Power dispatching method, system and equipment under disaster

Abstract

The invention provides a power dispatching method, system and equipment under disasters, and the method comprises the steps: constructing a meteorological model of a target meteorological disaster, and obtaining meteorological characteristic parameters of the target meteorological disaster in an area where a power grid is located based on the meteorological model of the target meteorological disaster; acquiring line fault parameters of a target structure influenced by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, and acquiring output parameters of target power generation equipment influenced by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters; and determining a target power flow parameter of the power grid based on the line fault parameter and the output parameter, determining a target Pareto solution of each target function in a preset function set based on the target power flow parameter and a preset normal constraint algorithm, and performing power dispatching on the power grid according to the target Pareto solution of each target function. Based on the scheme of the invention, the distribution and utilization of power resources in the power grid can be optimized, and the toughness and disaster resistance of the power grid under meteorological disasters are improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric power dispatching, and in particular, to a method, system and device for electric power dispatching under disasters. Background Art

[0002] As society continues to increase its requirements for the stability and reliability of power supply, the power grid needs to pay more attention to its ability to respond to meteorological disasters during design and operation. For example, meteorological disasters such as typhoons, heavy rains, and blizzards can damage specific structures and equipment in the power grid, which not only directly affects the normal operation of the power grid, but may also cause large-scale power outages through chain reactions. Therefore, how to improve the resilience of the power grid under meteorological disasters has become an urgent problem to be solved. Summary of the invention

[0003] The embodiments of the present application provide a method, system and device for power dispatching under disaster conditions.

[0004] A first aspect of an embodiment of the present application provides a method for power dispatching under disaster conditions, the method comprising:

[0005] Construct a meteorological model of the target meteorological disaster, and obtain meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located based on the meteorological model of the target meteorological disaster;

[0006] Obtain line fault parameters of target structures affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters, and obtain output parameters of target power generation equipment affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters;

[0007] The target flow parameters of the power grid are determined based on the line fault parameters and the output parameters, the target Pareto solutions of each target function in the preset function set are determined based on the target flow parameters and the preset normal constraint algorithm, and the power grid is dispatched according to the target Pareto solutions of each target function.

[0008] A second aspect of the embodiment of the present application provides a disaster power dispatching system, including:

[0009] A first acquisition unit is used to construct a meteorological model of a target meteorological disaster, and acquire meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located based on the meteorological model of the target meteorological disaster;

[0010] The second acquisition unit is used to acquire line fault parameters of target structures affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters, and to acquire output parameters of target power generation equipment affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters;

[0011] The dispatching unit is used to determine the target flow parameters of the power grid based on the line fault parameters and the output parameters, determine the target Pareto solution of each objective function in the preset function set based on the target flow parameters and the preset normal constraint algorithm, and perform power dispatch on the power grid according to the target Pareto solution of each objective function.

[0012] According to a third aspect of an embodiment of the present application, a computer device is provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0013] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0015] Figure 1 A schematic diagram of a flow chart of a method for power dispatching under disaster conditions provided by one embodiment of the present application;

[0016] Figure 2 An example schematic diagram of a two-layer control architecture of a distribution network provided by an embodiment of the present application;

[0017] Figure 3 A schematic diagram of a process for obtaining meteorological characteristic parameters provided in one embodiment of the present application;

[0018] Figure 4 A schematic diagram of a process for obtaining line fault parameters provided by an embodiment of the present application;

[0019] Figure 5 A schematic diagram of a process for obtaining output parameters provided in one embodiment of the present application;

[0020] Figure 6 A schematic diagram of a complete parameterized typhoon spatiotemporal evolution distribution network disaster dynamic update calculation process provided by an embodiment of the present application;

[0021] Figure 7 A schematic diagram of a process for determining target power flow parameters provided by an embodiment of the present application;

[0022] Figure 8 A schematic diagram of a process for determining a target Pareto solution provided by an embodiment of the present application;

[0023] Fig. 9An example schematic diagram of the multi-stage impact of a typhoon disaster on a power grid provided in one embodiment of the present application;

[0024] Fig.10 A schematic diagram of a process for determining a target solution plane provided in accordance with an embodiment of the present application;

[0025] Fig.11 A schematic diagram of a process for determining a target Pareto solution based on a scanning method provided by an embodiment of the present application;

[0026] Fig.12 A schematic diagram of a process for determining a target Pareto solution based on scanning and screening provided by an embodiment of the present application;

[0027] Fig.13 An example schematic diagram of a two-dimensional convex solution edge feature and a non-convex solution edge feature provided by an embodiment of the present application;

[0028] Fig.14 An example schematic diagram of a three-dimensional convex solution edge feature and a non-convex solution edge feature provided by an embodiment of the present application;

[0029] Fig.15 An example schematic diagram of a distribution network topology provided for an embodiment of the present application;

[0030] Fig.16 An example schematic diagram of the output of a target power generation device provided in one embodiment of the present application;

[0031] Fig.17 An example schematic diagram of the output of a target power generation device provided in one embodiment of the present application;

[0032] Fig.18 An example schematic diagram of energy storage charging and discharging provided in one embodiment of the present application;

[0033] Fig.19 An example schematic diagram of the total load of a power grid provided in one embodiment of the present application;

[0034] Fig. 20 An example schematic diagram of obtaining a solution set based on a normal constraint algorithm provided in one embodiment of the present application;

[0035] Fig.21 An example schematic diagram of obtaining a solution set based on a weighted algorithm provided in one embodiment of the present application;

[0036] Fig. 22 An example schematic diagram of screening solution sets of different solution precisions provided in one embodiment of the present application;

[0037] Fig.23 An example schematic diagram of screening a solution set provided for an embodiment of the present application;

[0038] Fig.24 An example schematic diagram of the time complexity corresponding to different solution algorithms provided in one embodiment of the present application;

[0039] Fig.25 A schematic diagram of a brief flow chart of a method for power dispatching under disaster conditions provided by an embodiment of the present application;

[0040] Fig.26 A schematic diagram of a comprehensive flow of a method for power dispatching under disaster conditions provided by an embodiment of the present application;

[0041] Fig. 27 A schematic diagram of the structure of a power dispatching system under disaster conditions provided by one embodiment of the present application;

[0042] Fig.28 A schematic diagram of the computer device structure provided for one embodiment of the present application. DETAILED DESCRIPTION

[0043] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0044] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0045] As society continues to increase its requirements for the stability and reliability of power supply, the power grid needs to pay more attention to its ability to respond to meteorological disasters in its design and operation. As a key infrastructure for power transmission and distribution, the resilience of the power grid in the face of meteorological disasters such as typhoons, heavy rains, and blizzards has become an important indicator for measuring the stability and reliability of the system. The impact of meteorological disasters on the power grid is multifaceted. For example, strong winds may cause the transmission lines to swing, break, or even collapse the towers, causing damage to the transmission lines; for another example, heavy rains and blizzards may also damage the basic structure of the power grid and cause related equipment failures. These meteorological disasters not only directly affect the normal operation of the power grid, but may also cause large-scale power outages through chain reactions.

[0046] In order to effectively respond to the challenges of meteorological disasters to the power grid, it is necessary to improve the resilience of the power grid under meteorological disasters. Based on this, the scheme mainly provided by the embodiment of the present application includes: first, obtaining the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located, the line fault parameters of the target structure affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, and the output parameters of the target power generation equipment affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters. It can be seen that the above parameters can characterize the impact of the target meteorological disaster on the power grid, and the target flow parameters of the power grid are determined based on the above parameters to describe the power flow state in the power grid. Furthermore, based on the target flow parameters and the preset normal constraint algorithm, the target Pareto solution of each objective function in the preset function set is determined, so that the target Pareto solution can effectively balance the requirements of each objective function. Finally, the power grid is dispatched according to the target Pareto solution of each objective function, which can optimize the allocation and utilization of power resources in the power grid and improve the resilience and disaster resistance of the power grid under meteorological disasters.

[0047] The following will be combined Figure 1 - Fig.26 , the power dispatching method under disasters provided in the embodiments of the present application is introduced in detail.

[0048] See also Figure 1 , Figure 1 A schematic diagram of a flow chart of a method for power dispatching under disaster conditions provided by an embodiment of the present application. Figure 1 As shown, the method of the embodiment of the present application may include the following steps S101-S103.

[0049] S101, constructing a meteorological model of a target meteorological disaster, and obtaining meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located based on the meteorological model of the target meteorological disaster;

[0050] Specifically, on one hand, the target meteorological disaster involved in this embodiment is a type of meteorological disaster, which may be various weather phenomena occurring in nature, such as typhoons, rainstorms, snowstorms, tornadoes, thunderstorms, hail, high temperatures, low temperatures (cold waves), sandstorms, etc. In some possible implementations, the target meteorological disaster may specifically refer to a typhoon disaster, which is characterized by its strong wind and rainfall, and may cause damage to power transmission lines, substation facilities, and power generation equipment related to the power grid.

[0051] The meteorological characteristic parameters of the target meteorological disaster refer to the quantitative data describing the intensity and characteristics of the target meteorological disaster. Taking typhoon disaster as an example, the corresponding meteorological characteristic parameters can be the typhoon center pressure, maximum wind speed, maximum instantaneous wind speed, wind circle radius, rainfall, rainfall intensity, duration, travel path, wind direction change, wind speed at a specific location, wind direction at a specific location, etc., which can reflect the intensity, movement path, impact range and possible disaster degree of the typhoon, and provide basic data for evaluating the impact of typhoon on the power grid.

[0052] The power grid involved in this embodiment refers to a network structure used to transmit or distribute electric energy in a power system, such as a transmission network or a distribution network.

[0053] Among them, the transmission network is mainly responsible for transmitting the electric energy generated by the power station to the load center or distribution network over long distances. Its characteristics are relatively high voltage level, long transmission distance, large capacity, complex structure, etc. The reliability and stability of the transmission network affect the operation of the entire power system and are directly related to the transmission efficiency and power supply quality of electric energy.

[0054] The distribution network is mainly responsible for distributing the electric energy transmitted by the transmission network to each user. Its characteristics are relatively low voltage level, wide coverage, large number of users, relatively simple structure but many branches, etc. The reliability and safety of the distribution network are directly related to the user's electricity experience and the quality of power supply. Especially in the face of emergencies such as meteorological disasters, the resilience of the distribution network is particularly important.

[0055] For example, in some possible implementations, based on the characteristics of the distribution network (such as a large number of users, a wide coverage area, a relatively simple structure but many branches, etc.), the power grid involved in this embodiment may specifically refer to the distribution network. Compared with the transmission network, the distribution network has a low degree of redundancy, uneven distribution of sources and loads, and access to a high proportion of uncertain resources, which makes it inherently vulnerable and has a weak ability to cope with typhoons. However, the diverse distributed resources are both the source of its vulnerability and provide a rich scheduling strategy for the distribution network to cope with extreme disasters. Existing research focuses on a single resource and lacks overall support for scheduling. Various flexible resources in the distribution network can be used to collaboratively support the response to meteorological disasters.

[0056] Regarding the process of obtaining the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located, in some possible implementations, the meteorological data of the area where the power grid is located can be obtained, and the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located can be determined based on the meteorological data. In some possible implementations, the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located are predetermined and stored in a local or remote server, and the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located can be directly read from the local or remote server.

[0057] S102, obtaining line fault parameters of target structures affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters, and obtaining output parameters of target power generation equipment affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters.

[0058] On the one hand, the target structure involved in this embodiment refers to the physical structure in the power grid that may be affected by the target meteorological disaster, such as the transmission lines, substation facilities, towers, etc. in the power grid. These physical structures may fail or be damaged under the influence of the target meteorological disaster, thereby affecting the normal operation of the power grid.

[0059] Taking typhoon disasters as an example, the target structures affected by typhoon disasters in the power grid can be transmission lines and towers. Among them, transmission lines, as the main channel for power transmission, may swing, break or even collapse under the influence of strong winds of typhoons, resulting in power transmission interruption. Towers, as important facilities supporting transmission lines, may also tilt, break or collapse under the impact of typhoon winds and rainfall, further exacerbating the damage to transmission lines.

[0060] The line fault parameters of the target structure under the influence of meteorological characteristic parameters refer to the quantitative data that describe the failure or damage of the target structure under the influence of meteorological disasters. Taking transmission lines or towers as an example, the line fault parameters of these target structures under the influence of meteorological characteristic parameters can be fault location, fault type (such as line break, short circuit, tower collapse, etc.), fault time, fault probability, etc. Among them, the fault location refers to the specific location where the fault occurs; the fault type describes the nature and manifestation of the fault; the fault time records the time when the fault occurs; and the fault probability reflects the possibility of failure of the target structure under the influence of meteorological characteristic parameters.

[0061] Regarding the process of obtaining the line fault parameters of the target structure affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, in some possible implementations, the first meteorological characteristic load and the second meteorological characteristic load of the target structure in the target meteorological disaster can be obtained, wherein the first meteorological characteristic load is the meteorological characteristic critical value of the corresponding target structure failing in the target meteorological disaster, and the second meteorological characteristic load characterizes the specific performance of the meteorological characteristic load of the corresponding target structure under the action of the meteorological characteristic parameters. Based on the first meteorological characteristic load and the second meteorological characteristic load of the target structure, the line fault parameters of the target structure under the action of the meteorological characteristic parameters are determined, and then the line fault parameters of the power grid under the action of the meteorological characteristic parameters are determined. In some possible implementations, the line fault parameters of the target structure under the action of the meteorological characteristic parameters are predetermined and stored in a local or remote server, and the line fault parameters of the target structure under the action of the meteorological characteristic parameters can be directly read from the local or remote server.

[0062] On the one hand, the target power generation equipment involved in this embodiment refers to equipment in the power grid that converts various energy sources into electrical energy. These equipment may be affected by the target meteorological disaster, resulting in changes in output or equipment damage. For example, the target power generation equipment may be wind turbines, photovoltaic generators, and other power generation equipment based on new energy.

[0063] Taking typhoon disasters as an example, the target power generation equipment affected by typhoon disasters in the power grid may be wind turbines and photovoltaic generators. Among them, wind turbines may suffer blade damage and tower collapse under the influence of typhoon strong winds, resulting in reduced output or even equipment damage. Photovoltaic generators may suffer solar panel damage, tilt or collapse of brackets under the influence of typhoon rainfall and strong winds, which will also affect their output.

[0064] The output parameters of the target power generation equipment under the influence of meteorological characteristic parameters refer to the quantitative data describing the output of the target power generation equipment under the influence of the target meteorological disaster. Taking a wind turbine as an example, its corresponding output parameters under the influence of a target meteorological disaster can be the expected output, actual output, etc. The expected output is the expected output of the wind turbine under normal meteorological conditions; the actual output is the actual output of the wind turbine under the current meteorological conditions. Taking a photovoltaic generator set as an example, its corresponding output parameters under the influence of a target meteorological disaster can also be the expected output, actual output, etc.

[0065] Regarding the process of obtaining the output parameters of the target power generation equipment affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, in some possible implementations, the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment affected by the target meteorological disaster in the power grid can be obtained, wherein the first meteorological characteristic threshold is the meteorological characteristic critical value of the corresponding target power generation equipment shutting down in the target meteorological disaster, and the second meteorological characteristic threshold is the meteorological characteristic critical value of the corresponding target power generation equipment damaged in the target meteorological disaster. Then, based on the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment, the output parameters of the target power generation equipment under the action of the meteorological characteristic parameters can be determined. In some possible implementations, in some possible implementations, the output parameters of the target power generation equipment under the action of the meteorological characteristic parameters are predetermined and stored in a local or remote server, and the output parameters of the target power generation equipment under the action of the meteorological characteristic parameters can be directly read from the local or remote server.

[0066] S103, determining the target flow parameters of the power grid based on the line fault parameters and the output parameters, determining the target Pareto solution of each objective function in the preset function set based on the target flow parameters and the preset normal constraint algorithm, and performing power dispatch on the power grid according to the target Pareto solution of each objective function.

[0067] Specifically, after obtaining the line fault parameters of the target structure under the action of meteorological characteristic parameters and the output parameters of the target power generation equipment under the action of meteorological characteristic parameters, the line fault parameters and the output parameters can be used to determine the target power flow parameters of the power grid. Among them, the target power flow parameters of the power grid refer to the quantitative data describing the power flow state in the power grid. The target power flow parameters can be parameters such as voltage, current, and power, which reflect the operating state and load distribution of the power grid under the target meteorological disaster.

[0068] Regarding the process of determining the target power flow parameters of the power grid based on the line fault parameters and the output parameters, in some possible implementations, the mapping relationship between the parameter combination and the power flow parameters can be predetermined, and the mapping relationship indicates that a specific parameter combination has a corresponding power flow parameter. The mapping relationship can be obtained based on historical data, simulation experiments or theoretical analysis, and stored and represented in the form of a database, function or machine learning model. Then, based on the line fault parameters and the output parameters, the target parameter combination of the current power grid is determined. Using the target parameter combination and the mapping relationship between the predetermined parameter combination and the power flow parameters, the target power flow parameters of the power grid can be determined.

[0069] For example, a power flow parameter prediction model based on machine learning can be established. During the training process, the power flow parameter prediction model can learn the relationship between the parameter combination and the power flow parameters. Therefore, the power flow parameter prediction model uses the line fault parameters and output parameters as input features and the target power flow parameters as output targets. Of course, in addition to the machine learning-based model, other methods can also be used to determine the mapping relationship between the parameter combination and the power flow parameters, such as simulation calculation based on physical models, estimation based on empirical formulas, etc.

[0070] After the target power flow parameters of the power grid are determined, the target Pareto solution of each target function in the preset function set can be determined based on the target power flow parameters and a preset normal constraint algorithm.

[0071] The preset function set refers to a set of objective functions, which reflect the requirements of power grid resilience from different dimensions. Each objective function in the preset function set represents the goals or standards that the power grid needs to achieve in terms of power supply capacity, economy, and power quality when responding to target meteorological disasters.

[0072] The target Pareto solution of any objective function in the preset function set refers to a set of solutions that can simultaneously meet the requirements of the objective function and other objective functions in the preset function set except the objective function in the corresponding solution space. The target Pareto solution represents a certain degree of balance between the objective function and other objective functions, that is, the performance of the objective function cannot be further improved without sacrificing the performance of other objective functions.

[0073] The preset normal constraint algorithm is an algorithm for solving multi-objective optimization problems. It converts the multi-objective optimization problem into a series of single-objective optimization problems to obtain the target Pareto solution.

[0074] Regarding the process of determining the target Pareto solution of each objective function in the preset function set based on the target flow parameter and the preset normal constraint algorithm, in some possible implementations, the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension can be determined from the preset function set, wherein the first dimension, the second dimension, and the third dimension can respectively reflect the power supply capacity, economy, and power quality of the power grid. Then, based on the target flow parameter, the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension are respectively optimized and solved to obtain the solution set of the preset function set. Among them, the single-objective optimization solution refers to the process of performing separate optimization solutions for each objective function in the preset function set. In the single-objective optimization solution process, for the selected current objective function, the influence of other objective functions other than the current objective function is temporarily ignored, and only the optimization of the current objective function is focused on. By solving the single-objective optimization problem of each objective function, a series of single-objective optimal solutions can be obtained, and these solutions constitute the solution set of the preset function set. That is to say, the solution set of the preset function set refers to the set containing all single-objective optimal solutions. Each point in the solution set of the preset function set represents a single-objective optimal solution. These solutions achieve the optimality in their respective objective functions, but may not be optimal in other objective functions.

[0075] Next, based on the solution set, the first dimension, the second dimension, and the third dimension of the preset function set, the solution space of the preset function set is constructed. The solution space is a multidimensional space, and the performance of different solutions on each objective function can be intuitively determined based on the solution space. Further, the target solution plane is determined in the solution space, and the first target vector and the second target vector are determined in the solution space. These two vectors represent the changes in two important directions in the solution space, and they can be used to guide the scanning process on the target solution plane. Then, the target solution plane is scanned based on the first target vector and the second target vector to obtain the target Pareto solution of each objective function in the preset function set.

[0076] In some possible implementations, the target Pareto solution of each objective function is a screened Pareto solution, and the screening process can be performed based on a Pareto filter and / or crowding distance so that the target Pareto solution of each objective function meets requirements in terms of information content, uniformity of solution set distribution, and actual application needs.

[0077] Furthermore, the power grid is dispatched according to the target Pareto solution of each objective function. Specifically, the power grid includes a variety of controlled units, which can be power generation equipment (such as wind turbines, photovoltaic generators, gas turbines, reactive power compensation devices, etc.) and energy storage equipment (such as mobile energy storage devices, chemical energy storage devices, charging piles, etc.). These controlled units jointly maintain the stable operation and power supply of the power grid.

[0078] The power dispatch of the power grid is based on control parameters. The power dispatch of the power grid refers to adjusting the operating status of these controlled units based on the control parameters to achieve stable operation and power supply optimization of the power grid under specific meteorological disaster conditions. These control parameters may include the output of power generation equipment, the charging and discharging status of energy storage equipment, the transmission power of transmission lines, and the voltage regulation of substations.

[0079] In some possible implementations, the target Pareto solution of each objective function can be directly used as the control parameter of the power grid. In this case, each target Pareto solution contains a set of control parameter values, which can be directly used to adjust the operating status of the controlled units in the power grid. Based on the control parameters, the output of the power generation equipment can be adjusted in real time, the charging and discharging strategy of the energy storage equipment can be optimized, the transmission power of the transmission line can be adjusted, and the voltage of the substation can be adjusted, so as to achieve the optimization of the power grid in multiple dimensions such as power supply capacity, economy and power quality.

[0080] In some possible implementations, the target Pareto solutions of each objective function may also be used to map the control parameters of the power grid. This generally involves a process of mapping the target Pareto solution from the solution space to the control parameter space. For example, the target Pareto solution may be converted into a specific control parameter value by establishing a mapping relationship between the target Pareto solution and the control parameter (such as through a functional relationship, a lookup table, or a machine learning model). Then, based on these control parameter values, the controlled units in the power grid are adjusted in real time to achieve optimized operation of the power grid.

[0081] Regardless of which method is used, power dispatching based on the target Pareto solution of each objective function can effectively balance the requirements of the power grid in multiple dimensions such as power supply capacity, economy and power quality, improve the resilience and disaster resistance of the power grid under meteorological disasters, and ensure the stability and reliability of power supply.

[0082] In this embodiment, the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located, the line fault parameters of the target structure affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, and the output parameters of the target power generation equipment affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters are first obtained. It can be seen that the above parameters can characterize the impact of the target meteorological disaster on the power grid, and the target power flow parameters of the power grid are determined based on the above parameters to describe the power flow state in the power grid. Furthermore, the target Pareto solution of each objective function in the preset function set is determined based on the target power flow parameters and the preset normal constraint algorithm, so that the target Pareto solution can effectively balance the requirements of each objective function. Finally, the power grid is dispatched according to the target Pareto solution of each objective function, which can optimize the allocation and utilization of power resources in the power grid and improve the resilience and disaster resistance of the power grid under meteorological disasters.

[0083] In one embodiment, based on Figure 1 The content of the embodiment shown in the figure proposes a two-layer control architecture for the distribution network. Figure 2 , Figure 2 An example schematic diagram of a two-layer control architecture of a distribution network provided in one embodiment of the present application, wherein the two-layer control architecture of the distribution network consists of a basic operation layer and an optimization scheduling layer.

[0084] The basic operation layer includes a variety of controlled units in the distribution network, which can be power generation equipment (such as wind turbines, photovoltaic generators, gas turbines, reactive power compensation devices, etc.) and energy storage equipment (such as mobile energy storage devices, chemical energy storage devices, charging piles, etc.). These controlled units jointly maintain the stable operation of the power grid and power supply. In addition, the basic operation layer can also pass necessary decision variables to the optimization scheduling layer.

[0085] The optimization operation layer is mainly responsible for realizing the control of the basic operation layer. Among them, the optimization operation layer defines the objective function corresponding to the first dimension (power supply capacity dimension), the objective function corresponding to the second dimension (economic dimension), and the objective function corresponding to the third dimension (power quality dimension). The objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension together constitute a preset function set.

[0086] The optimization operation layer can construct a meteorological model of the target meteorological disaster, and obtain the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located based on the meteorological model of the target meteorological disaster; obtain the line fault parameters of the target structure affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, and obtain the output parameters of the target power generation equipment affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters; determine the target flow parameters of the power grid based on the line fault parameters and the output parameters, determine the target Pareto solution of each objective function in the preset function set based on the target flow parameters and the preset normal constraint algorithm, and perform power dispatch on the power grid according to the target Pareto solution of each objective function. Optionally, the optimization operation layer can determine the target flow parameters based on the line fault parameters, the output parameters, and the decision variables transmitted by the basic operation layer.

[0087] Regarding the process of power dispatching of the power grid according to the target Pareto solution of each objective function, the corresponding power dispatching instructions can be transmitted from the optimization operation layer to the basic operation layer, and the operating status of each controlled unit in the basic operation layer is determined based on the power dispatching instructions, thereby realizing power dispatching of the distribution network.

[0088] In this embodiment, based on the two-layer control architecture of the distribution network, the source (power generation equipment), network (distribution network and transmission network), load (power load), and storage (energy storage system) of the distribution network can be coordinated to enhance the distribution network's ability to respond to meteorological disasters. At the same time, based on the two-layer control architecture, the multi-dimensional resilience of the distribution network in terms of power supply capacity, economy, and power quality under meteorological disasters is improved.

[0089] In one embodiment, for the controlled units of the basic operation layer, some methods for acquiring relevant parameters of the controlled units are provided below.

[0090] Gas turbines generate electricity by burning fossil fuels. The operating principle is relatively simple and efficient, with advantages such as high energy conversion efficiency, high ramp rate, and large power capacity. Its modeling includes ramp capacity limitations and maximum and minimum output power limitations:

[0091]

[0092] P i min ≤P i,t ≤P i max

[0093] Q i,t =F i,t P i,t

[0094] Among them, P max P is the maximum power output of the generator; minThe minimum power output of the generator; is the maximum climbing speed of the generator; F is the power factor.

[0095] The reactive power compensation model includes group switching capacitors and static reactive power compensation devices. Group switching capacitors have the advantages of low operation and maintenance costs and long life, and can provide graded reactive power compensation:

[0096] Q i,t+1 CB =Q i,t CB +λ i,t Q i CB

[0097] Q i min ≤Q i,t CB ≤Q i max

[0098]

[0099] Among them, Q CB To generate reactive power, the upper and lower limits are Q min , Q max , λ is the number of switching gears at a single time, and D is the maximum number of switching times. The static reactive power compensation device has the advantage of convenient scheduling and can provide continuous reactive power compensation within a certain range: Q i min ≤Q i,t SVC ≤Q i max

[0100] Among them, Q SVC is the reactive power generated by the compensator, and its upper and lower limits are Q min , Q max .

[0101] The classic energy storage model mainly includes charging and discharging state constraints, charging and discharging power constraints and capacity constraints:

[0102] α i,t ch +α i,t dis ≤1

[0103]

[0104]

[0105] E i max *SOC min≤E i,t ≤E i max *SOC max

[0106] Among them, α ch , α dis is a 0-1 variable indicating the charge and discharge state; P ch , P dis is the energy storage charging and discharging power, and its maximum values ​​are is the energy storage charging and discharging efficiency; E is the energy storage capacity, and its maximum value is E max To ensure the safe operation of the battery, set the upper and lower limits of the battery power percentage SOC max , SOC min .

[0107] The mobile energy storage model is similar to the classic energy storage:

[0108]

[0109]

[0110]

[0111]

[0112] In addition, it also includes the mobile energy storage's own moving distance limit:

[0113]

[0114] The mobile energy storage distance is mapped as:

[0115] l i,t mess =|x i,t+1 -x i,t |+|y i,t+1 -y i,t |

[0116] Further linearization is:

[0117]

[0118] Among them, α ch,mess , α dis,mess is a 0-1 variable indicating the charge and discharge state; P ch,mess , P dis,mess is the charging and discharging power of mobile energy storage, and its maximum values ​​are E is the energy storage charging and discharging efficiency; mess is the mobile energy storage capacity, and its maximum value is E maxTo ensure the safe operation of the battery, set the upper and lower limits of the battery power percentage SOC max , SOC min ; l mess is the moving distance; v mess is the moving speed; Δt is the time span between two sampling points; x, y are the mobile energy storage location information; C max is the upper limit of the total fuel for mobile energy storage; c v is the fuel consumption of mobile energy storage per unit mileage; m is the additional fuel loss coefficient; N is the number of mobile energy storage.

[0119] See also Figure 3 , Figure 3 A schematic diagram of a process for obtaining meteorological characteristic parameters provided in an embodiment of the present application is as follows: Figure 3 As shown, the method of the embodiment of the present application may include the following steps S201-S202, and steps S201-S202 may be used as Figure 1 The illustrated embodiment shows a detailed step of "constructing a meteorological model of a target meteorological disaster" in step S101.

[0120] S201, obtaining meteorological data of the area where the power grid is located, and extracting original meteorological parameters of the target meteorological disaster from the meteorological data;

[0121] S202, constructing a meteorological model of the target meteorological disaster based on the original meteorological parameters.

[0122] Specifically, the meteorological data involved in this embodiment refers to various meteorological data including temperature, humidity, wind speed, wind direction, air pressure, rainfall, etc. It can be understood that the meteorological data of the area where the power grid is located refers to the above-mentioned meteorological data collected within the geographical location of the power grid.

[0123] Regarding the process of obtaining meteorological data for the area where the power grid is located, in some possible implementations, it can be obtained online, such as by visiting the website of the Meteorological Bureau or using the meteorological data API to capture data from the meteorological monitoring station in real time; in some possible implementations, it can also be obtained locally, and the meteorological data for the area where the power grid is located is pre-stored in a local database or file, and can be directly read and used locally.

[0124] Furthermore, the original meteorological parameters of the target meteorological disaster are extracted from the meteorological data. The original meteorological parameters of the target meteorological disaster refer to the basic parameters that can characterize the characteristics of the meteorological disaster, such as the central air pressure, maximum wind speed, and wind circle radius of the typhoon. This process can be specifically performed by screening out parameters related to the target meteorological disaster from the acquired meteorological data according to the type and characteristics of the target meteorological disaster. For example, for a typhoon disaster, the maximum wind speed value can be extracted from the wind speed data, and the minimum air pressure value can be extracted from the air pressure data.

[0125] Furthermore, a meteorological model of the target meteorological disaster can be constructed based on the original meteorological parameters. The meteorological model of the target meteorological disaster refers to a model that describes the characteristics and evolution laws of meteorological disasters through mathematical equations or algorithms. For example, a typhoon model can include a wind field model, an air pressure model, etc. This process can be specifically manifested as establishing a corresponding mathematical model based on the relationship between the original meteorological parameters and meteorological principles, such as establishing a typhoon wind field model based on the relationship between wind speed and air pressure, or establishing a typhoon evolution model based on the law of wind speed changes over time.

[0126] Regarding the process of building a meteorological model for a target meteorological disaster, in some possible implementations, the target meteorological disaster may be a typhoon disaster. Volatility factors may be introduced, such as considering the fluctuation of wind speed at different heights and at different times, so that the model can more accurately describe the dynamic changes of the typhoon wind field; micro-topography attenuation factors may also be introduced, such as considering the impact of terrain on wind speed, so that the model can more accurately predict the wind speed distribution of a typhoon in complex terrain.

[0127] In this embodiment, after the meteorological model of the target meteorological disaster is constructed, the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located can be obtained based on the meteorological model of the target meteorological disaster. The process is specifically manifested in using the meteorological model to simulate and calculate the area where the power grid is located, and obtain the meteorological parameters of the area under the influence of the target meteorological disaster, such as wind speed, wind direction, rainfall, etc., so as to determine the meteorological characteristic parameters of the area where the power grid is located, and provide a basis for subsequent power dispatching.

[0128] In one embodiment, Figure 3 Step S202 of the illustrated embodiment is further refined and may include the following steps:

[0129] Determine the micro-topography attenuation parameters of the typhoon disaster-related areas;

[0130] A meteorological model of the target meteorological disaster is constructed based on the original meteorological parameters and micro-topography attenuation parameters.

[0131] Specifically, the target meteorological disaster may be a typhoon disaster. The meteorological data of the area where the power grid is located may include temperature, humidity, wind speed, wind direction, air pressure, rainfall, etc. The original meteorological parameters of the target meteorological disaster may include typhoon center pressure, maximum wind speed, wind circle radius, rainfall intensity, etc.

[0132] In some possible implementations, the meteorological model of the target meteorological disaster is expressed as:

[0133]

[0134] Among them, the original meteorological parameters include r c 、v m 、rm , b, α, z0, χ, s, micro-topography attenuation parameters include u, ΔH(t), ΔX;

[0135] v(r,θ,z,t) is the wind speed of any target point in the wind field of the typhoon disaster, r is the distance between the center of the typhoon disaster and the target point, θ is the angle between the target point and the x-axis of the reference coordinate system, z is the vertical height of the target point, r c is the maximum wind circle radius of the typhoon disaster, v m is the maximum wind speed in the center of the typhoon disaster, r m is the maximum wind circle radius of the typhoon disaster, R is the sum of r and r m where t is the ratio of t to h, b is the outer wind profile information of the typhoon disaster, α is the vertical attenuation coefficient of the typhoon disaster, z0 is the horizontal reference height of the target point, χ is the wind circle coefficient of the typhoon disaster, s is the asymmetry factor of the typhoon disaster, u is the first wind speed attenuation coefficient caused by the building density in the axial direction, ΔH(t) is the second wind speed attenuation coefficient caused by the change of the urban underlying surface on the horizontal wind speed of the moving time-varying typhoon, ΔX is the overall attenuation contribution rate of the wind field caused by urbanization, and t is the time period calculated with the typhoon landing as the initial moment.

[0136] In some possible implementations, the derivation process of the meteorological model of the above target meteorological disaster is as follows:

[0137] The initial meteorological model of the target meteorological disaster constructed based on the original meteorological parameters can be expressed by the following formula:

[0138]

[0139] Among them, the original meteorological parameters include r c 、v m 、r m , b, α, z0, χ, s. v(r, θ, z) is the wind speed of any target point in the wind field of the typhoon disaster, r is the distance between the center of the typhoon disaster and the target point, θ is the angle between the target point and the x-axis of the reference coordinate system, z is the vertical height of the target point, r c is the maximum wind circle radius of the typhoon disaster, v m is the maximum wind speed in the center of the typhoon disaster, r m is the maximum wind circle radius of the typhoon disaster, R is the sum of r and r m The ratio between them, b is the outer wind profile information of the typhoon disaster, α is the vertical attenuation coefficient of the typhoon disaster, z0 is the horizontal reference height of the target point, χ is the wind circle coefficient of the typhoon disaster, and s is the asymmetry factor of the typhoon disaster.

[0140] For the maximum wind speed v in the center of a typhoon disaster m , using extreme value type III distribution, namely Weibull distribution description:

[0141]

[0142] Among them, a is the scale parameter, γ is the shape parameter, and b is the location parameter.

[0143] Furthermore, considering that typhoon disasters evolve over time, the typhoon information changes are updated using linear interpolation, that is, the typhoon position information and wind circle information are shown as follows:

[0144]

[0145] Among them, x t ,y t They represent the location information of the typhoon in the network coordinates at time t; x0 and y0 represent the location information of the typhoon in the network coordinates at the initial time; Respectively represent the wind circle information of the typhoon at time t; They respectively represent the position information of the typhoon in the network coordinates at the initial moment; T is the observation period.

[0146] In addition, considering that different building densities will have different attenuation capabilities for typhoon wind speed, the wind speed will be attenuated due to friction in the urban distribution network area. Therefore, micro-topography attenuation parameters are introduced, which include u, ΔH(t), and ΔX.

[0147] u is the first wind speed attenuation coefficient caused by the building density in the axial direction, and the calculation formula is as follows:

[0148]

[0149] ΔH(t) is the second wind speed attenuation coefficient caused by the change of the urban underlying surface on the horizontal wind speed of the moving time-varying typhoon, and the calculation formula is as follows:

[0150] ΔH(t)=0.75ΔH0-0.508[1+sinθ]t

[0151] ΔX is the overall attenuation contribution rate of the wind field caused by urbanization, and the calculation formula is as follows:

[0152]

[0153] In the above calculation formulas of u, ΔH(t), and ΔX, t is the time period calculated from the typhoon landing as the initial moment; H g is the gradient wind height corresponding to the actual landform type; α is the wind profile coefficient; ΔH0 is the initial attenuation; θ is the angle between the typhoon moving path and the horizontal axis; K is the von Karman coefficient; Z0 is the roughness length; X u Detecting meteorological trends for urban areas; rDetect weather trends for weather stations.

[0154] Based on the above formula, the initial meteorological model of the target meteorological disaster is corrected to obtain the meteorological model of the target meteorological disaster, as follows:

[0155]

[0156] In this embodiment, by obtaining the meteorological data of the area where the power grid is located, the original meteorological parameters of the target meteorological disaster are extracted from the meteorological data, and then a meteorological model of the target meteorological disaster is constructed based on the original meteorological parameters, and finally the meteorological model is used to obtain the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located. These meteorological characteristic parameters can accurately reflect the specific impact of the target meteorological disaster on the area where the power grid is located, and provide key basic data support for the subsequent power grid power dispatching and disaster response measures. Through this process, the potential threat of meteorological disasters to the power grid can be more accurately assessed, thereby effectively improving the response capability and resilience of the power grid in the face of meteorological disasters.

[0157] See also Figure 4 , Figure 4 A schematic diagram of a process for obtaining line fault parameters provided by an embodiment of the present application is as follows: Figure 4 As shown, the method of the embodiment of the present application may include the following steps S301-S304, and steps S301-S304 may be used as Figure 1 The illustrated embodiment includes step S101 of “obtaining line fault parameters of target structures in the power grid affected by target meteorological disasters under the action of meteorological characteristic parameters”.

[0158] S301, obtaining a first meteorological characteristic load of a target structure in a power grid affected by a target meteorological disaster in the target meteorological disaster, where the first meteorological characteristic load is a meteorological characteristic critical value at which a corresponding target structure fails in the target meteorological disaster;

[0159] S302, determining a second meteorological characteristic load of the target structure in the target meteorological disaster based on the meteorological characteristic parameter;

[0160] S303, determining a line fault parameter of the target structure under the action of the meteorological characteristic parameters based on the first meteorological characteristic load and the second meteorological characteristic load of the target structure;

[0161] S304, determining the line fault parameters of the power grid under the action of the meteorological characteristic parameters based on the line fault parameters of the target structure under the action of the meteorological characteristic parameters.

[0162] Specifically, the first meteorological characteristic load of the target structure in the target meteorological disaster involved in this embodiment refers to the critical meteorological condition for the target structure to fail in the target meteorological disaster. For example, for a transmission line, its first meteorological characteristic load may be the maximum wind speed value that causes the line to break; for a tower, it may be the maximum wind pressure value that causes the tower to collapse. There may be many types of target structures, such as transmission lines (or transmission lines at a certain level in a power grid), towers, substation facilities, etc. Different types of target structures may have different first meteorological characteristic loads in the target meteorological disaster. For example, a transmission line may pay more attention to wind speed, while a tower may pay more attention to wind pressure and rainfall.

[0163] Regarding the process of obtaining the first meteorological characteristic load of the target structure in the power grid affected by the target meteorological disaster in the target meteorological disaster, in some possible implementation methods, it can be obtained through historical data analysis, such as collecting the failure records of the power grid in similar meteorological disasters in the past, and statistically analyzing the failure conditions of different target structures under different meteorological conditions, so as to determine its first meteorological characteristic load; it can also be obtained through experimental simulation or numerical simulation, such as simulating the effects of meteorological disasters such as typhoons on target structures in the laboratory, or using computer simulation software to simulate and calculate the stress conditions of the target structure under different meteorological conditions, and then determine its first meteorological characteristic load.

[0164] Furthermore, the second meteorological characteristic load of the target structure in the target meteorological disaster can be determined based on the meteorological characteristic parameters. The second meteorological characteristic load of the target structure in the target meteorological disaster refers to the meteorological load actually borne by the target structure under the current meteorological conditions, such as the wind speed and wind pressure values ​​actually borne by the transmission line in a typhoon disaster.

[0165] After determining the first meteorological characteristic load and the second meteorological characteristic load of the target structure, the line fault parameters of the power grid under the meteorological characteristic parameters can be determined based on the line fault parameters of the target structure under the meteorological characteristic parameters. The line fault parameters refer to parameters that describe the probability, location, type, and other information of line failure under the influence of meteorological disasters. Regarding this process, in some possible implementations, a line fault model can be established, and the first meteorological characteristic load and the second meteorological characteristic load can be used as inputs. The line fault parameters are obtained through model calculations, such as using a probability statistical model to calculate the failure probability of the line under different meteorological conditions, or using a mechanical model to calculate the stress of the line under the action of wind load, so as to determine its fault location and type, etc.

[0166] Finally, based on the line fault parameters of the target structure under the action of meteorological characteristic parameters, the line fault parameters of the power grid under the action of meteorological characteristic parameters can be determined. This process can be manifested as directly taking the line fault parameters of the target structure as the line fault parameters of the power grid. For example, when there is only one transmission line in the power grid, its line fault parameters can be directly used as the line fault parameters of the entire power grid; or some processing can be performed, such as comprehensive analysis and integration of the line fault parameters of multiple target structures to obtain the line fault parameters of the entire power grid. For example, when there are multiple target structures such as transmission lines and towers in the power grid, their fault conditions can be comprehensively considered, and the line fault probability, fault location distribution and other parameters of the entire power grid can be calculated by weighted average, probability superposition and other methods.

[0167] In a possible implementation of this embodiment, the target meteorological disaster may be a typhoon disaster. For typhoon disasters, the target structures affected by them in the power grid may include transmission lines, towers, substation facilities, etc. The first meteorological characteristic load may be the maximum wind speed value that causes line disconnection, the maximum wind pressure value that causes tower collapse, etc. The second meteorological characteristic load is the stress of these target structures under meteorological conditions such as the actual wind speed and wind pressure of the typhoon. By obtaining and analyzing these parameters, the line fault parameters of the power grid under typhoon disasters can be determined, providing an important basis for power dispatching.

[0168] Specifically, in a feasible implementation of this embodiment, the first meteorological characteristic load of the target structure affected by the target meteorological disaster in the target meteorological disaster can be obtained from relevant design standards, such as the line wind load W L Relevant design standards:

[0169] W L =0.5αμ s dL W ρv 2 sin 2 θ

[0170] Where, α is the wind load span coefficient; d is the outer diameter of the conductor; μ s is the wind load shape coefficient; L w is the horizontal span; W0 is the standard value of the benchmark wind pressure; ρ is the air density; v is the wind speed; θ is the angle between the wind speed and the line direction.

[0171] From this, we can get the maximum wind speed that the line can withstand, which is also the first meteorological characteristic load. When the transmission line or tower is in a typhoon wind field, a fault will occur if the wind speed exceeds the maximum wind load. For a line in the distribution network, a fault in itself or the surrounding towers will cause a disconnection. However, due to the uncertainty of the design and service life of the line, the maximum wind speed it can bear is not fixed. Its actual value is the second meteorological characteristic load of the target structure in the target meteorological disaster, which can be approximated as a normal distribution, specifically satisfying the following distribution:

[0172]

[0173] Among them, meteorological characteristic parameters include μ v , δ v , v. v is the wind speed at the location of the target structure, μ v is the mean of the design wind speed of the transmission line corresponding to the target structure, δ v is the standard deviation of wind strength. On this basis, based on the meteorological characteristic parameters, the second meteorological characteristic load of the target structure in the target meteorological disaster is determined, that is, according to the meteorological model of the target meteorological disaster constructed based on the original meteorological parameters, the wind speed v at the location of the target structure in the wind field is calculated with the typhoon center as the coordinate origin. Then the failure rate of the transmission line, that is, the line failure parameter of the target structure under the action of the meteorological characteristic parameters, can be expressed as:

[0174]

[0175] Where P is the line failure rate corresponding to the target structure, v is the wind speed at the location of the target structure, μ v is the mean of the design wind speed of the transmission line corresponding to the target structure, δ v is the standard deviation of wind resistance;

[0176] The line fault parameters of the power grid under the influence of meteorological characteristic parameters include P k , the calculation formula of the line fault parameters of the power grid under the influence of meteorological characteristic parameters is as follows:

[0177]

[0178] Among them, P k is the line failure rate corresponding to the k-th level line in the power grid.

[0179] In this embodiment, the first meteorological characteristic load and the second meteorological characteristic load of the target structure affected by the target meteorological disaster in the power grid are obtained, and then the line fault parameters of the target structure under the action of the meteorological characteristic parameters are determined based on these two loads, and finally the line fault parameters of the power grid under the action of the meteorological characteristic parameters are determined. This process can accurately evaluate the impact of the target meteorological disaster on different target structures in the power grid, and provide key fault data support for the power dispatching of the power grid and the formulation of disaster response measures. Through this method, the potential threat of meteorological disasters to the power grid can be more effectively predicted and responded to, thereby improving the resilience and disaster resistance of the power grid in the face of meteorological disasters.

[0180] See also Figure 5 , Figure 5 A schematic diagram of a process for obtaining output parameters provided in an embodiment of the present application is as follows: Figure 5 As shown, the method of the embodiment of the present application may include the following steps S401-S402, and steps S401-S402 may be used as Figure 1 The illustrated embodiment includes step S101 of “obtaining output parameters of target power generation equipment in the power grid affected by target meteorological disasters under the action of meteorological characteristic parameters”, which is a detailed step.

[0181] S401, obtaining a first meteorological characteristic threshold and a second meteorological characteristic threshold of a target power generation equipment affected by a target meteorological disaster in a power grid, wherein the first meteorological characteristic threshold is a meteorological characteristic critical value at which the corresponding target power generation equipment is shut down in a target meteorological disaster, and the second meteorological characteristic threshold is a meteorological characteristic critical value at which the corresponding target power generation equipment is damaged in a target meteorological disaster;

[0182] S402: Determine output parameters of the target power generation equipment under the action of the meteorological characteristic parameters based on the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment.

[0183] Specifically, the first meteorological characteristic threshold of the target power generation equipment involved in the target meteorological disaster in this embodiment refers to the critical meteorological condition for the shutdown of the target power generation equipment in the target meteorological disaster. For example, for a wind turbine, its first meteorological characteristic threshold may be the maximum wind speed value that causes the wind turbine to shut down; for a photovoltaic generator set, it may be the rainfall intensity value that causes the photovoltaic panel to stop generating electricity. The second meteorological characteristic threshold is the critical meteorological condition for the damage of the target power generation equipment in the target meteorological disaster, such as the maximum wind speed value for the damage of the wind turbine blades, the maximum rainfall value for the damage of the photovoltaic generator set panels, etc.

[0184] Regarding the process of obtaining the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment affected by the target meteorological disaster in the power grid, in some possible implementation methods, it can be obtained by referring to the design standards and specifications of the relevant equipment. For example, the design specifications of wind turbines will clearly define their shutdown and damage thresholds at different wind speeds; it can also be obtained through historical data analysis, such as statistical analysis of the shutdown and damage of power generation equipment in similar meteorological disasters in the past, so as to determine its first meteorological characteristic threshold and the second meteorological characteristic threshold.

[0185] Furthermore, the output parameters of the target power generation equipment under the action of the meteorological characteristic parameters can be determined based on the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment. The output parameter refers to the actual amount of electric energy output generated by the power generation equipment under specific meteorological conditions, such as the power generation of a wind turbine at different wind speeds, the power generation of a photovoltaic power generation group at different light intensities, etc. This process can be specifically manifested as comparing the current meteorological characteristic parameters, such as wind speed, wind direction, rainfall, etc., with the first meteorological characteristic threshold and the second meteorological characteristic threshold, to determine whether the power generation equipment is in normal operation, shutdown or damaged state, and then determine its output parameter. For example, when the wind speed is lower than the first meteorological characteristic threshold, the wind turbine operates normally, and its output parameter is the power generation calculated based on the wind speed; when the wind speed exceeds the second meteorological characteristic threshold, the wind turbine is damaged, and its output parameter is zero.

[0186] In a possible implementation of this embodiment, the target meteorological disaster may be a typhoon disaster. For typhoon disasters, the target power generation equipment affected by the disaster in the power grid may include wind turbines and photovoltaic power generation groups. The first meteorological characteristic threshold of the wind turbine may be its shutdown wind speed value in a typhoon, and the second meteorological characteristic threshold may be the maximum wind speed value at which the blades are damaged; the first meteorological characteristic threshold of the photovoltaic power generation group may be the rainfall intensity value at which the photovoltaic panel stops generating electricity, and the second meteorological characteristic threshold may be the maximum rainfall value at which the solar panel is damaged. By obtaining these thresholds and combining them with meteorological characteristic parameters such as wind speed and rainfall in a typhoon, the output parameters of the target power generation equipment under typhoon disasters can be accurately determined, providing an important basis for power dispatching and disaster response in the power grid.

[0187] Specifically, in this embodiment, taking the wind turbine in the power grid as an example, following the principle of protecting equipment, when the wind speed in the wind farm where the equipment is located is relatively high, it should be safely shut down to avoid permanent damage. When the wind speed in the wind farm is too high, it will cause permanent damage to the shut down equipment. Under normal circumstances, the output of the wind turbine is:

[0188]

[0189] in, is the expected output, v pis the protective shutdown wind speed, that is, the first meteorological characteristic threshold, which is the meteorological characteristic critical value of the corresponding target power generation equipment shutting down in the target meteorological disaster, and v is the wind speed in the wind farm.

[0190] After the protective shutdown, if the wind speed does not exceed the second meteorological characteristic threshold (permanent damage wind speed), it can be put into use again after the wind speed decays. If it exceeds the permanent damage wind speed, it cannot be used again. Its output is:

[0191]

[0192] Among them, v b is the critical damaging wind speed, i.e., the critical value of the meteorological characteristics of the target power generation equipment damaged in the target meteorological disaster, v h is the maximum wind speed in history. It should be understood that the amount of wind power generated is related to the current wind speed v, and the mathematical model of its normal power generation adopts Weibull distribution:

[0193]

[0194] Among them, c and k are the shape parameter and scale parameter of Weibull distribution, which are statistical parameters.

[0195] In summary, based on the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment, the output parameter of the target power generation equipment under the action of the meteorological characteristic parameters is determined.

[0196] Taking typhoon disaster as an example, the target power generation equipment includes wind turbines. The wind turbine output model is obtained by considering the protection of wind turbines and the destructiveness of typhoons. The calculation formula of the output parameters of the target power generation equipment under the action of meteorological characteristic parameters is as follows:

[0197]

[0198] Among them, t is the time, is the maximum output of the wind turbine at time t under typhoon disaster. The coordinates of the wind turbine in the power grid are (x, y). is the maximum wind speed that the wind turbine can bear, is the cut-out wind speed of the wind turbine, The real-time wind speed at the location of the wind turbine; is the maximum wind speed experienced by the wind turbine up to time t; c is the wind energy utilization coefficient, and k is the exponential decay rate of the wind turbine.

[0199] Since the damage to the equipment is irreversible and instantaneous, a step function is used to judge the damage of the wind turbine. There is a certain delay in the shutdown and startup of the wind turbine, and after being cut off, it can be put into operation again when the wind speed returns to the safe operating value. Therefore, a continuous hyperbolic sine activation function is used. When the wind speed is greater than the protection wind speed, the output of the wind turbine is 0.

[0200] Specifically, the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located are obtained based on the meteorological model of the target meteorological disaster established previously, and the line fault parameters and output parameters of the power grid under the action of the meteorological characteristic parameters are determined based on the line fault parameters of the target structure. The dynamic disaster evolution algorithm of the distribution network under typhoon disaster can be obtained, as follows: Figure 6 As shown, Figure 6 The demonstration shows a complete parameterized typhoon spatiotemporal evolution distribution network disaster dynamic update calculation process. First, the system samples the meteorological information of the current node, and then calculates the wind speed and wind direction angle of each line in the distribution network at that moment. By integrating meteorological and geographic information, a dynamic wind field model is generated, and based on this, the wind speed on the line and the wind speed borne by the unit are calculated. Then, the distribution line failure probability function is used to calculate the failure rate of each line, and these data are recorded as the initial failure state matrix.

[0201] Subsequently, the algorithm will calculate the cascading failure rate of the line based on the topological structure of the line, and use this information to update the fault state matrix. In addition, the system will evaluate the design parameters such as wind speed that new energy units such as wind turbines and photovoltaic panels are subjected to based on wind farm information, and compare them with their bearing capacity, so as to calculate the disaster situation of new energy units and lines. In this step, the system compares the real-time wind speed with the protective wind speed, destructive wind speed and historical maximum wind speed of the equipment to determine the output of the unit: if the wind speed exceeds the protective wind speed but does not reach the destructive wind speed, the unit will stop output; if the wind speed exceeds the destructive wind speed, the unit will not only stop output, but also update its historical maximum wind speed record; if the wind speed is lower than the protective wind speed, and the historical maximum wind speed does not exceed the protective wind speed, the wind turbine will continue to output normally.

[0202] After completing the above calculations, the system will update the meteorological and geographic information. If the disaster has not ended, the algorithm will return to the step of meteorological information sampling and continue the next round of calculations to achieve real-time updates of the distribution network's disaster situation and distribution network topology. This dynamic process ensures the resilience and adaptability of the distribution network under typhoon disasters, enabling it to better cope with the challenges of extreme weather events.

[0203] In this embodiment, the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment affected by the target meteorological disaster in the power grid are obtained, and then the output parameters of the target power generation equipment under the action of the meteorological characteristic parameters are determined based on these two thresholds. This process can accurately assess the impact of the target meteorological disaster on the power generation equipment, and provide key power generation data support for the power dispatching of the power grid and the formulation of disaster response measures. Through this method, the potential threat of meteorological disasters to the power generation capacity of the power grid can be more effectively predicted and responded to, thereby improving the resilience and disaster resistance of the power grid in the face of meteorological disasters.

[0204] See also Figure 7 , Figure 7 A schematic diagram of a process for determining target power flow parameters is provided for one embodiment of the present application, such as Figure 7 As shown, the method of the embodiment of the present application may include the following steps S501-S502, and steps S501-S502 may be used as Figure 1 The illustrated embodiment is a detailed step of "determining the target power flow parameters of the power grid based on the line fault parameters and the output parameters" in step S103.

[0205] S501, determining a target parameter combination based on a line fault parameter and an output parameter;

[0206] S502, determining a target power flow parameter of the power grid based on the target parameter combination and a mapping relationship between the parameter combination and the power flow parameter.

[0207] Specifically, based on the line fault parameters of the target structure affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, and the output parameters of the target power generation equipment affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters, the target parameter combination can be determined. It can be understood that the target parameter combination represents the impact of the target meteorological disaster on the line fault and output of the power grid.

[0208] Furthermore, based on the target parameter combination and the mapping relationship between the parameter combination and the power flow parameter, the target power flow parameter of the power grid is determined. The mapping relationship can be obtained based on historical data, simulation experiments or theoretical analysis, and stored and represented in the form of a database, function or machine learning model.

[0209] For example, the mapping relationship between the parameter combination and the power flow parameters can be represented based on a database, and a query statement can be constructed based on the target parameter combination, that is, the target power flow parameters of the power grid can be queried from the corresponding database through the query statement. For another example, the mapping relationship between the parameter combination and the power flow parameters can be represented based on a function, and the target parameter combination is substituted into the corresponding function to calculate the target power flow parameters of the power grid. For another example, the mapping relationship between the parameter combination and the power flow parameters can be represented based on a machine learning model, and the target parameter combination is input into the corresponding machine learning model to obtain the target power flow parameters of the power grid output by the machine learning model.

[0210] In some possible implementations, the mapping relationship between the parameter combination and the power flow parameter can be specifically expressed as a power flow model (a mathematical model based on a function), which is a branch power flow model based on second-order cone relaxation. The target power flow model is expressed as:

[0211]

[0212]

[0213]

[0214] v j =v i -2(R ij P ij +X ij Q ij )+(R ij 2 +X ij 2 )i ij

[0215] Among them, G j , B j Respectively represent the jth equality constraint and inequality constraint; P ij , Q ij They represent the active power and reactive power flowing from node i to node j in the power grid respectively; P j , Q j They represent the active power and reactive power injected into the grid at node j respectively; R ij , X ij Respectively represent the resistance and impedance of the branch between node i and node j in the power grid; i ij is the square of the current amplitude on the branch between node i and node j in the power grid; v ij is the square of the voltage amplitude on the branch between node i and node j in the power grid; v j is the square of the voltage amplitude at node j in the power grid.

[0216] In this embodiment, by combining the line fault parameters with the output parameters, the operating status of the power grid under meteorological disasters can be more comprehensively evaluated. First, based on the line fault parameters, it is possible to determine which lines may be damaged by meteorological disasters, thereby affecting the transmission and distribution of electricity. At the same time, considering the output parameters, that is, the actual power generation capacity of the power generation equipment under meteorological disasters, helps to understand the power supply potential and limitations of the power grid. Combining the two to form a target parameter combination can more accurately reflect the actual operating status of the power grid under specific meteorological conditions. Furthermore, using the mapping relationship between the parameter combination and the flow parameters, the target flow parameters of the power grid, such as voltage, current, power, etc., can be calculated. These parameters are the basis for power grid dispatching and optimization. In this way, not only can the operational risks that the power grid may face be warned in advance, but also a scientific basis can be provided for power dispatching to ensure that the power grid can still maintain a stable and reliable power supply under meteorological disasters. This helps to improve the resilience of the power grid and reduce power outages caused by disasters.

[0217] See also Figure 8 , Figure 8 A schematic diagram of a process for determining a target Pareto solution is provided for one embodiment of the present application, such as Figure 8 As shown, the method of the embodiment of the present application may include the following steps S601-S605, and steps S601-S605 may be used as Figure 1 The illustrated embodiment shows a detailed step of "determining the target Pareto solution of each target function in the preset function set based on the target power flow parameter and the preset normal constraint algorithm" in step S103.

[0218] S601, determining an objective function corresponding to a first dimension, an objective function corresponding to a second dimension, and an objective function corresponding to a third dimension from a preset function set;

[0219] S602, based on the target power flow parameter, performing single-objective optimization solutions for the target function corresponding to the first dimension, the target function corresponding to the second dimension, and the target function corresponding to the third dimension, respectively, to obtain a solution set of a preset function set;

[0220] S603, constructing a solution space of the preset function set based on the solution set of the preset function set, the first dimension, the second dimension, and the third dimension;

[0221] S604, determining a target solution plane in the solution space, and determining a first target vector and a second target vector in the solution space;

[0222] S605, scanning the target solution plane based on the first target vector and the second target vector to obtain a target Pareto solution of each target function in the preset function set.

[0223] Specifically, the first dimension involved in this embodiment refers to the power supply capacity dimension, which reflects the ability of the power grid to stably and reliably supply power to users under specific meteorological disaster conditions. The second dimension refers to the economic dimension, which reflects the economic cost of the power grid in responding to meteorological disasters, including power generation costs, power transportation losses, power purchase costs, etc. The third dimension refers to the power quality dimension, which reflects the power quality indicators such as voltage stability, frequency stability, and power factor of the power grid during the power supply process.

[0224] From the preset function set, the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension can be determined. It can be understood that there is at least one objective function corresponding to the first dimension, at least one objective function corresponding to the second dimension, and at least one objective function corresponding to the third dimension.

[0225] Further, based on the target power flow parameters, the target function corresponding to the first dimension, the target function corresponding to the second dimension, and the target function corresponding to the third dimension are respectively optimized and solved for a single objective, and a solution set of a preset function set is obtained. The process of single objective optimization solution can be specifically expressed as: for each objective function, it is regarded as a unique optimization target, and the influence of other objective functions is temporarily ignored. In the optimization process, the target power flow parameters (such as voltage, current, power, etc.) are used as constraints, and the single objective optimization problem is solved by mathematical programming or optimization algorithms (such as linear programming, nonlinear programming, genetic algorithm, etc.) to obtain the optimal solution of the objective function. The optimal solutions of all single objective optimization problems are combined to form a solution set of a preset function set. Therefore, the solution set of the preset function set can be a set of points in a multidimensional space, where each point represents a single objective optimal solution. These solutions are optimal in their respective objective functions, but may not be optimal in other objective functions. The distribution of points in the solution set reflects the trade-offs and conflicts between different objective functions.

[0226] Furthermore, based on the solution set, the first dimension, the second dimension and the third dimension of the preset function set, the solution space of the preset function set is constructed. The process is specifically manifested as follows: First, according to the definition and properties of each objective function in the preset function set, the number of dimensions of the solution space and the value range of each dimension are determined. Since the preset function set contains objective functions in three dimensions: power supply capacity, economy and power quality, the solution space will be a three-dimensional space, with each dimension corresponding to an objective function. Then, each point in the solution set of the preset function set (i.e., each single-objective optimal solution) is mapped to this three-dimensional space to form a point set in the solution space. The distribution of these points in the solution space reflects the trade-offs and conflicts between different objective functions, and provides a basis for solving the subsequent objective Pareto solution.

[0227] Further, a target solution plane is determined in the solution space, and a first target vector and a second target vector are determined in the solution space. The process is specifically as follows: first, a target solution plane is determined in the solution space according to actual needs, and the target solution plane represents a state of reaching a certain balance or compromise between certain objective functions. Then, a first target vector and a second target vector are determined in the solution space. These two vectors respectively represent changes in two important directions in the solution space, and can be used to guide the scanning process on the target solution plane.

[0228] Furthermore, the target solution plane is scanned based on the first target vector and the second target vector to obtain the target Pareto solution of each target function in the preset function set. The process is specifically manifested as follows: first, starting from an initial point on the target solution plane, scanning along the direction of the first target vector, calculating the target function value of each point on the scanning path, and judging whether these points meet the Pareto optimality condition. If not, continue scanning in the direction of the first target vector; if satisfied, add the point to the target Pareto solution set and scan along the direction of the second target vector. Repeat this process until all possible points on the target solution plane are traversed. During the scanning process, the normal constraint algorithm can be used to accelerate the solution process, that is, by converting the multi-objective optimization problem into a series of single-objective optimization problems for solution, the target Pareto solution is obtained. Finally, the target Pareto solution set of each target function is obtained, and these solution sets achieve a balance between different target functions, providing a variety of possible power dispatching schemes for the power grid.

[0229] In some possible implementations, the objective function of the first dimension may include at least one of a first objective function, a second objective function, a third objective function, and a fourth objective function:

[0230] The first objective function is defined based on area and can be expressed as:

[0231]

[0232] Among them, P N (t) is the system load / system operation function of the power grid during normal operation, and P(t) is the system load / system operation function of the power grid after a disaster occurs. To understand the practical significance of the above area-based resilience optimization objective function, please refer to Fig. 9 , Fig. 9An example schematic diagram of the multi-stage impact of typhoon disasters on the power grid provided for an embodiment of the present application. Among them, the impact of typhoon disasters on the power grid can include four stages: ① Pre-disaster prevention stage (t0 to t1), the disaster is about to occur. In this stage, the distribution network system operation function is complete; ② Disaster absorption stage (t1 to t2), this transient process occurs after the disaster occurs at time t1. In this stage, a large number of faults occur in the distribution network, and the system operation function cannot maintain a normal level and gradually declines. Emergency measures need to be taken to restore the system operation function as much as possible; ③ Disaster reduction operation stage (t2 to t3), after a period of time after the disaster occurs, due to the self-recovery of distribution and the weakening of the intensity of the disaster, it gradually reaches another stable operation state. At this time, the power grid is reduced and stable, and there may be certain fluctuations; ④ Post-disaster recovery stage (t3 to t4), the disaster is basically weakened after a long time, and the distribution network system operation function gradually improves, and finally returns to normal operation.

[0233] The second objective function is defined based on probability and can be expressed as:

[0234] minr2=P{D'(t) <D min}

[0235] Among them, considering the uncertainty of power grid failure and topology after the disaster, the limited recovery capacity of the power grid itself, and the uncertainty of the duration and attenuation of the disaster, the minimum probability D of the system operation function requirement of the power grid is set min .

[0236] The third objective function is defined based on the amount of load loss and can be expressed as:

[0237]

[0238] Among them, considering the different importance of different loads in actual application scenarios, such as primary load, secondary load, etc., different load importance weights are assigned, P i N (t) is the load of the power grid during normal operation, P i (t) is the actual supply load of the power grid after the disaster occurs. i is the importance weight of the corresponding load.

[0239] The fourth objective function is defined based on the amount of lost load and the total load, which can be expressed as:

[0240]

[0241] Among them, considering that the load loss is related to the total load, P i N (t) is the load of the power grid during normal operation, P i(t) is the actual supply load of the power grid after the disaster occurs. i is the importance weight of the corresponding load, and N is the number of nodes in the power grid.

[0242] In some possible implementations, the objective function of the second dimension may include at least one of a fifth objective function, a sixth objective function, and a seventh objective function:

[0243] The fifth objective function is defined based on network loss and can be expressed as:

[0244]

[0245] Among them, the network loss is minimized, that is, the economy and efficiency of the line when transmitting power. When the power is transmitted through the grid line, the active power consumed by the branch resistance is minimized. ij is the resistance of the branch between nodes i and j in the power grid; i ij is the square of the current amplitude in the branch between nodes i and j in the power grid.

[0246] The sixth objective function is defined based on the main grid electricity purchase and can be expressed as:

[0247]

[0248] Among them, the sixth objective function is improved from the following initial function:

[0249]

[0250] Considering the minimum purchase of electricity from the transmission network, it means that under normal operation, when the distribution network is connected to the transmission network, it purchases electricity from the transmission network to meet its own stable operation and obtain better resource utilization. t sell is the electricity price at time t, C t buy is the electricity purchase price at time t, P t buy is the amount of electricity purchased from the grid at time t, P t sell is the amount of electricity sold to the grid at time t. During a disaster, the transmission grid may be affected and fail, resulting in power outages, making it difficult to maintain a stable supply of disaster relief materials to the distribution grid. Therefore, when a typhoon disaster strikes, a statistical penalty factor is added. The wind speed v at the connection point between the distribution network and the transmission network is t As a criterion for evaluating whether the power support of the transmission network is effective, the penalty factor is invalid when the wind speed is low. nor When the penalty factor increases rapidly with the increase of wind speed, the above initial function can be improved to the sixth objective function.

[0251] The seventh objective function is defined based on the power generation cost and can be expressed as:

[0252]

[0253] Among them, C m is the fixed cost parameter for power generation, C build is the power generation investment cost parameter, C p is the power generation operation cost parameter, P t Consider the power generation cost, that is, the cost of generating electricity within the grid, including the construction and maintenance costs of the generator and the cost of generating electricity per kilowatt-hour.

[0254] In some possible implementations, the objective function of the third dimension may include at least one of an eighth objective function, a ninth objective function, a tenth objective function, and an eleventh objective function:

[0255] The eighth objective function is defined based on the node voltage offset and can be expressed as:

[0256]

[0257] Among them, the node voltage deviation in the power grid is considered to be the smallest, that is, the fluctuation of the node voltage near the rated voltage point. The smaller the value, the better the voltage quality. In the formula, U i,t is the actual voltage of node i at time t, U i 0 is the rated voltage of node i.

[0258] The ninth objective function is defined based on the power supply quality and can be expressed as:

[0259]

[0260] Among them, the better the power supply quality is, the smaller the difference in the required power change is for the generator and the main grid side. i max is the maximum power generation of distributed generator i, P i min is the minimum power generation of distributed generator i. is the maximum amount of electricity purchased from the transmission grid, It is the minimum value of electricity purchased from the transmission grid.

[0261] The tenth objective function is defined based on the utilization of clean energy and can be expressed as:

[0262]

[0263] Among them, the wind and solar power abandonment is minimized, that is, the power abandoned when absorbing wind power and photovoltaic power is minimized, so as to make better use of clean energy. real is the actual power generation of the wind turbine and / or photovoltaic generator set, P in It is the actual grid power input to the wind turbine and / or photovoltaic generator.

[0264] The eleventh objective function is defined based on the environmental pollution situation and can be expressed as:

[0265]

[0266] Among them, the environmental pollution degree is taken into consideration, that is, the degree of damage to the environment that may be caused by power generation based on fossil fuels. j The environmental loss caused by the jth type of pollutant generated by the generator, w j The content of the jth type of pollutant generated by this type of generator.

[0267] In some possible implementations, the process of determining the target Pareto solution of each objective function in the preset function set based on the target power flow parameter and the preset normal constraint algorithm in this embodiment can be expressed by the following formula based on the second-order cone mixed integer programming multi-objective optimization problem:

[0268]

[0269]

[0270] Among them, the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension are expressed as minR, minF, and minQ respectively, G(x) is the equality constraint vector, H(x) is the inequality constraint vector, and x is the parameter variable.

[0271] In this embodiment, by introducing the target flow parameters and the preset normal constraint algorithm, the target Pareto solution of each objective function in the preset function set can be systematically determined. First, the objective functions corresponding to the first dimension, the second dimension, and the third dimension are clarified from the preset function set. These objective functions cover multiple key dimensions such as power supply capacity, economy, and power quality, and fully reflect the core needs of power grid dispatching. Based on the target flow parameters, the single-objective optimization and solution of the objective functions of each dimension can be obtained, and the solution set of the preset function set can be obtained. These solution sets provide rich candidate solutions for subsequent multi-objective optimization. By constructing a solution space and determining the target solution plane therein, the distribution of each solution in the multidimensional space can be intuitively displayed. Further, by scanning the target solution plane using the first target vector and the second target vector, the Pareto optimal solution that meets the requirements of multiple objective functions can be efficiently found. These optimal solutions not only achieve a good balance between the objective functions, but also provide a scientific basis for power grid dispatching, which helps to achieve optimal allocation and efficient utilization of resources, and improve the operation stability and power supply reliability of the power grid under complex meteorological conditions.

[0272] See also Fig.10 , Fig.10 A schematic diagram of a process for determining a target solution plane is provided for one embodiment of the present application, such as Fig.10 As shown, the method of the embodiment of the present application may include the following steps S701-S702, and steps S701-S702 may be used as Figure 8 The illustrated embodiment is a refinement of step S604 of “determining a target solution plane in the solution space”.

[0273] S701, determining a plurality of boundary points corresponding to a solution set of a preset function set in a solution space;

[0274] S702, determining a target solution plane based on multiple boundary points.

[0275] Specifically, first, according to the definition and properties of each objective function in the preset function set, as well as the number of dimensions of the solution space and the value range of each dimension, the point set in the solution space can be determined. These point sets are formed by mapping each point in the solution set of the preset function set (i.e., each single-objective optimal solution) to the solution space. Then, the points located on the boundary of the solution space are screened out from these point sets, and these points are the boundary points. The determination of boundary points can be achieved by comparing the values ​​of different points in various dimensions. Points located at the edge of the solution space and reaching the upper or lower limit of their value range in at least one dimension can be regarded as boundary points. Since the solution space is multidimensional, boundary points may be located on various edges or corners of the solution space.

[0276] After determining multiple boundary points, some key points can be selected from these boundary points to define a target solution plane. The target solution plane is a two-dimensional plane with a specific direction and position in the solution space, which represents a state of reaching a certain balance or compromise between certain objective functions. When selecting key points, the trade-offs and conflicts between the various objective functions, as well as the requirements and limitations in actual power grid scheduling can be considered. For example, representative boundary points in the three dimensions of power supply capacity (first dimension), economy (second dimension) and power quality (third dimension) can be selected as key points, and then the target solution plane is defined by these key points. Based on this, in some possible implementations, the number of boundary points is 3, corresponding to the three dimensions of power supply capacity, economy and power quality.

[0277] In some possible implementations, assuming that the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension are expressed as minR, minF, and minQ, respectively, then based on the target power flow parameter, the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension are respectively optimized and solved for a single objective, and the solution set of the preset function set can include x R =[x R1 ,x R2 ,..,x Rn ], x F =[x F1 ,x F2 ,..,x Fn ], x Q =[x Q1 ,x Q2 ,..,x Qn ].

[0278] Furthermore, based on the solution set of the preset function set, the first dimension, the second dimension and the third dimension (R, F, Q), the solution space of the preset function set is constructed. R =[x R1 ,x R2 ,..,x Rn ], x F =[x F1 ,x F2 ,..,x Fn ], x Q =[x Q1 ,x Q2 ,..,x Qn ] are mapped to the solution space, and the three boundary points are X1=(R(x R ),F(x R ),Q(x R )), X2=(R(x F),F(x F ),Q(x F )), X3=(R(x Q ),F(x Q ),Q(x Q )). Based on the above three boundary points, the solution plane Ω1 (as the target solution plane) can be determined.

[0279] In this embodiment, by determining multiple boundary points in the solution space, the outline of the target solution plane can be accurately outlined. These boundary points represent the extreme points of the preset function set solution set in the multidimensional space. They not only reflect the optimal or near-optimal state of various dimensions such as power supply capacity, economy and power quality, but also reveal the trade-off relationship between different dimensions. The target solution plane determined based on these boundary points provides an intuitive and visual tool for finding Pareto optimal solutions that meet the requirements of multiple objective functions in a multidimensional space. On the target solution plane, the trade-offs and compromises between power supply capacity, economy and power quality of different solutions can be clearly seen, thereby helping decision makers to choose the most appropriate power dispatching scheme according to actual needs. This method of determining the target solution plane based on boundary points not only improves the scientificity and accuracy of power dispatching, but also helps to optimize the configuration and utilization of power grid resources and enhance the resilience and disaster resistance of the power grid under complex meteorological conditions. By accurately grasping the trade-off relationship between different dimensions, it is possible to better cope with the impact of meteorological disasters on the power grid and ensure the stability and reliability of power supply.

[0280] In one embodiment, based on Fig.10 In the illustrated embodiment, the plurality of boundary points include a first boundary point, a second boundary point, and a third boundary point; Figure 8 The step S604 of the illustrated embodiment, “determining the first target vector and the second target vector in the solution space” is further refined and may include the following steps:

[0281] In the solution space, determine a first initial vector, a second initial vector, and a third initial vector, wherein the first initial vector points from the origin of the solution space to the first boundary point, the second initial vector points from the origin to the second boundary point, and the third initial vector points from the origin to the third boundary point;

[0282] Determine a first target vector in the solution space based on the first initial vector and the second initial vector;

[0283] A second target vector is determined in the solution space based on the first initial vector and the third initial vector.

[0284] Specifically, in order to perform effective multi-objective optimization scanning on the target solution plane, it is necessary to determine the first initial vector, the second initial vector and the third initial vector in the solution space.

[0285] The first initial vector refers to the vector pointing from the origin of the solution space to the first boundary point. In multidimensional space, the origin of the solution space represents a state without any constraints and specific objective function values, that is, a point where all objective function values ​​are zero or initial values. The first initial vector starts from the origin and points to the first boundary point. Its direction represents the change trend from the initial state to the first boundary point (that is, the power supply capacity reaches a certain extreme point) in the power supply capacity dimension (first dimension). The first initial vector represents the strategy direction that may be taken to improve the power supply capacity during the optimization process.

[0286] The second initial vector refers to the vector pointing from the origin of the solution space to the second boundary point. Similarly, the second initial vector starts from the origin and points to the second boundary point. Its direction represents the change trend from the initial state to the second boundary point (i.e., the economy reaches a certain extreme point) in the economic dimension (second dimension). The second initial vector represents the strategic direction that may be taken to reduce economic costs during the optimization process.

[0287] The third initial vector refers to the vector from the origin of the solution space to the third boundary point. The third initial vector starts from the origin and points to the third boundary point. Its direction represents the change trend from the initial state to the third boundary point (that is, the power quality reaches a certain extreme point) in the power quality dimension (the third dimension). The third initial vector represents the strategy direction that may be taken to improve the power quality during the optimization process.

[0288] Next, based on the first initial vector and the second initial vector, a first target vector is determined in the solution space. This process can be achieved by performing vector subtraction or linear combination on the first initial vector and the second initial vector. The first target vector will be used to guide the scanning process on the target solution plane to find a solution that achieves a balance between power supply capacity and economy.

[0289] Similarly, based on the first initial vector and the third initial vector, a second target vector is determined in the solution space. This process is also achieved by performing vector subtraction or linear combination on the first initial vector and the third initial vector. The second target vector will be used to guide the scanning process on the target solution plane to find a solution that achieves a balance between power supply capability and power quality.

[0290] In the above way, two main change directions (i.e., the first target vector and the second target vector) can be defined in the solution space, which respectively represent the trade-off between power supply capacity and economy, and between power supply capacity and power quality. The first target vector and the second target vector will serve as a guide for the subsequent scanning process to help find the Pareto optimal solution that meets the requirements of multiple objective functions.

[0291] In some possible implementations, the first initial vector, the second initial vector, and the third initial vector are respectively expressed as Assume that the first target vector and the second target vector are expressed as So It can be obtained based on the following formula:

[0292]

[0293] In this embodiment, by determining multiple boundary points in the solution space and constructing initial vectors based on these boundary points, the target solution plane in the solution space can be more accurately defined. The first initial vector, the second initial vector, and the third initial vector point to the three boundary points from the origin of the solution space, respectively. These vectors not only represent the extreme value directions of the three dimensions of power supply capacity, economy, and power quality, but also provide the basis for constructing the target vector. Based on these initial vectors, the first target vector and the second target vector can be flexibly determined. These two vectors represent the trade-off relationship between power supply capacity and economy, and power supply capacity and power quality, respectively. By scanning the target solution plane with these two vectors, the Pareto optimal solution that meets the requirements of multiple objective functions can be efficiently found. By accurately grasping the trade-off relationship between different dimensions, the configuration and utilization of power grid resources can be better optimized, the resilience and disaster resistance of the power grid can be improved, and the stability and reliability of power supply can be ensured.

[0294] See also Fig.11 , Fig.11 A schematic diagram of a process for determining a target Pareto solution based on a scanning method is provided for an embodiment of the present application, such as Fig.11 As shown, the method of the embodiment of the present application may include the following steps S801-S802, and steps S801-S802 may be used as Figure 8 The detailed steps of step S605 of the illustrated embodiment.

[0295] S801, obtaining a scanning step length and a plurality of scanning weights, the sum of the plurality of scanning weights being 1;

[0296] S802, based on the scanning step size, multiple scanning weights, the first target vector and the second target vector, scan the target solution plane to obtain the target Pareto solution of each target function in the preset function set.

[0297] Specifically, the scanning step size involved in this embodiment refers to the distance or increment of each step when scanning on the target solution plane. It can be understood that the scanning step size determines the precision and calculation amount of the scanning. The smaller the scanning step size, the finer the scanning, but the greater the calculation amount; the larger the scanning step size, the rougher the scanning, but the less calculation amount.

[0298] The multiple scanning weights involved in this embodiment correspond one-to-one to the multiple dimensions of the solution space. For example, the multiple scanning weights may include a first scanning weight, a second scanning weight, and a third scanning weight. Then the first scanning weight, the second scanning weight, and the third scanning weight correspond to the first dimension (power supply capacity dimension), the second dimension (economic dimension), and the third dimension (power quality dimension) of the solution space, respectively. The scanning weight characterizes the relative importance or weight between each objective function in the multi-objective optimization process. By adjusting the scanning weight, preferences and trade-offs for different objective functions can be achieved, so as to find a Pareto optimal solution that meets different needs. The sum of multiple scanning weights is 1, which means that these scanning weights must meet the normalization condition in quantity, that is, their sum must be equal to 1, which ensures that each objective function can be reasonably considered and weighed during the scanning process.

[0299] Furthermore, based on the scanning step size, multiple scanning weights, the first target vector and the second target vector, the target solution plane is scanned to obtain the target Pareto solution of each target function in the preset function set. Specifically, it starts from an initial point on the target solution plane, and the initial point can be the origin of the solution space or other specified points. Then, according to the scanning step size and multiple scanning weights, the scanning is performed along the direction of the first target vector and the second target vector. In each step of scanning, the value of each target function of the current point is calculated, and it is determined whether the point meets the Pareto optimality condition, that is, for each target function, there is no other point that has a better value on the target function and a value that is not inferior to all other target functions.

[0300] If the current point meets the Pareto optimality condition, the point is added to the target Pareto solution set of the corresponding objective function; if not, the scan continues along the current direction until the set scan range is reached or the stop condition is met. By continuously adjusting the scan direction and step size, and considering the influence of multiple scan weights, a set of evenly distributed and high-information target Pareto solutions can be found on the target solution plane. Each target Pareto solution corresponds to an objective function, providing multiple possible optimization solutions for power grid dispatching.

[0301] In some possible implementations, the plurality of scanning weights may include a first scanning weight, a second scanning weight, and a third scanning weight, which are represented by α1, α2, and α3, respectively.

[0302] Then based on the above scanning weights, we can combine the scanning step size to scan the points on the solution plane Ω1

[0303] In this embodiment, by introducing a scanning step and multiple scanning weights, the scanning process on the target solution plane can be controlled more flexibly, so as to more accurately find the target Pareto solution of each objective function in the preset function set. The scanning step determines the precision of the scan. The smaller the step, the finer the scan, and more potential optimal solutions can be captured; while the larger the step, although the amount of calculation is reduced, some important solutions may be missed. Multiple scanning weights represent the relative importance of different objective functions. By adjusting the weights, preferences and trade-offs for different scheduling objectives can be achieved. Based on these parameters, scanning the target solution plane can systematically traverse various areas in the solution space and find the Pareto optimal solution that meets the requirements of multiple objective functions.

[0304] See also Fig.12 , Fig.12 A schematic diagram of a process for determining a target Pareto solution based on scanning and screening is provided for one embodiment of the present application, such as Fig.12 As shown, the method of the embodiment of the present application may include the following steps S901-S902, and steps S901-S902 may be used as Fig.11 The detailed steps of step S802 of the illustrated embodiment.

[0305] S901, based on the scanning step size, multiple scanning weights, the first target vector and the second target vector, scanning the target solution plane to obtain an initial Pareto solution set of each target function in the preset function set;

[0306] S902, screening the initial Pareto solution sets of each objective function respectively to obtain the target Pareto solution of each objective function.

[0307] Specifically, based on the scanning step, multiple scanning weights, the first target vector and the second target vector, the target solution plane is scanned to obtain the initial Pareto solution set of each objective function in the preset function set. It can be understood that due to the non-convex edge problem of the solution space caused by the complexity of the model and the evolution of the typhoon, as well as the existence of local stacking and redundant information problems caused by the irregular solution boundary and the weight step, the initial Pareto solution set of each objective function has some solutions that do not meet the Pareto optimality condition, that is, the values ​​of these solutions on at least one objective function are not optimal, or while satisfying the optimality of a certain objective function, the values ​​of other objective functions are inferior to other solutions.

[0308] To this end, it is necessary to screen the initial Pareto solution set of each objective function respectively to obtain the target Pareto solution of each objective function. The screening process can be performed based on the Pareto optimality condition and / or the crowding distance. For example, for each objective function, screen out solutions that meet the Pareto optimality condition, that is, there is no other solution that has a better value on this objective function and is not inferior to other all other objective functions. Then, the crowding distance is used to further screen the solution set to remove redundant information and locally stacked solutions. The crowding distance represents the distribution density of the solution in the solution space. The larger the crowding distance, the sparser the distribution of the surrounding solutions and the higher the information content. By retaining solutions with larger crowding distances, it can be ensured that the initial Pareto solution set of each objective function has higher information entropy and better distribution uniformity.

[0309] In this embodiment, by introducing a scanning step and multiple scanning weights, and scanning the target solution plane in combination with the first target vector and the second target vector, the initial Pareto solution set of each objective function in the preset function set can be systematically obtained. This process not only improves the scientificity and flexibility of power dispatching, but also enables decision makers to more comprehensively consider the trade-off relationship between different dispatching objectives. After obtaining the initial Pareto solution set, the initial Pareto solution set of each objective function is further screened to remove redundant and inferior solutions, thereby obtaining a more accurate and practical target Pareto solution. These target Pareto solutions not only meet the requirements of multiple objective functions, but are also evenly distributed in the solution space, with high information content, providing a wealth of optimization solutions for power dispatching.

[0310] In one embodiment, Fig.12 Step S902 of the illustrated embodiment is further refined and may include the following steps:

[0311] Based on the preset Pareto filter, the initial Pareto solution set of each objective function is screened respectively to obtain the first Pareto solution of each objective function;

[0312] Based on the first Pareto solution of each objective function, a target Pareto solution of each objective function is determined.

[0313] Specifically, assuming that the solution space exhibits convex solution edge characteristics, the initial Pareto solution set of each objective function meets the requirements. However, since the impact of meteorological disasters (such as typhoon disasters) during their evolution is determined by the geographical location of the meteorological disaster and meteorological information, which are all time-varying, it is more likely that the solution space will exhibit non-convex solution edge characteristics.

[0314] Based on this, this embodiment proposes to screen the initial Pareto solution set of each objective function based on the Pareto filter to obtain the first Pareto solution of each objective function. The Pareto filter refers to an algorithm tool for screening the Pareto solution set, which can remove solutions that do not meet the requirements from the initial Pareto solution set according to the Pareto optimality condition and other preset screening criteria, thereby obtaining a more accurate and reliable Pareto solution set.

[0315] The screening process is specifically to input the initial Pareto solution set of each objective function into the Pareto filter; then, the Pareto filter evaluates and compares each solution in the initial Pareto solution set according to the preset screening criteria; finally, the solutions that meet the requirements are screened out to form the first Pareto solution of each objective function. During the screening process, the Pareto filter pays special attention to the non-convex solution edge features of the solution space, and ensures that the final solution set has good distribution and information content in the solution space by identifying and removing inferior solutions such as concave points.

[0316] See also Fig.13 , Fig.13 An example schematic diagram of two-dimensional convex solution edge features and non-convex solution edge features provided for an embodiment of the present application. The solution space is displayed in two dimensions. In the solution space exhibiting non-convex solution edge features, the inward points are inferior to the outward points in all dimensions, that is, the values ​​of these inward points on at least one objective function are inferior to other solutions, and the values ​​on all other objective functions are not better, so these inward points should be screened out from the initial Pareto solution set of each objective function.

[0317] See also Fig.14 , Fig.14 An example schematic diagram of a three-dimensional convex solution edge feature and a non-convex solution edge feature provided for an embodiment of the present application. Among them, the solution space is displayed in three dimensions. In the solution space exhibiting non-convex solution edge features, it is not easy to directly identify the inferior solutions generated by the non-convex solution edge features, because these inferior solutions may be hidden in the complex solution space structure. However, through the screening of the Pareto filter, these inferior solutions can still be effectively screened out from the initial Pareto solution set of each objective function, ensuring that the final solution set has higher quality and practicality.

[0318] Furthermore, the first Pareto solution of each objective function may be respectively determined as the target Pareto solution of each objective function.

[0319] In this embodiment, by introducing a preset Pareto filter to screen the initial Pareto solution set of each objective function, redundant and inferior solutions can be efficiently removed, thereby retaining high-quality solutions that meet the requirements of multiple objective functions, namely the first Pareto solution. Based on the first Pareto solution of each objective function, the target Pareto solution of each objective function can be further determined. These solutions are evenly distributed in the solution space and have high information content, providing rich optimization options for power dispatching, which helps to optimize the configuration and utilization of power grid resources under complex meteorological conditions.

[0320] In one embodiment, Fig.12 Step S902 of the illustrated embodiment is further refined and may include the following steps:

[0321] Determine the crowding distance of the initial Pareto solution set of each objective function respectively;

[0322] Based on the crowding distance of the initial Pareto solution in the initial Pareto solution set of each objective function, the initial Pareto solution set of each objective function is screened respectively to obtain the second Pareto solution of each objective function;

[0323] Based on the second Pareto solution of each objective function, a target Pareto solution of each objective function is determined.

[0324] Specifically, the initial Pareto solution set of each objective function is closely related to the shape of the solution space, the scanning step size, and the scanning weight. When the solution edge in the solution space presents an irregular shape, the initial Pareto solution set of each objective function may have a local stacking problem. In addition, the setting of the scanning step size and the scanning weight will also significantly affect the distribution of the initial Pareto solution set. Specifically, the smaller the scanning step size and the scanning weight, the closer the initial Pareto solution set of each objective function can be to the shape of the actual solution space, but this may also make the stacking problem more serious and generate more redundant information, which will bring inconvenience to the actual decision-making process.

[0325] To this end, this embodiment proposes to introduce the indicator of crowding distance to screen the initial Pareto solution set of each objective function. First, it is necessary to determine the crowding distance of each initial Pareto solution in the initial Pareto solution set of each objective function. The process is specifically manifested as follows: for the initial Pareto solution set of each objective function, the crowding distance of each solution in the target solution plane is calculated. The calculation of the crowding distance can comprehensively consider the distribution of solutions on each objective function value and the relative position relationship between solutions. Specifically, the sum of the differences between each solution and its adjacent solutions on each objective function value can be calculated, and this sum of differences is the crowding distance of the solution. The larger the crowding distance, the sparser the distribution of the solution in the solution space, and the higher the amount of information it contains; conversely, the smaller the crowding distance, the denser the distribution of the solution in the solution space, and there may be redundant information.

[0326] Furthermore, based on the crowding distance of each initial Pareto solution in the initial Pareto solution set of each objective function, the initial Pareto solution set of each objective function is screened to obtain the second Pareto solution set of each objective function. The process is specifically as follows: first, a crowding distance threshold is set. Then, for the initial Pareto solution set of each objective function, the solutions whose crowding distance is less than the crowding distance threshold are removed from the solution set, and only the solutions whose crowding distance is greater than or equal to the crowding distance threshold are retained. Through this step, the second Pareto solution set of each objective function can be obtained. The solutions in the second Pareto solution set are more evenly distributed in the solution space, contain more information, and do not contain redundant information. Finally, based on the second Pareto solution set of each objective function, the target Pareto solution of each objective function is determined.

[0327] Furthermore, the second Pareto solution of each objective function may be determined as the target Pareto solution of each objective function.

[0328] In some possible implementations, screening the initial Pareto solution set of each objective function based on the crowding distance can be implemented based on the following formula:

[0329]

[0330] Among them, the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension are respectively expressed as minR, minF, and minQ. The objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension are respectively optimized to obtain x i+1 ,x i-1 . And f i ∈{R,F,Q} is a single objective value in the multi-objective optimization. By measuring the information content of the decision solution with the congestion degree and sorting the results, as much information as possible on the edge of the Pareto solution can be obtained with limited computing resources.

[0331] In this embodiment, by introducing the crowding distance as a screening index, the quality and distribution of the solutions in the initial Pareto solution set of each objective function can be measured more accurately. Solutions with large crowding distances are sparsely distributed in the solution space and have high information content, and are important candidate solutions for optimizing scheduling. Screening the initial Pareto solution set of each objective function based on the crowding distance can effectively remove redundant and inferior solutions, and retain those high-quality solutions that meet the requirements of multiple objective functions and are evenly distributed in the solution space, that is, the second Pareto solution. Based on the second Pareto solution of each objective function, the target Pareto solution of each objective function can be further determined. These solutions provide rich optimization options for power grid scheduling, help optimize the configuration and utilization of power grid resources, and improve the resilience and disaster resistance of the power grid under complex meteorological conditions.

[0332] In one embodiment, Fig.12 Step S902 of the illustrated embodiment is further refined and may include the following steps:

[0333] Based on the preset Pareto filter, the initial Pareto solution set of each objective function is screened respectively to obtain the first Pareto solution of each objective function;

[0334] Determine the crowding distance of the first Pareto solution in the first Pareto solution set of each objective function respectively;

[0335] Based on the crowding distance of the first Pareto solution in the first Pareto solution set of each objective function, the first Pareto solution set of each objective function is screened to obtain the second Pareto solution of each objective function;

[0336] Based on the second Pareto solution of each objective function, a target Pareto solution of each objective function is determined.

[0337] Specifically, this embodiment combines the Pareto filter with the crowding distance to implement the screening of the initial Pareto solution set of each objective function, and finally obtains the target Pareto solution of each objective function, so that the target Pareto solution of each objective function can effectively meet the Pareto optimality condition and have high information content.

[0338] In one embodiment, the target flow parameters of the power grid are first obtained, and then the target function corresponding to the first dimension, the target function corresponding to the second dimension, and the target function corresponding to the third dimension are respectively optimized and solved based on the target flow parameters of the power grid to obtain a solution set of a preset function set; based on the solution set of the preset function set, the first dimension, the second dimension, and the third dimension, a solution space of the preset function set is constructed.

[0339] Determine the target solution plane, the first target vector, and the second target vector in the solution space. Scan the target solution plane based on the first target vector and the second target vector to obtain the first Pareto solution set of each target function in the preset function set, and the number of first Pareto solutions in the first Pareto solution set is at least one. Determine the crowding distance of the first Pareto solution in the first Pareto solution set of each target function respectively; based on the crowding distance of the first Pareto solution in the first Pareto solution set of each target function, screen the first Pareto solution set of each target function respectively to obtain the second Pareto solution of each target function; based on the second Pareto solution of each target function, determine the target Pareto solution of each target function. Finally, dispatch the power grid according to the target Pareto solution of each target function.

[0340] In this embodiment, by combining the Pareto filter and the crowding distance to double-screen the initial Pareto solution set, the target Pareto solution of each objective function can be obtained more accurately. The Pareto filter first removes solutions that do not meet the Pareto optimality conditions to ensure the effectiveness of the solution set. Subsequently, the crowding distance further measures the distribution density of solutions in the solution space, removes redundant and inferior solutions, and retains high-quality solutions with high information content. This process makes the final target Pareto solution set more refined and practical, which is conducive to optimizing the configuration and utilization of power grid resources and improving the resilience and disaster resistance of the power grid under complex meteorological conditions.

[0341] Based on the above Figure 2 - Fig.14 The embodiment shown and the related detailed embodiments will be combined with Fig.15 - Fig.24 , an embodiment is proposed to further illustrate the application of power dispatch under disasters based on normal constraints.

[0342] Specifically, see Fig.15 , Fig.15 This is an example schematic diagram of a distribution network topology provided in an embodiment of the present application. To construct the distribution network topology, the software tools used in this embodiment include:

[0343] Matlab: A high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical computing. In this embodiment, Matlab is used as a platform for algorithm development and solution.

[0344] Matpower: An open source toolbox for simulation and analysis of power systems and power markets, providing functions such as power grid flow calculation and optimal power flow calculation. In this embodiment, Matpower provides network topology information for building and simulating distribution network models.

[0345] yalmip: A MATLAB toolbox for modeling and solving optimization problems, supporting multiple optimization solvers. In this example, yalmip is used to construct and represent the optimization problem.

[0346] Gurobi: A high-performance mathematical optimization solver that supports a variety of optimization problems such as linear programming, quadratic programming, and integer programming. In this embodiment, Gurobi is used as a solver to solve the optimization problem constructed by yalmip.

[0347] Fig.15 The component types and corresponding configuration parameters involved in the distribution network topology shown include:

[0348] On-load voltage regulator: Located Fig.15The node 1 shown is used as a voltage regulating device at the connection with the main grid to adjust the grid voltage. Its ratio adjustment range is 0.95-1.05, indicating that the transformer ratio can be adjusted within this range; the number of gears is 10, indicating that there are 10 different ratio levels available for adjustment; the maximum switching times is 10 times, indicating that the transformer ratio can be adjusted up to 10 times within a certain period of time.

[0349] Distributed gas turbines: located in Fig.15 The nodes 20 and 29 shown are used as power generation devices to provide electric power.

[0350] Static VAR compensation device: located in Fig.15 The nodes 4 and 32 shown are used to provide or absorb reactive power to maintain the power factor of the power grid stable. The maximum capacity is 900 kVar, which represents the maximum reactive power that the device can provide.

[0351] Group switching capacitor: located Fig.15 The nodes 8 and 15 shown are used to change the capacitor capacity of the power grid by switching capacitors, thereby adjusting reactive power. The number of gears is 6, indicating that there are 6 different capacitor capacity levels available for switching; the maximum switching times is 8 times, indicating that the capacitor can be switched up to 8 times within a certain period of time; the maximum capacity is 600kVar, indicating the maximum reactive power of all capacitor capacity levels added together.

[0352] New energy power generation equipment (wind turbines or photovoltaic generators): located in Fig.15 The node 24 and the node 16 shown are used to provide clean energy.

[0353] Energy storage device: located at Fig.15 The nodes 6 and 16 shown are used to store and release electric energy to cope with fluctuations and changes in grid load.

[0354] Mobile energy storage vehicle: A mobile energy storage device that can be flexibly deployed in the power grid to provide power support. The maximum moving speed of the mobile energy storage vehicle is v max =2.4km / min.

[0355] Based on the above Fig.15 The distribution network topology is obtained, and the meteorological data of the power grid area provided by the meteorological station is obtained. The time interval between the two measurement results after the disaster is T = 2 to 6 hours. Consider the impact of the typhoon disaster on the distribution network lines and renewable energy output in the interval of T = 6 hours. Taking the typhoon starting coordinates as the origin, it can be seen from the meteorological data of the power grid area provided by the meteorological station that the typhoon path can be regarded as a straight line in a short period of time. The coordinates of its stop period are (20,6). The data of the two recorded time points are used as references, and the head end is R m,0=28km,v m,0 =26m / s, and the end is R m,T =22km,v m,T =24m / s. The horizontal and vertical wind profiles are affected by the shielding effect of buildings and friction resistance, and decay over time. Suppose the longitude and latitude coordinates of the typhoon center at time t are (x t ,y t ), the location of the target object (such as power equipment or other structures in the power grid) is (x e ,y e ), then according to the longitude and latitude conversion formula, the distance d between the two points is:

[0356]

[0357] Where R is the radius of the earth. Substituting the distance between the two points into the typhoon wind speed calculation formula, the wind speed at the target object can be obtained, and then its disaster situation can be judged. For the transmission lines in the power grid, the end points and center points of the transmission lines can be analyzed to obtain the corresponding line failure rate, and the scenario with the highest probability can be obtained from the failure rate matrix.

[0358] After the typhoon impact ends, that is, when the line center leaves the typhoon level 10 wind circle, emergency repairs can be started to restore load supply as soon as possible. The recovery process is also uncertain, and a certain probability distribution function is usually used to describe the process, that is, the emergency repair time satisfies the log-normal distribution:

[0359]

[0360] Among them, T f is the actual repair time; μ r , σ r is the corresponding mean and variance of the emergency repair time. Due to the different years of operation of transmission lines, the longer the operation years, the higher the time cost required for maintenance. By introducing the line aging coefficient κ, the emergency repair time is further corrected to:

[0361] oeLh f ~N(σ r ,μ r )κ

[0362] From the obtained fault set, we can get the typhoon-distribution network disaster scenario:

[0363] S={s1,s2,s3,...,s m ,...,s n}

[0364] The corresponding probability of typhoon-distribution network disaster scenario:

[0365] P={p1,p2,p3,...,pm ,...,p n}

[0366] The typical scenario of using information entropy weight method to screen it, the corresponding entropy value δ s for:

[0367]

[0368] Among them, u s,i,t is the line fault condition corresponding to the ith line in scenario s at time t, which is a 0-1 variable, and is 1 if a fault occurs, and 0 if no fault occurs. Furthermore, this embodiment selects a scenario with high information content as a typical scenario for calculation.

[0369] Based on the above scenarios and the aforementioned modeling, the output parameters of the target power generation equipment in the power grid affected by the target meteorological disaster under the influence of the meteorological characteristic parameters of the typhoon disaster can be analyzed.

[0370] See also Fig.16 , Fig.16 The following is a schematic diagram of an example of the output of a target power generation device provided in one embodiment of the present application. The target power generation device includes a wind turbine and a photovoltaic generator set, both of which are new energy power generation devices. Fig.16 As shown, the average maximum wind speed v of the typhoon is m =25m / s and the average maximum wind speed of typhoon v m =Measured at 36m / s.

[0371] See also Fig.17 , Fig.17 This is an example schematic diagram of the output of the target power generation equipment provided by an embodiment of the present application. The typhoon period is 6 hours. Fig.17 The output of the target power generation equipment for 24 hours is shown. In the case of a typhoon disaster, the target power generation equipment is cut out for some time periods to protect it, and the typhoon wind speed is too high, which will cause it to remain in the cut-out state after the typhoon passes.

[0372] See also Fig.18 and Fig.19 , Fig.18 An example schematic diagram of energy storage charging and discharging provided in one embodiment of the present application is shown in FIG. Fig.19 This is an example schematic diagram of the total amount of power grid load provided in one embodiment of the present application. Fig.18 The figure shows the operation of the basic part of the distribution network under relatively mild wind speed. After time 2, the typhoon disaster generally invaded the distribution network area, causing severe disasters. New energy power generation equipment was shut down, and gas turbines were required to generate electricity to supply the load. At this time, the energy storage was discharged, and the mobile energy storage delivered electricity to the affected network area. Fig.19It shows that the total load of the power grid dropped sharply after time 2, and at time 6 the typhoon disaster was basically alleviated, part of the line was repaired, and the new energy power generation equipment was restored.

[0373] Considering the multi-dimensional resilience improvement strategy under the spatiotemporal evolution of typhoon disasters, in some cases, the dual indicators of power supply capacity and economy can be improved. The corresponding multi-objective optimization problem is expressed as follows:

[0374]

[0375] The specific meanings of the function variables in the formula can be found in the above embodiments and will not be repeated here. The normal constraint algorithm is used to solve the multi-objective optimization problem, and the corresponding solution sets are obtained under the 20-point method with a smaller solution accuracy and the 100-point method with a larger solution accuracy. It can be understood that there is a mapping relationship between the solution accuracy and the scanning step size. In other words, the scanning step size determines the fineness and calculation amount of the scan. The smaller the scanning step size, the finer the scan, but the greater the calculation amount; the larger the scanning step size, the coarser the scan, but the calculation amount is reduced.

[0376] See also Fig. 20 , Fig. 20 This is an example diagram of obtaining a solution set based on a normal constraint algorithm provided for an embodiment of the present application. In the solution process, the smaller the traversal weight step size is set, the more points are obtained and the more perfect the solution boundary is, but it takes more time and increases linearly. At the same time, there are dense point areas in the solution process, mainly concentrated near the inflection point, which is also a common result in general weighted method processing.

[0377] See also Fig.21 , Fig.21 An example schematic diagram of obtaining a solution set based on a weighted algorithm provided for an embodiment of the present application. The boundary obtained by scanning the weighted method is only a part of the actual solution boundary. Even if the normal constraint method has a very small solution accuracy, the boundary obtained is more complete than the weighted method. For some extreme cases where it is necessary to consider increasing economic investment to maximize the supply load capacity, it is obvious that the results obtained by the normal constraint method are more in line with application requirements.

[0378] In order to ensure that the set indicators have a good guiding significance for decision-making, and to avoid the problem of indicator inline that may occur when multiple indicators are investigated at the same time, the present embodiment can also test the cross-coupling relationship between indicators under three-dimensional conditions. If only the empirical coefficient is used to convert the multi-objective optimization problem into a single-objective optimization problem for solution, the result obtained may limit the decision-making field of view and lead to misjudgment. Therefore, more boundary information can be obtained by using the normal constraint method. In principle, it can also be known that the normal constraint method can obtain points within the entire boundary range as long as the number of traversal points is sufficient. Because it first obtains the boundary vertex, and then traverses the entire solution space from the vertex. When the number of traversal points increases, the information about the boundary is more complete, the fitting effect becomes better, and the time required for calculation is more, and a lot of redundant information is also generated, that is, stacked points, the numerical difference between the points is small, and it appears redundant when quick decision-making or mastering global information is required, so it can be screened based on Pareto filter and crowding distance.

[0379] See also Fig. 22 , Fig. 22 An example schematic diagram of screening solution sets of different solution accuracies provided for an embodiment of the present application. Among them, the result is that the stacked information is eliminated to a certain extent, eliminating the optimal decision solution set obtained by non-Pareto optimality. Note that compared with the solution accuracy of 100 points, there are still non-Pareto solutions in the case of 20-point solution accuracy. This is caused by the traversal step size being too large and the sample size being too small. A better Pareto edge is obtained in the result corresponding to the solution accuracy of 100 points. It can also be found that under the spatiotemporal evolution of typhoon disasters, since the model boundary will change over time, non-convex edges will be generated in it, resulting in the normal constraint method based on the numerical analytical method to produce non-Pareto solutions. Therefore, it is necessary to screen based on Pareto filters and crowding distances.

[0380] Considering the multi-dimensional resilience improvement strategy under the spatiotemporal evolution of typhoon disasters, in some cases, the dual indicators of power supply capacity and power quality can be improved. The corresponding multi-objective optimization problem is expressed by the following formula:

[0381]

[0382] The specific meaning of the function variables in the formula can be found in the above embodiments and will not be repeated here. Since more line node information is added to the power quality, it is directly affected by the disaster situation of the distribution network. The model boundary changes more frequently during the actual disaster process, so there are more non-convex points. Therefore, a large number of non-Pareto solutions appear in the results obtained. After screening based on the Pareto filter and the crowding distance, many internal points on the non-convex boundary are eliminated, leaving the Pareto solution, and a clean Pareto edge is obtained, showing better boundary information and dominant solution, which is of great help to actual decision-making. In this case, the stacking area appears at the inflection point.

[0383] See also Fig.23 , Fig.23 An example schematic diagram of screening a solution set provided for an embodiment of the present application. Among them, combined with the above two cases of toughness improvement in the two-dimensional case, considering the improvement of the three indicators of power supply capacity, economy and power quality, the solution boundary after using the normal constraint method and screening is different from the two-dimensional case in that the solution plane is not the Pareto solution set of the plane after slicing, but a set with the Pareto solution set as a subset. The Pareto edge can still be obtained by screening based on the Pareto filter and the crowding distance, because there is a third indicator at this time, and the result will be affected by it.

[0384] At this time, if the solution is solved according to the ordinary weight scanning method, due to the addition of three indicators with different numerical spans, the solution of the target with a small order of magnitude is more complicated and time-consuming, resulting in the overall solution being very difficult, and the time complexity is far greater than the normal constraint method. In the three-dimensional case, for a day-ahead optimization problem, only the 24-hour configuration is obtained, and the solution may lose timeliness due to the long solution time. It should be noted that compared with the single-objective optimization problem, the multi-objective optimization gives a set of decision solutions for selection, and the expansion of its overall optimization architecture also requires more computing power support, which will lead to an increase in computational complexity, resulting in an increase in the time resources consumed in the engineering operation, and a decrease in the feasibility of engineering applications with limited computing power resources or time resources. In addition, from the perspective of the actual distribution network dispatching structure, the distribution network is actually an information-physical coupled network, and the dispatching of its physical layer equipment depends on the operation instructions obtained by the secondary equipment. The instructions are issued by the upper decision-making layer and transmitted to the corresponding dispatched physical equipment through the information transmission system to realize the equipment control process. This process is highly dependent on the connectivity of the information transmission system and the availability of physical equipment. When the system is hit by extreme natural disasters, both the information transmission system and the physical equipment will be damaged, resulting in equipment being unadjustable or information transmission being difficult. When optimizing objectives on this information-physical coupling network, more decision variables and judgment information are required for multiple objectives, so there are more impacts and restrictions under disasters.

[0385] See also Fig.24 , Fig.24 This is a schematic diagram of an example of the time complexity of different solution algorithms provided in one embodiment of the present application. Fig.24 By comparison, it can be seen that the time complexity of the improved normal constraint method is much smaller than that of the weighted scanning method for the same model, the same dimension, and the same number of iterations; and after the dimension is increased, problems that are difficult to solve by the weighted scanning method can still be quickly solved using the improved normal constraint method. Its solution speed is only similar to that of the two-dimensional weighted scanning method, and is even faster at some numbers of iterations. This shows that it has better performance in terms of time resource consumption.

[0386] For the Pareto solution set obtained above, there are multiple solutions, and any solution is no worse than any point. In actual decision-making, the corresponding execution strategy can be selected according to actual needs, such as the need to supply important loads or the need for high power quality. It is also possible to consider introducing methods such as hierarchical analysis to judge the pros and cons of strategies. On the whole, the choice of strategy is related to the specific needs of the application scenario or the subjectivity of the decision maker, and the solution can be flexibly adjusted according to the actual decision-making needs. Especially in disaster response, different disaster types, resource availability, and social needs will continue to change. The resulting decision set allows decision makers to dynamically weigh various goals, reveal goal conflicts and trade-offs, and decision makers can intuitively understand how other goals will be affected when optimizing a certain goal.

[0387] For example, in order to help understand the implementation process of the power dispatching method under disasters obtained by combining the embodiments of the present application with the above-mentioned embodiment 1, please refer to Fig.25 , Fig.25 A brief flow chart of a power dispatching method under disaster conditions is provided, specifically:

[0388] In the present embodiment of the application, a multi-objective optimization model is constructed by comprehensively considering the power supply capacity, economy and power quality of the power grid to improve the resilience of the power grid under typhoon disasters. The model first obtains the power flow parameters of the power grid by combining the distributed resources of the power grid, such as energy storage, generators, load sources and control elements, and dynamic wind field models of meteorological disasters such as typhoons, by predicting information such as data, fluctuations and noise. These parameters not only reflect the operating status of the power grid under the influence of typhoon disasters, but also provide basic data for the optimal dispatch of the power grid.

[0389] Next, the objective functions of power supply capacity, economy and power quality are used in combination with power flow parameters to find the optimal solution in the solution space through the normal constraint algorithm. The solution space is multidimensional, and each dimension represents an objective function. By determining the target solution plane in the solution space, a solution that strikes a balance between power supply capacity, economy and power quality can be found. In the solution space, by selecting the current operating point and obtaining the decision plane, each point on the plane represents a potential scheduling solution.

[0390] Furthermore, based on the decision of the current spatial position, the system searches for anchor points, that is, the optimal solutions found in the solution space, which can be used as candidate solutions for grid dispatch. Then, through solution space segmentation, the solution space is divided into multiple regions, each of which contains a set of possible dispatch solutions. In this way, the most appropriate solution can be selected from these regions for implementation according to the actual needs and operating conditions of the grid.

[0391] In addition, the system will also cyclically update the decision-making process based on real-time updated meteorological information and power grid status to ensure that the power grid dispatch plan can adapt to the dynamic changes of typhoon disasters and achieve real-time optimization of the power grid. This process not only improves the resilience of the power grid under typhoon disasters, but also ensures the economy and power quality of the power grid operation through comprehensive considerations in multiple dimensions, and achieves stable power supply and efficient operation of the power grid under disasters.

[0392] Based on the above Figure 1 - Fig.25 For the illustrated embodiment, see Fig.26 , Fig.26 A schematic diagram of a comprehensive flow chart of a method for power dispatching under disaster conditions provided for one embodiment of the present application.

[0393] First, a meteorological model of the target meteorological disaster is constructed, and the meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located are obtained based on the meteorological model of the target meteorological disaster. The line fault parameters of the target structure affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters are obtained, and the output parameters of the target power generation equipment affected by the target meteorological disaster in the power grid under the action of the meteorological characteristic parameters are obtained. The target power flow parameters of the power grid are determined based on the line fault parameters and the output parameters.

[0394] On the basis of obtaining the target power flow parameters of the power grid, the following steps are further performed:

[0395] (1) Solution space segmentation, adding constraints, and traversal solution:

[0396] Solution space partitioning: According to the target flow parameters and the preset function set (including multiple objective functions), each objective function is first optimized and solved separately to obtain a preliminary solution set. These solution sets form a preliminary distribution in the multidimensional objective space. Then, based on the distribution characteristics of these solution sets in the objective space, the solution space is divided into multiple sub-regions. Each sub-region may contain a part of the Pareto frontier (that is, the optimal solution set in the multi-objective optimization problem) and may also contain some non-optimal solutions. The purpose of solution space partitioning is to divide the complex solution space into smaller and more manageable parts so that the subsequent optimization solution process can find the Pareto optimal solution more efficiently.

[0397] New constraints: During the traversal solution process, a series of constraints need to be established to ensure that the solution is practical. These constraints may include stability constraints, safety constraints, equipment capacity constraints, power balance constraints, etc. of the power grid. Stability constraints ensure that the power grid will not experience problems such as voltage collapse or frequency instability during operation; safety constraints ensure that the power grid can meet safety standards under various operating conditions; equipment capacity constraints limit the maximum output power and transmission capacity of power generation equipment and transmission lines; power balance constraints require that the power generation power and load power in the power grid must be balanced. By establishing constraints, it can be ensured that the solution obtained during the traversal solution process meets the actual operation requirements of the power grid.

[0398] Traversal solution: Based on the partitioning of the solution space and the establishment of constraints, an appropriate optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, etc.) is used to traverse and solve each sub-region. The process of traversal solution is to visit the solutions in the sub-region one by one and evaluate the performance of these solutions on the objective function. By comparing the objective function values ​​of different solutions, the optimal solution or approximate optimal solution in each sub-region can be found. The traversal solution process needs to comprehensively consider multiple objective functions to find a solution that achieves a balance in multiple dimensions such as power supply capacity, economy, and power quality. After the traversal solution is completed, the optimal solutions or approximate optimal solutions in all sub-regions are combined to form a Pareto optimal solution set in the entire solution space.

[0399] (2) Screening based on Pareto filter and screening based on crowding distance:

[0400] Screening based on Pareto filter: First, use the Pareto filter to screen the initially obtained Pareto optimal solution set. The Pareto filter is an algorithmic tool for screening the Pareto optimal solution set. It can remove solutions that do not meet the requirements based on the Pareto optimality condition and other preset screening criteria (such as the diversity of solutions, the uniformity of solution distribution, etc.). The Pareto optimality condition requires that a solution is not inferior to other solutions in all objective functions, that is, there are no other solutions that are better than this solution in all objective functions. Through Pareto filter screening, solutions dominated by other solutions (that is, solutions that are not better than other solutions in all other objective functions) can be removed, thereby obtaining a more streamlined and high-quality Pareto optimal solution set.

[0401] Screening based on crowding distance: In order to further improve the quality and diversity of the Pareto optimal solution set, crowding distance can be used as a screening indicator. Crowding distance is an indicator that measures the distance between a solution and other solutions in the target space. The larger the crowding distance, the sparser the distribution of the solution in the target space, the greater the difference between it and other solutions, and the higher the information content. By calculating the crowding distance of each solution and setting a crowding distance threshold, solutions with crowding distances less than the threshold can be removed from the solution set, and only solutions with crowding distances greater than or equal to the threshold are retained. In this way, a Pareto optimal solution set that meets the Pareto optimality condition and has high diversity can be obtained. Crowding distance screening helps to avoid the solution set from being too concentrated and improves the distribution uniformity and information content of the solution set.

[0402] Finally, after the above screening process, the target Pareto solutions of each objective function are obtained. These target Pareto solutions achieve a good balance between multiple objective functions such as power supply capacity, economy and power quality, and provide a variety of feasible solutions for power dispatching of the power grid.

[0403] (3) Dispatching power from the power grid based on the target Pareto solution of each objective function:

[0404] According to the obtained target Pareto solution, a variety of power dispatching schemes can be formulated. These schemes perform differently on different objective functions. Some schemes can pay more attention to the economy of the power grid, and maximize economic benefits by optimizing the power generation cost, power purchase cost and network loss; some schemes can pay more attention to the stability and security of the power grid, and meet the power demand of users by ensuring the stability and security of the power grid under various operating conditions; and some schemes can pay more attention to the quality of power, and improve the quality of power by optimizing voltage stability, frequency stability and power factor.

[0405] In practical applications, the most appropriate power dispatching scheme can be selected and implemented according to the actual situation and needs of the power grid. For example, when the power grid load is high and the electricity price is low, a scheme that focuses more on economy can be selected to reduce the cost of power generation and power purchase; when there are safety hazards or failure risks in the power grid, a scheme that focuses more on stability and safety can be selected to ensure the reliable operation of the power grid; when users have high requirements for power quality, a scheme that focuses more on power quality can be selected to improve the power supply quality. By implementing the selected power dispatching scheme, the allocation and utilization of power resources in the power grid can be optimized, and the resilience and disaster resistance of the power grid under meteorological disasters can be improved. Specifically, according to the control parameter values ​​in the target Pareto solution, the output of various power generation equipment (such as wind turbines, photovoltaic generators, gas turbines, etc.) in the power grid can be adjusted, the charging and discharging strategies of energy storage equipment (such as chemical energy storage devices, mobile energy storage vehicles, etc.) can be optimized, and the transmission power of the transmission line and the voltage of the substation can be adjusted. Through these adjustment measures, it can be ensured that the power grid can still provide stable and reliable power supply under meteorological disasters to meet the power needs of users.

[0406] In addition, as meteorological disasters change in real time, the state and demand of the power grid will also change. Therefore, it is necessary to monitor meteorological information and power grid status in real time and dynamically adjust the power dispatch plan based on this information. By cyclically updating the decision-making process, it can ensure that the power grid dispatch plan can adapt to the dynamic changes of meteorological disasters and realize real-time optimization of the power grid.

[0407] It should be understood that, although the various steps in the flowchart are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0408] The following will be combined Fig. 27 The disaster power dispatching system provided in the embodiment of the present application is introduced in detail. It should be noted that: Fig. 27 The disaster power dispatching system in the application is used to implement the Figure 1 - Fig.26 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 - Fig.26Specifically, the disaster power dispatching system 1 may include a first acquisition unit 11, a second acquisition unit 12 and a dispatching unit 13, as follows:

[0409] A first acquisition unit 11 is used to construct a meteorological model of a target meteorological disaster, and acquire meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located based on the meteorological model of the target meteorological disaster;

[0410] The second acquisition unit 12 is used to acquire line fault parameters of target structures affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters, and to acquire output parameters of target power generation equipment affected by target meteorological disasters in the power grid under the action of meteorological characteristic parameters;

[0411] The dispatching unit 13 is used to determine the target power flow parameters of the power grid based on the line fault parameters and the output parameters, determine the target Pareto solution of each objective function in the preset function set based on the target power flow parameters and the preset normal constraint algorithm, and perform power dispatch on the power grid according to the target Pareto solution of each objective function.

[0412] Optionally, in some embodiments, the first acquisition unit 11 can be used to: acquire meteorological data of the area where the power grid is located, extract original meteorological parameters of the target meteorological disaster from the meteorological data; and construct a meteorological model of the target meteorological disaster based on the original meteorological parameters.

[0413] Optionally, in some embodiments, the target meteorological disaster is a typhoon disaster; the first acquisition unit 11 can be used to: determine the micro-topography attenuation parameters of the area corresponding to the typhoon disaster; and construct a meteorological model of the target meteorological disaster based on the original meteorological parameters and the micro-topography attenuation parameters.

[0414] Optionally, in some embodiments, the meteorological model of the target meteorological disaster is expressed as:

[0415]

[0416] Among them, the original meteorological parameters include r c 、v m 、r m , b, α, z0, χ, s, micro-topography attenuation parameters include u, ΔH(t), ΔX;

[0417] v(r,θ,z,t) is the wind speed of any target point in the wind field of the typhoon disaster, r is the distance between the center of the typhoon disaster and the target point, θ is the angle between the target point and the x-axis of the reference coordinate system, z is the vertical height of the target point, r c is the maximum wind circle radius of the typhoon disaster, v m is the maximum wind speed in the center of the typhoon disaster, r m is the maximum wind circle radius of the typhoon disaster, R is the sum of r and rm where t is the ratio of t to h, b is the outer wind profile information of the typhoon disaster, α is the vertical attenuation coefficient of the typhoon disaster, z0 is the horizontal reference height of the target point, χ is the wind circle coefficient of the typhoon disaster, s is the asymmetry factor of the typhoon disaster, u is the first wind speed attenuation coefficient caused by the building density in the axial direction, ΔH(t) is the second wind speed attenuation coefficient caused by the change of the urban underlying surface on the horizontal wind speed of the moving time-varying typhoon, ΔX is the overall attenuation contribution rate of the wind field caused by urbanization, and t is the time period calculated with the typhoon landing as the initial moment.

[0418] Optionally, in some embodiments, the second acquisition unit 12 can be used to: obtain a first meteorological characteristic load of a target structure in a power grid affected by a target meteorological disaster in a target meteorological disaster, the first meteorological characteristic load being a meteorological characteristic critical value at which a corresponding target structure fails in a target meteorological disaster; determine a second meteorological characteristic load of the target structure in a target meteorological disaster based on meteorological characteristic parameters; determine line fault parameters of the target structure under the action of the meteorological characteristic parameters based on the first meteorological characteristic load and the second meteorological characteristic load of the target structure; determine line fault parameters of the power grid under the action of the meteorological characteristic parameters based on the line fault parameters of the target structure under the action of the meteorological characteristic parameters.

[0419] Optionally, in some embodiments, the target meteorological disaster is a typhoon disaster, and the meteorological characteristic parameters include μ v ,δ v , v, the line fault parameters of the target structure under the action of meteorological characteristic parameters include P, and the calculation formula of the line fault parameters of the target structure under the action of meteorological characteristic parameters is as follows:

[0420]

[0421] Where P is the line failure rate corresponding to the target structure, v is the wind speed at the location of the target structure, μ v is the mean of the design wind speed of the transmission line corresponding to the target structure, δ v is the standard deviation of wind resistance strength;

[0422] The line fault parameters of the power grid under the influence of meteorological characteristic parameters include P k , the calculation formula of the line fault parameters of the power grid under the influence of meteorological characteristic parameters is as follows:

[0423]

[0424] Among them, P k is the line failure rate corresponding to the k-th level line in the power grid.

[0425] Optionally, in some embodiments, the second acquisition unit 12 can be used to: obtain a first meteorological characteristic threshold and a second meteorological characteristic threshold of a target power generation equipment affected by a target meteorological disaster in a power grid, the first meteorological characteristic threshold being a meteorological characteristic critical value at which the corresponding target power generation equipment is shut down in a target meteorological disaster, and the second meteorological characteristic threshold being a meteorological characteristic critical value at which the corresponding target power generation equipment is damaged in a target meteorological disaster; based on the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment, determine the output parameters of the target power generation equipment under the action of the meteorological characteristic parameters.

[0426] Optionally, in some embodiments, the target power generation equipment includes a wind turbine, and the meteorological characteristic parameters include The calculation formula for the output parameters of the target power generation equipment under the influence of meteorological characteristic parameters is as follows:

[0427]

[0428] Among them, t is the time, is the maximum output of the wind turbine at time t under typhoon disaster. The coordinates of the wind turbine in the power grid are (x, y). is the maximum wind speed that the wind turbine can bear, is the cut-out wind speed of the wind turbine, The real-time wind speed at the location of the wind turbine; is the maximum wind speed experienced by the wind turbine up to time t; c is the wind energy utilization coefficient, and k is the exponential decay rate of the wind turbine.

[0429] Optionally, in some embodiments, the dispatch unit 13 may be used to: determine a target parameter combination based on line fault parameters and output parameters; determine a target power flow parameter of the power grid based on the target parameter combination and a mapping relationship between the parameter combination and the power flow parameter.

[0430] Optionally, in some embodiments, the mapping relationship between the parameter combination and the power flow parameter is expressed as a target power flow model, and the target power flow model is expressed as:

[0431]

[0432]

[0433]

[0434] v j =v i -2(R ij P ij +X ij Q ij )+(Rij 2 +X ij 2 )i ij

[0435] Among them, G j , B j Respectively represent the jth equality constraint and inequality constraint; P ij , Q ij They represent the active power and reactive power flowing from node i to node j in the power grid respectively; P j , Q j They represent the active power and reactive power injected into the grid at node j respectively; R ij , X ij Respectively represent the resistance and impedance of the branch between node i and node j in the power grid; i ij is the square of the current amplitude on the branch between node i and node j in the power grid; v ij is the square of the voltage amplitude on the branch between node i and node j in the power grid; v j is the square of the voltage amplitude at node j in the power grid.

[0436] Optionally, in some embodiments, the scheduling unit 13 can be used to: determine, from a preset function set, an objective function corresponding to the first dimension, an objective function corresponding to the second dimension, and an objective function corresponding to the third dimension; based on the target flow parameters, perform single-objective optimization solutions for the objective function corresponding to the first dimension, the objective function corresponding to the second dimension, and the objective function corresponding to the third dimension, respectively, to obtain a solution set of the preset function set; construct a solution space of the preset function set based on the solution set of the preset function set, the first dimension, the second dimension, and the third dimension; determine a target solution plane in the solution space, and determine a first target vector and a second target vector in the solution space; scan the target solution plane based on the first target vector and the second target vector to obtain a target Pareto solution of each objective function in the preset function set.

[0437] Optionally, in some embodiments, the scheduling unit 13 may be used to: determine, in the solution space, a plurality of boundary points corresponding to a solution set of a preset function set; and determine a target solution plane based on the plurality of boundary points.

[0438] Optionally, in some embodiments, the multiple boundary points include a first boundary point, a second boundary point, and a third boundary point; the scheduling unit 13 can be used to: determine a first initial vector, a second initial vector, and a third initial vector in the solution space, the first initial vector points from the origin of the solution space to the first boundary point, the second initial vector points from the origin to the second boundary point, and the third initial vector points from the origin to the third boundary point; determine a first target vector in the solution space based on the first initial vector and the second initial vector; determine a second target vector in the solution space based on the first initial vector and the third initial vector.

[0439] Optionally, in some embodiments, the scheduling unit 13 can be used to: obtain a scanning step size and multiple scanning weights, the sum of the multiple scanning weights is 1; based on the scanning step size, multiple scanning weights, the first target vector and the second target vector, scan the target solution plane to obtain the target Pareto solution of each target function in the preset function set.

[0440] Optionally, in some embodiments, the scheduling unit 13 can be used to: scan the target solution plane based on the scanning step size, multiple scanning weights, the first target vector and the second target vector to obtain the initial Pareto solution set of each objective function in the preset function set; and screen the initial Pareto solution set of each objective function respectively to obtain the target Pareto solution of each objective function.

[0441] Optionally, in some embodiments, the scheduling unit 13 can be used to: screen the initial Pareto solution set of each objective function based on a preset Pareto filter to obtain a first Pareto solution of each objective function; and determine a target Pareto solution of each objective function based on the first Pareto solution of each objective function.

[0442] Optionally, in some embodiments, the scheduling unit 13 can be used to: respectively determine the crowding distance of the initial Pareto solution in the initial Pareto solution set of each objective function; based on the crowding distance of the initial Pareto solution in the initial Pareto solution set of each objective function, respectively screen the initial Pareto solution set of each objective function to obtain the second Pareto solution of each objective function; based on the second Pareto solution of each objective function, determine the target Pareto solution of each objective function.

[0443] The effects that can be achieved by this embodiment can be found in the relevant embodiments of the above-mentioned power dispatching method under disasters, which will not be repeated here.

[0444] For the specific limitations of the above-mentioned power dispatching system under disasters, please refer to the limitations of the power dispatching method under disasters in the above text, which will not be repeated here. Each unit in the above-mentioned power dispatching system under disasters can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned units.

[0445] In one embodiment, a computer device is provided, the internal structure diagram of which can be as follows: Fig.28 As shown. The computer device includes a processor, a memory, a network interface and a database connected via 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for power dispatching under disasters as described above can be implemented. It includes: a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, any step in the method for power dispatching under disasters as described above is implemented.

[0446] 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, any step in the above disaster power dispatching method can be implemented.

[0447] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0448] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0449] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the instructions in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0450] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0451] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0452] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for power dispatching under disaster conditions, characterized in that: include: Constructing a meteorological model of a target meteorological disaster, and obtaining meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located based on the meteorological model of the target meteorological disaster; Obtaining line fault parameters of a target structure in the power grid affected by the target meteorological disaster under the action of the meteorological characteristic parameters, and obtaining output parameters of a target power generation equipment in the power grid affected by the target meteorological disaster under the action of the meteorological characteristic parameters; The target power flow parameters of the power grid are determined based on the line fault parameters and the output parameters, the target Pareto solutions of each target function in a preset function set are determined based on the target power flow parameters and a preset normal constraint algorithm, and the power grid is dispatched according to the target Pareto solutions of each target function.

2. The method according to claim 1, characterized in that The step of constructing a meteorological model for a target meteorological disaster includes: Acquire meteorological data of the area where the power grid is located, and extract original meteorological parameters of the target meteorological disaster from the meteorological data; A meteorological model of the target meteorological disaster is constructed based on the original meteorological parameters.

3. The method according to claim 2, characterized in that The target meteorological disaster is a typhoon disaster; and constructing a meteorological model for the target meteorological disaster based on the original meteorological parameters includes: Determine the micro-topography attenuation parameters of the area corresponding to the typhoon disaster; A meteorological model of the target meteorological disaster is constructed based on the original meteorological parameters and micro-topography attenuation parameters.

4. The method according to claim 3, characterized in that The meteorological model of the target meteorological disaster is expressed as: The original meteorological parameters include: c 、v m 、r m , b, α, z0, χ, s, the micro-topography attenuation parameters include u, ΔH(t), ΔX; v(r,θ,z,t) is the wind speed of any target point in the wind field of the typhoon disaster, r is the distance between the center of the typhoon disaster and the target point, θ is the angle between the target point and the x-axis of the reference coordinate system, z is the vertical height of the target point, r c is the maximum wind circle radius of the typhoon disaster, v m is the maximum wind speed at the center of the typhoon disaster, r m is the maximum wind circle radius of the typhoon disaster, R is the sum of r and r m where , b is the outer wind profile information of the typhoon disaster, α is the vertical attenuation coefficient of the typhoon disaster, z0 is the horizontal reference height of the target point, χ is the wind circle coefficient of the typhoon disaster, s is the asymmetry factor of the typhoon disaster, u is the first wind speed attenuation coefficient caused by the influence of building density in the axial direction, ΔH(t) is the second wind speed attenuation coefficient caused by the change of the urban underlying surface on the horizontal wind speed of the mobile time-varying typhoon, ΔX is the overall attenuation contribution rate of the wind field caused by urbanization, and t is the time period calculated with the typhoon landing as the initial moment.

5. The method according to claim 1, characterized in that The obtaining of line fault parameters of a target structure in the power grid affected by the target meteorological disaster under the action of the meteorological characteristic parameters includes: Acquire a first meteorological characteristic load of a target structure in the power grid affected by the target meteorological disaster in the target meteorological disaster, where the first meteorological characteristic load is a meteorological characteristic critical value at which a corresponding target structure fails in the target meteorological disaster; Based on the meteorological characteristic parameters, determining a second meteorological characteristic load of the target structure in the target meteorological disaster; Determining a line fault parameter of the target structure under the action of the meteorological characteristic parameters based on a first meteorological characteristic load and a second meteorological characteristic load of the target structure; Based on the line fault parameters of the target structure under the action of the meteorological characteristic parameters, the line fault parameters of the power grid under the action of the meteorological characteristic parameters are determined.

6. The method according to claim 1, characterized in that The target meteorological disaster is a typhoon disaster, and the meteorological characteristic parameters include μ v , δ v , v, the line fault parameters of the target structure under the action of the meteorological characteristic parameters include P, and the line fault parameters of the target structure under the action of the meteorological characteristic parameters are calculated as follows: Where P is the line failure rate corresponding to the target structure, v is the wind speed at the location of the target structure, μ v is the mean of the design wind speed of the transmission line corresponding to the target structure, δ v is the standard deviation of wind resistance; The line fault parameters of the power grid under the action of the meteorological characteristic parameters include P k , the calculation formula of the line fault parameters of the power grid under the action of the meteorological characteristic parameters is as follows: Among them, P k is the line failure rate corresponding to the k-th level line in the power grid.

7. The method according to claim 1, characterized in that The obtaining of output parameters of target power generation equipment in the power grid affected by the target meteorological disaster under the action of the meteorological characteristic parameters includes: Acquire a first meteorological characteristic threshold and a second meteorological characteristic threshold of a target power generation equipment in the power grid affected by the target meteorological disaster, wherein the first meteorological characteristic threshold is a meteorological characteristic critical value at which the corresponding target power generation equipment is shut down in the target meteorological disaster, and the second meteorological characteristic threshold is a meteorological characteristic critical value at which the corresponding target power generation equipment is damaged in the target meteorological disaster; Based on the first meteorological characteristic threshold and the second meteorological characteristic threshold of the target power generation equipment, an output parameter of the target power generation equipment under the action of the meteorological characteristic parameter is determined.

8. The method according to claim 1, characterized in that The target power generation equipment includes a wind turbine, and the meteorological characteristic parameters include The calculation formula of the output parameter of the target power generation equipment under the action of the meteorological characteristic parameters is as follows: Among them, t is the time, is the maximum output of the wind turbine generator at time t under the typhoon disaster, and the coordinates of the wind turbine generator in the power grid are (x, y), is the maximum wind speed that the wind turbine can bear, is the cut-out wind speed of the wind turbine, is the real-time wind speed at the location of the wind turbine; is the maximum wind speed experienced by the wind turbine up to time t; c is the wind energy utilization coefficient, and k is the exponential decay rate of the wind turbine.

9. The method according to claim 1, characterized in that: The determining the target power flow parameter of the power grid based on the line fault parameter and the output parameter comprises: Determining a target parameter combination based on the line fault parameter and the output parameter; Based on the target parameter combination and the mapping relationship between the parameter combination and the power flow parameter, the target power flow parameter of the power grid is determined.

10. The method according to claim 9, characterized in that The mapping relationship between the parameter combination and the power flow parameters is expressed as a target power flow model, and the target power flow model is expressed as: v j =v i -2(R ij P ij +X ij Q ij )+(R ij 2 +X ij 2 )i ij Among them, G j , B j Respectively represent the jth equality constraint and inequality constraint; P ij , Q ij They represent the active power and reactive power flowing from node i to node j in the power grid respectively; P j , Q j They represent the active power and reactive power injected into the grid at node j respectively; R ij , X ij Respectively represent the resistance and impedance of the branch between node i and node j in the power grid; i ij is the square of the current amplitude on the branch between node i and node j in the power grid; v ij is the square of the voltage amplitude on the branch between node i and node j in the power grid; v j is the square of the voltage amplitude at node j in the power grid.

11. The method according to claim 1, characterized in that: The determining of the target Pareto solution of each target function in the preset function set based on the target power flow parameter and the preset normal constraint algorithm includes: Determine, from the preset function set, an objective function corresponding to the first dimension, an objective function corresponding to the second dimension, and an objective function corresponding to the third dimension; Based on the target power flow parameters, performing single-objective optimization solutions on the target function corresponding to the first dimension, the target function corresponding to the second dimension, and the target function corresponding to the third dimension, respectively, to obtain a solution set of the preset function set; Constructing a solution space of the preset function set based on the solution set of the preset function set, the first dimension, the second dimension, and the third dimension; determining a target solution plane in the solution space, and determining a first target vector and a second target vector in the solution space; The target solution plane is scanned based on the first target vector and the second target vector to obtain a target Pareto solution of each target function in the preset function set.

12. The method according to claim 11, characterized in that The step of determining a target solution plane in the solution space comprises: In the solution space, determining a plurality of boundary points corresponding to the solution set of the preset function set; Based on the plurality of boundary points, a target solution plane is determined.

13. The method according to claim 12, characterized in that The plurality of boundary points include a first boundary point, a second boundary point, and a third boundary point; and determining a first target vector and a second target vector in the solution space includes: In the solution space, determine a first initial vector, a second initial vector, and a third initial vector, wherein the first initial vector points from the origin of the solution space to the first boundary point, the second initial vector points from the origin to the second boundary point, and the third initial vector points from the origin to the third boundary point; Determine a first target vector in the solution space based on the first initial vector and the second initial vector; A second target vector is determined in the solution space based on the first initial vector and the third initial vector.

14. The method according to claim 11, characterized in that The scanning of the target solution plane based on the first target vector and the second target vector to obtain a target Pareto solution of each target function in the preset function set includes: Obtaining a scanning step length and a plurality of scanning weights, wherein the sum of the plurality of scanning weights is 1; Based on the scanning step size, the multiple scanning weights, the first target vector and the second target vector, the target solution plane is scanned to obtain a target Pareto solution of each target function in the preset function set.

15. The method according to claim 14, characterized in that The scanning of the target solution plane based on the scanning step size, the multiple scanning weights, the first target vector, and the second target vector to obtain a target Pareto solution of each target function in the preset function set includes: Scanning the target solution plane based on the scanning step size, the multiple scanning weights, the first target vector, and the second target vector to obtain an initial Pareto solution set of each target function in the preset function set; The initial Pareto solution sets of each objective function are screened respectively to obtain the target Pareto solution of each objective function.

16. The method according to claim 15, characterized in that The initial Pareto solution sets of the objective functions are screened respectively to obtain the target Pareto solutions of the objective functions, including: Based on a preset Pareto filter, the initial Pareto solution set of each objective function is screened to obtain a first Pareto solution of each objective function; Based on the first Pareto solution of each of the objective functions, a target Pareto solution of each of the objective functions is determined.

17. The method according to claim 15, characterized in that The initial Pareto solution sets of the objective functions are screened respectively to obtain the target Pareto solutions of the objective functions, including: Determine the crowding distance of the initial Pareto solution set of each objective function respectively; Based on the crowding distance of the initial Pareto solution in the initial Pareto solution set of each objective function, the initial Pareto solution set of each objective function is screened to obtain a second Pareto solution of each objective function; Based on the second Pareto solution of each of the objective functions, a target Pareto solution of each of the objective functions is determined.

18. A disaster power dispatching system, characterized in that: include: A first acquisition unit is used to construct a meteorological model of a target meteorological disaster, and acquire meteorological characteristic parameters of the target meteorological disaster in the area where the power grid is located based on the meteorological model of the target meteorological disaster; A second acquisition unit is used to acquire line fault parameters of a target structure in the power grid affected by the target meteorological disaster under the action of the meteorological characteristic parameters, and to acquire output parameters of a target power generation equipment in the power grid affected by the target meteorological disaster under the action of the meteorological characteristic parameters; A dispatching unit is used to determine the target power flow parameters of the power grid based on the line fault parameters and the output parameters, determine the target Pareto solution of each of the target functions in a preset function set based on the target power flow parameters and a preset normal constraint algorithm, and perform power dispatch on the power grid according to the target Pareto solution of each of the target functions.

19. A computer device comprising: The method comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method according to any one of claims 1 to 17 when executing the computer program.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 17 are implemented.

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