodes in the distribution network system.

[0063] Regarding the voltage stability margin, from the perspective of the operation safety of the distribution network, the static voltage stability margin of the distribution network system is an important indicator. When the voltage in the system crosses the static voltage stability limit, a voltage collapse accident will occur, endangering the system safety. The voltage stability margin can be the minimum singular value of the Jacobian matrix of the power flow equation. In actual calculation, methods such as the singular value decomposition method, the sensitivity method, the continuous power flow continuation method, the collapse point method, and the nonlinear programming method can be used to obtain it.

[0064] In the actual operation of the distribution network system, if the transmission line is overloaded or even severely overloaded, it will affect the safe and stable operation of the distribution network system. Therefore, the load index also needs to be concerned. The line load mainly refers to the magnitude of the current in the line. The following indicators can be established accordingly, including the maximum, minimum, and average load rates (Load Rate, LR) of the line, as well as the heavy-load rate (Heavy-Load Rate, HR), etc. The calculation formulas of these indicators are as follows:

[0065]

[0066]

[0067]

[0068]

[0069] Among them, I i Represents the actual current of line i, and I max Represents the maximum current of line i, and P i Is an indicator function indicating whether line i is overloaded. The expression of this indicator function is as follows:

[0070]

[0071] By comprehensively considering the above various load-related indicators, the overall load level during the operation of the distribution network system can be evaluated.

[0072] S302. Based on the operation evaluation indicators of the target distribution network, classify the status quantity data to obtain scenario category labels.

[0073] Furthermore, after obtaining the operation evaluation indicators of the distribution network, classify the status quantity data to obtain scenario category labels. Specifically, each operation evaluation indicator of the distribution network is presented in the form of a numerical value. For all the obtained status quantity data, the numerical value of the corresponding operation evaluation indicator of the distribution network must correspond to a numerical range. Classify the overall numerical range, that is, classify all the status quantity data according to the numerical range of any operation evaluation indicator of the distribution network. It should be noted that custom scenario category labels can be set for the status quantity data in different numerical ranges, for example, corresponding to the categories of low, relatively low, medium, relatively high, and high in sequence. For example, there are 2,500 groups of status quantity data, and the numerical range corresponding to the voltage quality of these 2,500 groups of status quantity data is 0.9 - 0.95. Divide these 2,500 groups of status quantity data into five categories, that is, corresponding to the numerical ranges of voltage quality of 0.9 - 0.91, 0.91 - 0.92, 0.92 - 0.93, 0.93 - 0.94, and 0.94 - 0.95 respectively. Among them, 500 groups of status quantity data correspond to the numerical range of voltage quality of 0.92 - 0.93, then these 500 groups of status quantity data correspond to the same scenario label.

[0074] The method provided in this embodiment can, by obtaining the operation evaluation indicators of the target distribution network according to the status quantity data and classifying the status quantity data based on the operation evaluation indicators of the target distribution network to obtain scenario category labels, realize classifying the status quantity data based on the operation evaluation indicators of the target distribution network to obtain scenario category labels, and then obtain the operation scenario set of the target distribution network according to the scenario category labels, so as to ensure sufficient training for the operation analysis and decision-making of the distribution network based on artificial intelligence and meet the actual power grid production and operation requirements.

[0075] In one embodiment, S302 is further refined. Optionally, classifying the status quantity data based on the operation evaluation indicators of the target distribution network to obtain scenario category labels can be to classify the status quantity data based on the operation evaluation indicators of the target distribution network and the preset number of classifications to obtain scenario category labels;

[0076] Specifically, each operation evaluation index of the distribution network is presented in the form of a numerical value. For each set of operation boundary conditions, there is a corresponding set of state quantity data, and a corresponding set of numerical values of the operation evaluation index of the distribution network. When obtaining the numerical values of the operation evaluation index of a set of target distribution networks, the numerical values of the operation evaluation index of this set of target distribution networks must correspond to a numerical range. According to the preset number of classifications, the operation evaluation index of this set of target distribution networks within this numerical range is divided into intervals, obtaining the operation evaluation indexes of the target distribution networks in multiple different interval ranges. Further, according to the operation evaluation indexes of the target distribution networks in these multiple different interval ranges, this set of state quantity data is classified to obtain different types of state quantity data. Then, any one of the interval ranges of the operation evaluation indexes of the target distribution networks in these multiple different interval ranges corresponds to a type of state quantity data. Since each interval range corresponds to a different scenario category label respectively, and each interval range corresponds to a different type of state quantity data respectively, therefore, different types of state quantity data correspond to different scenario category labels. For example, according to a set of state quantity data of the target distribution network, a set of operation evaluation indexes of the target distribution network is obtained, and this set of operation evaluation indexes refers to the operation evaluation index of the target distribution network. Among them, the numerical range of the voltage quality in the obtained set of operation evaluation indexes is between 0.9 and 0.95. Then, the voltage quality with a numerical range between 0.9 and 0.95 is divided into intervals. For example, the preset number of classifications is 5, that is, the voltage quality with a numerical range between 0.9 and 0.95 can be divided into five different interval ranges of voltage quality: voltage quality of 0.9 - 0.91, voltage quality of 0.91 - 0.92, voltage quality of 0.92 - 0.93, voltage quality of 0.93 - 0.94, and voltage quality of 0.94 - 0.95. Further, according to these five different interval ranges of voltage quality, this set of state quantity data is classified. Then, each of these five different interval ranges of voltage quality corresponds to a type of state quantity data, that is, five types of state quantity data can be obtained. Furthermore, any one of these five types of state quantity data corresponds to a scenario category label. For example, the voltage quality of 0.9 - 0.91, the voltage quality of 0.91 - 0.92, the voltage quality of 0.92 - 0.93, the voltage quality of 0.93 - 0.94, and the voltage quality of 0.94 - 0.95 are respectively defined as low voltage quality, relatively low voltage quality, medium voltage quality, relatively high voltage quality, and high voltage quality. Among them, for example, the scenario category label corresponding to this type of state quantity data corresponding to the voltage quality of 0.9 - 0.91 is low voltage quality.

[0077] The method provided in this embodiment classifies the state quantity data based on the operation evaluation index of the target distribution network and the preset number of classifications to obtain the scenario category label, so as to realize classifying the state quantity data based on the operation evaluation index of the target distribution network to obtain the scenario category label, and then obtain the operation scenario set of the target distribution network according to the scenario category label, so as to ensure sufficient training for the operation analysis and decision-making of the distribution network based on artificial intelligence and meet the actual power grid production and operation requirements.

[0078] Next, the specific implementation process of obtaining the operation boundary conditions of the target distribution network is introduced.

[0079] In one embodiment, the method further includes obtaining the correspondence between different meteorological factors and different operation boundary conditions of the distribution network; determining the operation boundary conditions of the target distribution network according to the correspondence.

[0080] Among them, the meteorological factors include but are not limited to temperature, air pressure, precipitation, visibility, humidity, and light intensity;

[0081] Furthermore, determining the correspondence between different meteorological factors and different operation boundary conditions of the distribution network, that is, determining the correspondence between temperature, air pressure, precipitation, visibility, humidity, and light intensity and the output of renewable energy or load. For example, the correspondence may include the correspondence between wind speed and wind power output, the correspondence between light intensity and photovoltaic output, and the correspondence between temperature and load output.

[0082] After obtaining the correspondence between different meteorological factors and different operation boundary conditions of the distribution network, the operation boundary conditions of the target distribution network can be directly determined according to the correspondence. For example, when the wind speed of the target distribution network is obtained, the wind power output, which is the operation boundary condition of the target distribution network, can be directly determined according to the correspondence between wind speed and wind power output; correspondingly, when the light intensity of the target distribution network is obtained, the photovoltaic output, which is the operation boundary condition of the target distribution network, can be directly determined according to the correspondence between light intensity and photovoltaic output, and when the temperature of the target distribution network is obtained, the load output, which is the operation boundary condition of the target distribution network, can be directly determined according to the correspondence between temperature and load output.

[0083] In this embodiment, by obtaining the correspondence between different meteorological factors and different operation boundary conditions of the distribution network, the operation boundary conditions of the target distribution network can be determined for obtaining the state quantity data of the distribution network through power flow calculation later.

[0084] In one embodiment, the operating boundary conditions of the target distribution network are determined according to the corresponding relationships, including determining the operating boundary conditions of the target distribution network according to the corresponding relationship between wind speed and wind power output, the corresponding relationship between light intensity and photovoltaic output, and the corresponding relationship between temperature and load output.

[0085] Further, the corresponding relationship between wind speed and wind power output is as follows:

[0086]

[0087] Wherein, P W is the output of the wind turbine, V t is the wind speed at time t, V ci is the cut-in wind speed of the wind turbine, V co is the cut-out wind speed of the wind turbine, V r is the rated wind speed of the wind turbine, P r is the rated power of the wind turbine, and A, B, and C are fitting coefficients.

[0088] The corresponding relationship between light intensity and photovoltaic output is as follows:

[0089]

[0090] Wherein, P b is the photovoltaic output, P sn is the rated power of the photovoltaic array, G bt is the light intensity coefficient at time t, G ste is the unit light intensity, R c is the light intensity of a specific intensity.

[0091] The corresponding relationship between temperature and load output is as follows:

[0092]

[0093] Wherein, E is the daily power consumption, which represents the load output, n is the date, and Temp n，mean represents the daily average temperature, and a, b, and c are fitting coefficients.

[0094] In this embodiment, through the corresponding relationships between wind speed and wind power output, light intensity and photovoltaic output, and temperature and load output, the operating boundary conditions of the target distribution network can be determined, providing data support for obtaining state quantity data through calculation later.

[0095] In addition, in one embodiment, the present application also provides a complete example, which is based on the IEEE 33-node distribution system. The meteorological data corresponding to the meteorological factors is sourced from a real dataset. The meteorological data of Beijing area in the past seven years is selected, including wind speed, light intensity, and temperature, which respectively correspond to wind power output, photovoltaic power output, and load output, as the boundary conditions for the operation of the distribution network, so as to obtain the distribution network operation data.

[0096] This example selects the meteorological data of 2555 days in seven years. Through the corresponding relationships between the above-mentioned wind speed and wind power output, light intensity and photovoltaic power output, and temperature and load output, the renewable energy output and load of the system are calculated, with each day as a data sample. For each sample, considering a time resolution of 5 minutes, there are 288 moments in a day, and each moment corresponds to a system power flow section. For each system power flow section, the data includes the active and reactive power injections of the slack node, the voltage and phase angle information of all nodes, the output of renewable energy, and the active power demand of the load node. Assuming that the power factor of the load node is fixed, the reactive power load does not need to be considered. For a system with 1 slack node, n con connected nodes, n load load nodes, and n gen renewable energy outputs, the amount of data n included in each power flow section is:

[0097]

[0098] For the IEEE 33-node distribution network, each power flow section contains 108 data, and each sample is a 108x288 matrix, with a total of 2555 data points.

[0099] The selected evaluation indicators for the operation of the distribution network include six indicators: line loss rate, voltage quality, voltage stability margin, average line load rate, maximum line load rate, and heavy load rate. For the daily power flow sequence, the average value, average value, minimum value, average value, maximum value, and average value of each moment indicator are respectively taken as the daily power flow sequence indicators. For the classification of scenarios, according to the voltage quality indicator, it is evenly divided into five types of samples, which can be used as labels when training relevant models.

[0100] In this way, first, based on the corresponding relationships between wind speed and wind power output, light intensity and photovoltaic output, and temperature and load output, the operating boundary conditions of the target distribution network are determined. Second, the state variable data of the distribution network operation are obtained through calculation. Then, based on the state variable data, six operation evaluation indexes of the distribution network, namely, line loss rate, voltage quality, voltage stability margin, average line load rate, maximum line load rate, and heavy load rate, are calculated. Finally, the state variable data are classified to obtain the scenario category labels. For example, the voltage quality index is evenly divided into five categories of samples. One category of voltage quality corresponds to one scenario category label. This category of voltage quality and its corresponding scenario category label are a distribution network operation scenario. All categories of voltage quality and their corresponding multiple scenario category labels are the distribution network operation scenario set.

[0101] In this embodiment, first, based on the corresponding relationships between wind speed and wind power output, light intensity and photovoltaic output, and temperature and load output, the operating boundary conditions of the target distribution network are determined. Then, based on the operating boundary conditions of the target distribution network, the state variable data of the target distribution network are obtained through power flow calculation. Second, the state variable data are classified to obtain the scenario category labels. Finally, based on the scenario category labels, the distribution network operation scenario set required for training the distribution network operation analysis and decision-making based on artificial intelligence is obtained. Thus, the obtained operation scenario set can be used as the data required for training, realizing the sufficient training of the distribution network operation analysis and decision-making based on artificial intelligence.

[0102] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0103] Based on the same inventive concept, the embodiment of the present application also provides a device for obtaining a distribution network operation scenario set for implementing the above-mentioned method for obtaining a distribution network operation scenario set. The implementation solution provided by this device for solving problems is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for obtaining a distribution network operation scenario set provided below can refer to the limitations on the method for obtaining a distribution network operation scenario set in the above text, and will not be repeated here.

[0104] In one embodiment, as Figure 4 shown, a device 400 for obtaining an operating scenario set of a distribution network is provided, including: a first obtaining module 401, a second obtaining module 402, and a third obtaining module 403, where:

[0105] The first obtaining module 401 is configured to obtain state quantity data of the target distribution network through power flow calculation based on the operating boundary conditions of the target distribution network;

[0106] The second obtaining module 402 is configured to classify the state quantity data to obtain scenario category labels;

[0107] The third obtaining module 403 is configured to obtain an operating scenario set of the target distribution network according to the scenario category labels.

[0108] In one embodiment, the second obtaining module 402 includes:

[0109] The first obtaining unit is configured to obtain an operating evaluation index of the target distribution network according to the state quantity data;

[0110] The second obtaining unit is configured to classify the state quantity data to obtain scenario category labels based on the operating evaluation index of the target distribution network.

[0111] In one embodiment, the second obtaining unit is specifically configured to classify the state quantity data to obtain scenario categories based on the operating evaluation index of the target distribution network and a preset number of classifications.

[0112] In one embodiment, the device 400 further includes:

[0113] An obtaining module is configured to obtain the correspondence between different meteorological factors and different operating boundary conditions of the distribution network;

[0114] A determining module is configured to determine the operating boundary conditions of the target distribution network according to the correspondence.

[0115] In one embodiment, the determining module is specifically configured to determine the operating boundary conditions of the target distribution network according to the correspondence between wind speed and wind power output, the correspondence between light intensity and photovoltaic output, and the correspondence between temperature and load output.

[0116] In one embodiment, the operating evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and heavy load rate.

[0117] Each module in the above power distribution network operation scenario set acquisition device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0118] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a method for acquiring a power distribution network operation scenario set. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0119] Those skilled in the art can understand that Figure 5 the structure shown in

[0120] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0121] Based on the operation boundary conditions of the target power distribution network, obtain the state quantity data of the target power distribution network through power flow calculation;

[0122] Classify the state quantity data to obtain scenario category labels;

[0123] According to the scenario category labels, obtain the operation scenario set of the target power distribution network.

[0124] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0125] Obtain the operation evaluation indexes of the target distribution network according to the status quantity data;

[0126] Classify the status quantity data based on the operation evaluation indexes of the target distribution network to obtain the scenario category labels.

[0127] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0128] Classify the status quantity data based on the operation evaluation indexes of the target distribution network and the preset number of classifications to obtain the scenario category labels.

[0129] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0130] Obtain the corresponding relationship between different meteorological factors and different operation boundary conditions of the distribution network;

[0131] Determine the operation boundary conditions of the target distribution network according to the corresponding relationship.

[0132] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0133] Determine the operation boundary conditions of the target distribution network according to the corresponding relationship between wind speed and wind power output, the corresponding relationship between light intensity and photovoltaic output, and the corresponding relationship between temperature and load output.

[0134] In one embodiment, the operation evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, average line load rate, maximum line load rate, and heavy load rate.

[0135] For the computer device provided above, the principles and specific processes in implementing the embodiments can be referred to the descriptions in the embodiments of the method for obtaining the distribution network operation scenario set in the foregoing embodiments, and will not be elaborated here.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0137] Based on the operation boundary conditions of the target distribution network, obtain the status quantity data of the target distribution network through power flow calculation;

[0138] Classify the status quantity data to obtain the scenario category labels;

[0139] According to the scenario category labels, obtain the operation scenario set of the target distribution network.

[0140] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0141] Obtain the operation evaluation indexes of the target distribution network according to the status quantity data;

[0142] Classify the status quantity data based on the operation evaluation indexes of the target distribution network to obtain the scenario category labels.

[0143] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0144] Classify the status quantity data based on the operation evaluation indexes of the target distribution network and the preset number of classifications to obtain the scenario category labels.

[0145] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0146] Obtain the corresponding relationships between different meteorological factors and different operation boundary conditions of the distribution network;

[0147] Determine the operation boundary conditions of the target distribution network according to the corresponding relationships.

[0148] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0149] Determine the operation boundary conditions of the target distribution network according to the corresponding relationship between wind speed and wind power output, the corresponding relationship between light intensity and photovoltaic output, and the corresponding relationship between temperature and load output.

[0150] In one embodiment, the operation evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, average line load rate, maximum line load rate, and heavy load rate.

[0151] For the computer-readable storage medium provided above, the principles and specific processes in implementing each embodiment can be referred to the descriptions in the embodiments of the method for obtaining the distribution network operation scenario set in the foregoing embodiments, and will not be elaborated here.

[0152] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0153] Based on the operation boundary conditions of the target distribution network, obtain the status quantity data of the target distribution network through power flow calculation;

[0154] Classify the status quantity data to obtain the scenario category labels;

[0155] Obtain the operation scenario set of the target distribution network according to the scenario category labels.

[0156] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0157] Obtain the operation evaluation indexes of the target distribution network according to the status quantity data;

[0158] Classify the status quantity data based on the operation evaluation indexes of the target distribution network to obtain scene category labels.

[0159] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0160] Classify the status quantity data based on the operation evaluation indexes of the target distribution network and the preset number of classifications to obtain scene category labels.

[0161] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0162] Obtain the correspondence between different meteorological factors and different operation boundary conditions of the distribution network;

[0163] Determine the operation boundary conditions of the target distribution network according to the correspondence.

[0164] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0165] Determine the operation boundary conditions of the target distribution network according to the correspondence between wind speed and wind power output, the correspondence between light intensity and photovoltaic output, and the correspondence between temperature and load output.

[0166] In one embodiment, the operation evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, average line load rate, maximum line load rate, and heavy load rate.

[0167] For the computer program product provided above, the principles and specific processes in implementing the embodiments can be referred to the descriptions in the embodiments of the method for obtaining the distribution network operation scenario set in the foregoing embodiments, and will not be elaborated here.

[0168] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0169] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0170] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for obtaining a set of distribution network operation scenarios, characterized in that, The method includes: Based on the operating boundary conditions of the target distribution network, obtaining the state variable data of the target distribution network through power flow calculation; According to the state variable data, obtaining the operating evaluation indexes of the target distribution network; the operating evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and heavy load rate; Based on the operating evaluation indexes of the target distribution network and the preset number of classifications, classifying the state variable data to obtain the scenario category labels; According to the scenario category labels, obtaining the operating scenario set of the target distribution network; Obtaining the corresponding relationship between different meteorological factors and different operating boundary conditions of the distribution network; Determining the operating boundary conditions of the target distribution network according to the corresponding relationship between wind speed and wind power output, the corresponding relationship between light intensity and photovoltaic output, and the corresponding relationship between temperature and load output.

2. An acquisition device for a set of distribution network operation scenarios, characterized in that, The device includes: A first obtaining module, configured to obtain the state variable data of the target distribution network through power flow calculation based on the operating boundary conditions of the target distribution network; A second obtaining module, configured to obtain the operating evaluation indexes of the target distribution network according to the state variable data; the operating evaluation indexes of the target distribution network include line loss rate, voltage quality, voltage stability margin, line average load rate, line maximum load rate, and heavy load rate; based on the operating evaluation indexes of the target distribution network and the preset number of classifications, classifying the state variable data to obtain the scenario category labels; A third obtaining module, configured to obtain the operating scenario set of the target distribution network according to the scenario category labels; An obtaining module, configured to obtain the corresponding relationship between different meteorological factors and different operating boundary conditions of the distribution network; A determining module, configured to determine the operating boundary conditions of the target distribution network according to the corresponding relationship between wind speed and wind power output, the corresponding relationship between light intensity and photovoltaic output, and the corresponding relationship between temperature and load output.

3. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method described in claim 1 are implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method described in claim 1 are implemented.

5. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method described in claim 1 are implemented.

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