Power distribution network scheduling method, equipment, medium and product

Abstract

The invention relates to the technical field of power system automation control, in particular to a power distribution network dispatching method and device, a medium and a product, and the method comprises the steps: carrying out the load prediction based on historical demand data, real-time load data and auxiliary prediction data, and determining the load prediction information. And then, carrying out supply and demand analysis based on each power generation plan and load prediction information, and determining a supply and demand analysis result. And when the supply and demand analysis result is short of supply, carrying out optimization scheduling analysis based on each power generation plan and the load prediction information, determining emergency scheduling information, sending the emergency scheduling information to the target power resource, and controlling the target power resource to carry out electric energy scheduling according to the emergency scheduling information. The formulation and implementation of the emergency scheduling information help to optimize the configuration of power resources, avoid the problem of power failure caused by insufficient power supply, improve the emergency response capability of the power system, and improve the quality and stability of power supply.

Description

Technical Field

[0001] The present application relates to the technical field of power system automation control, and in particular to a distribution network dispatching method, equipment, medium and product. Background Art

[0002] With the rapid development of the global economy and the significant improvement in people's living standards, the demand for electricity has shown a trend of sustained growth. As a key component of the power system, the distribution network plays a bridging role between power production and consumption. At the same time, the effectiveness of the distribution network dispatching and management is directly related to the stability, safety and economy of the entire power system.

[0003] The existing distribution network dispatching methods mainly rely on manual experience and fixed dispatching strategies, which are difficult to adapt to the complex changes in power load and the dynamic adjustment of the power grid structure. This will lead to unstable power supply and even cause serious problems such as power outages, affecting the quality and stability of power supply.

[0004] Therefore, how to solve the above technical problems is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0005] The purpose of this application is to provide a distribution network scheduling method, device, medium and product to solve at least one of the above technical problems.

[0006] The above invention objectives of the present application are achieved through the following technical solutions: In a first aspect, the present application provides a distribution network dispatching method, which adopts the following technical solution: A distribution network dispatching method, comprising: Acquire real-time load data, auxiliary prediction data and historical demand data of the distribution network, perform load prediction based on the historical demand data, the real-time load data and the auxiliary prediction data, and determine load prediction information; Obtaining a power generation plan corresponding to each power resource in the distribution network, performing supply and demand analysis based on each of the power generation plans and the load forecast information, and determining a supply and demand analysis result; When the supply and demand analysis result is that supply exceeds demand, an optimized scheduling analysis is performed based on each of the power generation plans and the load forecast information, emergency scheduling information is determined, and the emergency scheduling information is sent to the target power resources, and the target power resources are controlled to perform power scheduling according to the emergency scheduling information.

[0007] By adopting the above technical scheme, the real-time load data, auxiliary prediction data and historical demand data of the distribution network are obtained, and load prediction is performed based on the historical demand data, real-time load data and auxiliary prediction data to determine the load prediction information. The load prediction helps the power department to reasonably arrange the power generation plan, optimize the resource allocation, and improve the stability and safety of the power supply of the power grid. Then, the power generation plan corresponding to each power resource in the distribution network is obtained, and the supply and demand analysis is performed based on each power generation plan and load prediction information to determine the supply and demand analysis results, and the supply and demand analysis of electric energy is performed, which helps to improve the reliability of power supply and ensure the stable operation of the power system during peak hours or emergency situations. When the supply and demand analysis result is that the supply is insufficient to meet the demand, the optimization dispatch analysis is performed based on each power generation plan and load prediction information to determine the emergency dispatch information, and the emergency dispatch information is sent to the target power resource to control the target power resource to dispatch electric energy according to the emergency dispatch information. The formulation and implementation of emergency dispatch information helps to optimize the configuration of power resources, avoid power outages caused by insufficient power supply, and improve the emergency response capability of the power system, so that it can respond to various emergencies in a short time, and improve the quality and stability of power supply.

[0008] In a preferred example, the present application may be further configured as follows: performing optimization dispatch analysis based on each of the power generation plans and the load forecast information to determine emergency dispatch information includes: Obtaining a scheduling constraint condition, and based on the scheduling constraint condition, prioritizing each of the power resources to determine a scheduling sequence of the power resources; Acquire power resource information corresponding to each of the power resources in the distribution network, perform incremental power analysis based on each of the power resource information and the power generation plan, and determine the additional power corresponding to each of the power resources; Based on the power resource scheduling sequence and the additional power corresponding to each of the power resources, an optimization scheduling analysis is performed to determine emergency scheduling information.

[0009] In a preferred example, the present application may be further configured as follows: after performing optimization scheduling analysis based on the power resource scheduling sequence and the maximum additional power corresponding to each power resource and determining the emergency scheduling information, it also includes: Acquire multi-dimensional data corresponding to the distribution network, and construct a model based on the multi-dimensional data to obtain a virtual model of the distribution network, wherein the multi-dimensional data includes: grid structure data, load distribution data, and power generation resource data; Acquire meteorological data corresponding to the emergency dispatch period, perform simulation test based on the load forecast information, the meteorological data, the emergency dispatch information and the distribution network virtual model, and obtain simulation test results; When the simulation test result is passed, the emergency dispatch information is sent to the target power resource; when the simulation test result is failed, a dispatch information abnormality warning is generated to facilitate re-execution of the optimized dispatch analysis.

[0010] In a preferred example, the present application may be further configured as follows: performing incremental power analysis based on each of the power resource information and the power generation plan to determine the additional power corresponding to each of the power resources includes: Calculate theoretical power generation capacity based on the installed capacity in the power resource information to determine the theoretical power generation capacity; Performing equipment health estimation based on the equipment status information in the power resource information to determine the equipment health coefficient, and obtaining meteorological data corresponding to the emergency dispatch period, performing environmental impact estimation based on the meteorological data to determine the environmental impact coefficient; Calculate the actual power generation capacity based on the theoretical power generation capacity, the equipment health coefficient and the environmental impact coefficient to determine the actual power generation capacity; Incremental power analysis is performed based on the actual power generation capacity corresponding to each of the power resources and the power generation plan to determine the additional power corresponding to each of the power resources.

[0011] In a preferred example, the present application may be further configured as follows: performing demand forecasting based on the historical demand data, the real-time load data and the auxiliary forecasting data to determine load forecasting information includes: Performing data integration based on the historical demand data, the real-time load data and the auxiliary forecast data to obtain target demand data; Extracting a first feature based on the target demand data to determine a linear feature and a seasonal feature, and performing demand forecasting based on the linear feature and the seasonal feature to determine first load forecasting information; Performing a second feature extraction based on the target demand data to determine a nonlinear feature, and performing demand forecasting based on the nonlinear feature to determine second load forecasting information; A combined prediction is performed based on the first load prediction information and the second load prediction information to determine load prediction information.

[0012] In a preferred example, the present application may be further configured as follows: performing supply and demand analysis based on each of the power generation plans and the load forecast information to determine the supply and demand analysis result includes: Performing power supply calculation based on each of the power generation plans to determine power supply information; Based on the supply and demand analysis of the electric energy supply information and the load forecast information, a supply and demand analysis result is determined, wherein the supply and demand analysis result includes: oversupply, shortage of supply and supply and demand balance.

[0013] In a second aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned distribution network scheduling method.

[0014] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute the above-mentioned distribution network scheduling method.

[0015] In a fourth aspect, the present application provides a computer program product, which adopts the following technical solution: A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the distribution network scheduling method described above is implemented.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: The real-time load data, auxiliary forecast data and historical demand data of the distribution network are obtained, and load forecasting is performed based on the historical demand data, real-time load data and auxiliary forecast data to determine the load forecast information. Load forecasting helps the power department to reasonably arrange the power generation plan, optimize resource allocation, and improve the stability and security of the power supply of the power grid. Then, the power generation plan corresponding to each power resource in the distribution network is obtained, and supply and demand analysis is performed based on each power generation plan and load forecast information to determine the supply and demand analysis results. The supply and demand analysis of electric energy is performed, which helps to improve the reliability of power supply and ensure the stable operation of the power system during peak hours or emergency situations. When the supply and demand analysis result is that the supply is insufficient to meet the demand, an optimization dispatch analysis is performed based on each power generation plan and load forecast information to determine the emergency dispatch information, and the emergency dispatch information is sent to the target power resource to control the target power resource to dispatch electric energy according to the emergency dispatch information. The formulation and implementation of emergency dispatch information helps to optimize the configuration of power resources, avoid power outages caused by insufficient power supply, and improve the emergency response capability of the power system, enabling it to respond to various emergencies in a short time, and improve the quality and stability of power supply.

[0017] In the power system of the distribution network, there are different types of power generation resources, and different power generation resources have different power generation characteristics and cost-effectiveness. Based on the scheduling constraints, each power resource is prioritized and the power resource scheduling sequence is determined, so that the power can select appropriate power generation resources during the scheduling process and optimize the configuration of power resources. Then, the power resource information corresponding to each power resource in the distribution network is obtained, and the incremental power analysis is performed based on each power resource information and the power generation plan to determine the additional power corresponding to each power resource. Furthermore, based on the power resource scheduling sequence and the additional power corresponding to each power resource, the optimized scheduling analysis is performed to determine the emergency scheduling information. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a distribution network dispatching method according to one embodiment of the present application; Figure 2 It is a structural schematic diagram of a distribution network dispatching device according to one embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following combination Figures 1 to 3 This application is described in further detail.

[0020] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0021] In order to make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the data related to the object is involved in the embodiment of the present application, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in accordance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiment, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiment also needs to be implemented with the authorization and consent of the object.

[0022] In addition, the term "and / or" in this article 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 represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0023] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0024] The embodiment of the present application provides a distribution network dispatching method, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. Figure 1 As shown, the method includes step S101, step S102 and step S103, wherein: Step S101: Acquire real-time load data, auxiliary prediction data and historical demand data of the distribution network, perform load prediction based on the historical demand data, real-time load data and auxiliary prediction data, and determine load prediction information.

[0025] For the embodiment of the present application, in the process of power dispatching of the distribution network, load forecasting is an important prerequisite for power system planning, which helps the power department to reasonably arrange power generation plans, optimize resource allocation, and improve the stability and safety of power supply of the power grid. Therefore, the real-time load data of the distribution network is obtained, and the real-time load data reflects the current operating status and power demand of the distribution network, which can not only timely discover abnormal changes in power demand, but also provide important data basis for load forecasting. However, in the actual power consumption process, many factors will affect the power demand, such as meteorological factors, holiday factors, activity factors, economic factors, etc. The various factors that affect the power demand are recorded as auxiliary forecasting data, which helps to fully understand the changing laws and influencing factors of power demand, thereby improving the accuracy and reliability of load forecasting. At the same time, the historical demand data is the load power consumption data of the distribution network in the past period of time. The historical demand data is an important basis for load forecasting. By analyzing the historical demand data, the long-term change trend and seasonal characteristics of power demand can be grasped, providing an important reference basis for load forecasting.

[0026] Furthermore, load forecasting is performed based on historical demand data, real-time load data and auxiliary forecasting data to determine load forecasting information. The load forecasting information characterizes the demand for power load in a future period of time, including but not limited to: total load, peak load, load curve, load growth rate and load volatility in the forecasting period. There are many ways to forecast load, and the embodiments of the present application are no longer limited. In one achievable method, data integration is performed based on historical demand data, real-time load data and auxiliary forecasting data to obtain target demand data; a first feature extraction is performed based on the target demand data to determine linear features and seasonal features, and demand forecasting is performed based on the linear features and seasonal features to determine the first load forecasting information; a second feature extraction is performed based on the target demand data to determine nonlinear features, and demand forecasting is performed based on the nonlinear features to determine the second load forecasting information; combined forecasting is performed based on the first load forecasting information and the second load forecasting information to determine the load forecasting information. In the process of executing load forecasting, the linear features, nonlinear features and seasonal features of the power demand data are comprehensively considered, and a method of combining multiple models for forecasting is used to perform load forecasting, thereby improving the accuracy of load forecasting.

[0027] Step S102: Obtain a power generation plan corresponding to each power resource in the distribution network, perform supply and demand analysis based on each power generation plan and load forecast information, and determine a supply and demand analysis result.

[0028] For the embodiments of the present application, the demand for electricity in the distribution network will be affected by many factors. As the demand for electricity continues to increase, there may be an imbalance in the supply and demand of electricity. In order to ensure the stable operation of the power system and avoid economic losses caused by power outages or excess power, performing supply and demand analysis of electricity will help improve the reliability of power supply and ensure the stable operation of the power system during peak hours or in emergency situations.

[0029] Specifically, the power generation plan corresponding to each power resource in the distribution network is obtained. The power generation plan should include the expected power generation in each time period (for example, hour, day, week, month), and supply and demand analysis is performed based on each power generation plan and load forecast information to determine the supply and demand analysis results. There are many specific implementation methods for supply and demand analysis, and the embodiments of the present application are no longer limited. In one achievable method, the power supply calculation is performed based on each power generation plan to determine the power supply information; based on the power supply information and the load forecast information supply and demand analysis, the supply and demand analysis results are determined, wherein the supply and demand analysis results include: oversupply, undersupply and supply and demand balance. In another achievable method, a supply and demand analysis model is established based on the power generation plan and load forecast information, and the supply and demand analysis model can reflect the supply and demand situation in each time period and each region in the power system. In the supply and demand analysis model, the power supply and demand in each time period and each region are calculated, the power supply and demand are compared, and the supply and demand balance state is determined, that is, oversupply, undersupply or supply and demand balance.

[0030] Step S103: When the supply and demand analysis result is that supply exceeds demand, an optimization scheduling analysis is performed based on each power generation plan and load forecast information, emergency scheduling information is determined, and the emergency scheduling information is sent to the target power resources to control the target power resources to perform power scheduling according to the emergency scheduling information.

[0031] For the embodiment of the present application, when the supply and demand analysis result is that the supply is insufficient to meet the demand, it indicates that the power supply of the power system cannot meet the demand side's usage requirements. Therefore, based on each power generation plan and load forecast information, an optimized scheduling analysis is performed to determine the emergency scheduling information. The formulation and implementation of the emergency scheduling information helps to optimize the configuration of power resources, avoid power outages caused by insufficient power supply, and improve the emergency response capability of the power system, so that it can respond to various emergencies in a short time, and improve the quality and stability of power supply. There are many specific implementation methods for optimized scheduling analysis, which are no longer limited in the embodiment of the present application. In one achievable method, the scheduling constraint conditions are obtained, and based on the scheduling constraint conditions, each power resource is prioritized to determine the power resource scheduling sequence; the power resource information corresponding to each power resource in the distribution network is obtained, and the incremental power analysis is performed based on each power resource information and the power generation plan to determine the additional power corresponding to each power resource; the optimized scheduling analysis is performed based on the power resource scheduling sequence and the additional power corresponding to each power resource to determine the emergency scheduling information.

[0032] When the supply and demand analysis result shows that supply exceeds demand, it usually means that there is an oversupply of electricity, and it is necessary to adjust the power generation plan, reduce unnecessary power generation, or consider storing or selling excess electricity to other areas with demand. When the supply and demand analysis result shows that supply and demand are balanced, it indicates that the power supply is in a stable state. We should continuously monitor changes in supply and demand to ensure the stable operation of the power system.

[0033] It can be seen that in the embodiment of the present application, the real-time load data, auxiliary prediction data and historical demand data of the distribution network are obtained, and the load prediction is performed based on the historical demand data, the real-time load data and the auxiliary prediction data to determine the load prediction information. The load prediction helps the power department to reasonably arrange the power generation plan, optimize the resource allocation, and improve the stability and safety of the power supply of the power grid. Then, the power generation plan corresponding to each power resource in the distribution network is obtained, and the supply and demand analysis is performed based on each power generation plan and load prediction information to determine the supply and demand analysis results, and the supply and demand analysis of electric energy is performed, which helps to improve the reliability of power supply and ensure the stable operation of the power system during peak hours or in emergency situations. When the supply and demand analysis result is that the supply is insufficient to meet the demand, the optimization scheduling analysis is performed based on each power generation plan and load prediction information to determine the emergency scheduling information, and the emergency scheduling information is sent to the target power resource, and the target power resource is controlled to perform power scheduling according to the emergency scheduling information. The formulation and implementation of emergency scheduling information helps to optimize the configuration of power resources, avoid power outages caused by insufficient power supply, and improve the emergency response capability of the power system, so that it can respond to various emergencies in a short time, and improve the quality and stability of power supply.

[0034] Furthermore, in order to select appropriate power generation resources and optimize the configuration of power resources during the scheduling process, in the embodiment of the present application, an optimization scheduling analysis is performed based on each power generation plan and load forecast information to determine the emergency scheduling information, including: Obtain scheduling constraints, prioritize each power resource based on the scheduling constraints, and determine the power resource scheduling sequence; Obtain the power resource information corresponding to each power resource in the distribution network, perform incremental power analysis based on each power resource information and the power generation plan, and determine the additional power corresponding to each power resource; Based on the power resource dispatch sequence and the additional power corresponding to each power resource, an optimized dispatch analysis is performed to determine the emergency dispatch information.

[0035] For the embodiment of the present application, in the power system of the distribution network, there are different types of power generation resources, such as wind power generation, solar power generation, fossil energy power generation, etc. Different power generation resources have different power generation characteristics and cost-effectiveness. Therefore, each power resource is prioritized according to the scheduling constraints to facilitate the selection of appropriate power generation resources during the scheduling process and optimize the configuration of power resources.

[0036] Specifically, the dispatch constraint condition is obtained, which is a dispatch constraint condition determined by the dispatch management personnel based on multiple factors such as the area where the distribution network is located and the time period where the dispatch is located, for example, minimizing the cost of power generation, maximizing the efficiency of energy utilization, and reducing greenhouse gas emissions. Of course, the corresponding dispatch constraint condition can also be set according to the actual situation. Then, based on the power generation characteristics and dispatch constraint conditions of each power resource, each power resource is prioritized to determine the power resource dispatch sequence, wherein the power resource that is closer to the dispatch constraint condition is closer to the front position. At the same time, the power resource information corresponding to each power resource in the distribution network is obtained, and the power resource information includes but is not limited to: installed capacity, power generation type, power generation efficiency, equipment status information, etc., and incremental power analysis is performed based on each power resource information and power generation plan to determine the additional power corresponding to each power resource, wherein the additional power represents the additional power that can be supplied by increasing the work of the power resource on the basis of the original power generation plan. The specific implementation method of incremental power analysis is no longer limited in the embodiments of the present application. In one achievable method, the theoretical power generation capacity is calculated based on the installed capacity in the power resource information to determine the theoretical power generation capacity; the equipment health is estimated based on the equipment status information in the power resource information to determine the equipment health coefficient, and the meteorological data corresponding to the emergency dispatch period is obtained, and the environmental impact is estimated based on the meteorological data to determine the environmental impact coefficient; the actual power generation capacity is calculated based on the theoretical power generation capacity, the equipment health coefficient and the environmental impact coefficient to determine the actual power generation capacity; the incremental power analysis is performed based on the actual power generation capacity and the power generation plan corresponding to each power resource to determine the additional power corresponding to each power resource.

[0037] Then, according to the order of the power resource scheduling sequence, the additional power corresponding to the power resource is selected one by one, and the additional power is combined with the original power generation plan to determine whether the power demand can be met. If it cannot meet the demand, the additional power corresponding to the next power resource is selected until the power supply capacity can meet the power demand. The power resource selected from the power resource scheduling sequence is recorded as the target power resource, and the power generation plan of the target power resource is adjusted so that the target power resource can supply more additional power on the original power generation plan, that is, the target power resource that needs power supply adjustment and the corresponding adjusted power generation plan are recorded in the emergency scheduling information.

[0038] It can be seen that in the embodiment of the present application, in the power system of the distribution network, there are different types of power generation resources, and different power generation resources have different power generation characteristics and cost-effectiveness. Based on the scheduling constraints, each power resource is prioritized and the power resource scheduling sequence is determined, so that the power can select appropriate power generation resources during the scheduling process and optimize the configuration of power resources. Then, the power resource information corresponding to each power resource in the distribution network is obtained, and the incremental power analysis is performed based on each power resource information and the power generation plan to determine the additional power corresponding to each power resource. Furthermore, based on the power resource scheduling sequence and the additional power corresponding to each power resource, the optimized scheduling analysis is performed to determine the emergency scheduling information.

[0039] Furthermore, in order to avoid failures or accidents in actual operation and improve the safety of emergency dispatch of the power grid, in the embodiment of the present application, after the emergency dispatch information is determined, the optimization dispatch analysis is performed based on the power resource dispatch sequence and the maximum additional power corresponding to each power resource, and the following is also included: Acquire multi-dimensional data corresponding to the distribution network, build a model based on the multi-dimensional data, and obtain a virtual model of the distribution network, wherein the multi-dimensional data includes: grid structure data, load distribution data, and power generation resource data; Obtain meteorological data corresponding to the emergency dispatch period, perform simulation tests based on load forecast information, meteorological data, emergency dispatch information and distribution network virtual model, and obtain simulation test results; When the simulation test result is passed, the emergency dispatch information is sent to the target power resource; when the simulation test result is failed, a dispatch information abnormality warning is generated to facilitate re-execution of the optimized dispatch analysis.

[0040] For the embodiment of the present application, the multi-dimensional data corresponding to the distribution network is obtained, and the multi-dimensional data includes but is not limited to: grid structure data, load distribution data, and power generation resource data, wherein the grid structure data is used to characterize the topological structure information of the distribution network, including the connection relationship and parameters of equipment such as substations, lines, switches, etc.; the load distribution data is used to record the load data of each node in the distribution network, that is, the historical load record; the power generation resource data is used to record the power generation capacity, power generation efficiency and other parameters of distributed power sources (for example, solar energy, wind energy, etc.). Then, the multi-dimensional data from different sources are standardized to ensure the comparability and compatibility between the data, and the data is converted into a format suitable for model construction, so as to facilitate the subsequent construction of the virtual model of the distribution network. Then, the model is constructed based on the multi-dimensional data to obtain a virtual model of the distribution network, which can accurately reflect the key information such as the structure, load distribution and power generation resources of the distribution network. For model construction, according to the characteristics and requirements of the distribution network, a suitable modeling method and tool are selected, that is, a modeling method based on a physical model or a modeling method based on data drive can be adopted. Model building features that have an important impact on model building, such as the shape of the load curve, the volatility of power generation resources, etc., are extracted, and the model is trained based on multi-dimensional data and model building features to obtain the initial distribution network virtual model. Then, the initial distribution network virtual model is optimized, for example, by adjusting model parameters, using more advanced algorithms, etc., to improve the accuracy and generalization ability of the model, and the initial distribution network virtual model is verified using historical data, and the verified model is used as the distribution network virtual model. Constructing a distribution network virtual model through multi-dimensional data of the distribution network can more comprehensively reflect the actual operating status of the distribution network, which helps to quickly and accurately evaluate the response capability and stability of the distribution network during emergency dispatch periods.

[0041] Furthermore, through the meteorological data platform or API interface, the meteorological data corresponding to the emergency dispatch period is obtained according to the geographical location and time range, and the load forecast information, meteorological data, and emergency dispatch information are input into the distribution network virtual model for simulation testing, simulating the operation of the distribution network when the distribution network executes the emergency dispatch information under the conditions of the load forecast information and meteorological data. After the simulation test is completed, the execution effect of the emergency dispatch strategy is determined according to the simulation test parameters in the distribution network virtual model. If there is no power supply abnormality or local power outage, the simulation test result is determined to be passed; if there is a power supply abnormality or local power outage, the simulation test result is determined to be failed. When the simulation test result is passed, the emergency dispatch information is sent to the target power resource; when the simulation test result is failed, a dispatch information abnormality warning is generated, and the dispatch information abnormality warning is used to remind the dispatch management personnel to re-execute the optimization dispatch analysis. Combining meteorological data, load forecast information and emergency dispatch information, simulation testing can simulate the operation status of the distribution network under different emergency scenarios. The simulation test can discover potential dispatch problems in advance, thereby avoiding failures or accidents in actual operation, and improving the safety of power grid emergency dispatch.

[0042] It can be seen that in an embodiment of the present application, multi-dimensional data corresponding to the distribution network is obtained, and a model is constructed based on the multi-dimensional data to obtain a virtual model of the distribution network. Then, the meteorological data corresponding to the emergency dispatch period is obtained, and a simulation test is performed based on the load forecast information, meteorological data, emergency dispatch information and the distribution network virtual model to obtain a simulation test result. When the simulation test result is passed, the emergency dispatch information is sent to the target power resource; when the simulation test result is failed, an abnormal dispatch information warning is generated to facilitate re-execution of the optimized dispatch analysis. Combining meteorological data, load forecast information and emergency dispatch information, a simulation test is performed to simulate the operating status of the distribution network under different emergency scenarios. The simulation test can discover potential dispatch problems in advance, thereby avoiding failures or accidents in actual operation, and improving the safety of power grid emergency dispatch.

[0043] Furthermore, in order to ensure the stable operation of the power grid and the reliability of power supply, in the embodiment of the present application, incremental power analysis is performed based on each power resource information and power generation plan to determine the additional power corresponding to each power resource, including: Calculate theoretical power generation capacity based on installed capacity in power resource information to determine theoretical power generation capacity; Estimating equipment health based on equipment status information in power resource information, determining equipment health coefficient, obtaining meteorological data corresponding to the emergency dispatch period, estimating environmental impact based on meteorological data, and determining environmental impact coefficient; Calculate the actual power generation capacity based on the theoretical power generation capacity, equipment health factor and environmental impact factor to determine the actual power generation capacity; An incremental power analysis is performed based on the actual power generation capacity and power generation plan corresponding to each power resource to determine the additional power generated by each power resource.

[0044] For the embodiment of the present application, accurately evaluating the maximum power generation potential of each power resource under ideal conditions is helpful to provide a reliable theoretical basis for power generation planning and power dispatching. Therefore, the theoretical power generation capacity is calculated based on the installed capacity in the power resource information and the length of the emergency dispatch period to determine the theoretical power generation capacity, where the theoretical power generation capacity refers to the amount of electricity that the power resource can theoretically generate under ideal conditions in the future emergency dispatch period, and the installed capacity refers to the sum of the rated capacities of the engine groups in the power resource, which is the maximum active power that the engine group can output under standard operating conditions. For the theoretical power generation capacity calculation method, the theoretical power generation capacity = installed capacity * emergency dispatch period.

[0045] Then, the power resource information records the equipment status of the power equipment, including but not limited to: operating time, maintenance records, fault history, current operating status, etc., and obtains the pre-established equipment health assessment model, and uses the equipment health assessment model and equipment status information to quantitatively assess the overall health status of the equipment and determine the equipment health coefficient, where the equipment health coefficient is usually a value between 0 and 1, indicating the ratio of the health status of the equipment to a brand new device. The equipment health assessment model is built based on the technical parameters and performance indicators of the equipment, and the equipment health assessment model can comprehensively consider various status information of the power equipment.

[0046] At the same time, a pre-established environmental impact assessment model is obtained. The environmental impact assessment model is established based on the operating characteristics of power equipment and the impact mechanism of meteorological conditions, and can comprehensively consider the impact of meteorological factors on the operating efficiency, safety, stability and other aspects of power equipment. Therefore, the environmental impact assessment model and meteorological data are used to estimate the environmental impact and determine the environmental impact coefficient, which is a numerical value that quantifies the impact of the meteorological environment on the operation of power equipment. Furthermore, the actual power generation capacity is calculated based on the theoretical power generation capacity, equipment health coefficient and environmental impact coefficient to determine the actual power generation capacity, where the actual power generation capacity = theoretical power generation capacity * equipment health coefficient * environmental impact coefficient. Combining the theoretical power generation capacity, equipment health coefficient and environmental impact coefficient to calculate the actual power generation capacity can produce a more accurate and practical power generation capacity assessment result, which helps the power dispatching department to allocate power resources more reasonably and ensure the stable operation of the power grid and the reliability of power supply. Finally, an incremental power analysis is performed based on the actual power generation capacity and power generation plan corresponding to each power resource to determine the additional power corresponding to each power resource. The additional power represents the additional electric energy that can be supplied by increasing the work of the power resource on the basis of the original power generation plan, that is, the additional electric energy that can be supplied by the actual power generation capacity compared with the power generation plan.

[0047] It can be seen that in the embodiment of the present application, the theoretical power generation capacity is calculated based on the installed capacity in the power resource information, the theoretical power generation capacity is determined, and the maximum power generation potential of each power resource under ideal conditions is accurately evaluated, which helps to provide a reliable theoretical basis for power generation plan formulation and power dispatching. Then, the equipment health estimation is performed based on the equipment status information in the power resource information, the equipment health coefficient is determined, and the meteorological data corresponding to the emergency dispatch period is obtained, and the environmental impact estimation is performed based on the meteorological data to determine the environmental impact coefficient. Furthermore, the actual power generation capacity is calculated based on the theoretical power generation capacity, the equipment health coefficient and the environmental impact coefficient to determine the actual power generation capacity. Combining the theoretical power generation capacity, the equipment health coefficient and the environmental impact coefficient to calculate the actual power generation capacity can obtain a more accurate and practical power generation capacity evaluation result, which helps the power dispatching department to allocate power resources more reasonably and ensure the stable operation of the power grid and the reliability of power supply. Finally, the incremental power analysis is performed based on the actual power generation capacity and power generation plan corresponding to each power resource to determine the additional power corresponding to each power resource.

[0048] Furthermore, in order to improve the accuracy of load forecasting, in an embodiment of the present application, demand forecasting is performed based on historical demand data, real-time load data, and auxiliary forecasting data to determine load forecasting information, including: Integrate historical demand data, real-time load data and auxiliary forecast data to obtain target demand data; Extracting a first feature based on the target demand data to determine a linear feature and a seasonal feature, and forecasting demand based on the linear feature and the seasonal feature to determine first load forecast information; Extracting a second feature based on the target demand data to determine a nonlinear feature, and performing demand forecasting based on the nonlinear feature to determine second load forecasting information; A combined prediction is performed based on the first load prediction information and the second load prediction information to determine the load prediction information.

[0049] For the embodiments of the present application, in order to improve the accuracy of load forecasting, in the process of executing load forecasting, the linear characteristics, nonlinear characteristics and seasonal characteristics of the power demand data are comprehensively considered, and a method of combining predictions of multiple models is adopted to perform load forecasting, thereby providing the power system with accurate and reliable load forecasting information.

[0050] Specifically, data cleaning is performed based on historical demand data, real-time load data, and auxiliary forecast data. Data cleaning is used to check the integrity of the data, identify and correct outliers in the data, and convert the data into a consistent format and unit. Then, data fusion technology is used to integrate the historical demand data, real-time load data, and auxiliary forecast data after data cleaning, and merge them into a unified database to make the target demand data more complete, accurate, and reliable. Since electricity demand is often affected by seasonal factors, festive factors, etc., the electricity demand shows large fluctuations, and the target demand data usually includes data with linear characteristics and seasonal characteristics. Therefore, the first feature extraction is performed based on the target demand data to determine the linear characteristics and seasonal characteristics, wherein the linear characteristics refer to the characteristics that have a linear relationship with the electricity demand, and the seasonal characteristics refer to the characteristics related to the season in the target demand data. Furthermore, demand forecasting is performed based on the linear characteristics and seasonal characteristics to determine the first load forecasting information.

[0051] At the same time, there are many nonlinear relationships in actual electricity demand, such as the nonlinear correlation between economic activities and electricity demand. These nonlinear relationships cannot be accurately captured by simple linear models. Therefore, the second feature extraction is performed based on the target demand data to determine the nonlinear feature, and the demand forecast is performed based on the nonlinear feature to determine the second load forecast information. When performing demand forecasting for nonlinear features, a model suitable for nonlinear feature prediction can be selected, such as convolutional neural network, BP neural network, etc. Then, demand forecasting is performed based on nonlinear features to determine the second load forecast information. Finally, a combined forecast is performed based on the first load forecast information and the second load forecast information to determine the load forecast information. For the combined forecast, weighted average, Bayesian combination and other methods can be used to combine the two forecast results to obtain more accurate load forecast information.

[0052] It can be seen that in the embodiment of the present application, data integration is performed based on historical demand data, real-time load data and auxiliary forecast data to obtain target demand data. Then, a first feature extraction is performed based on the target demand data to determine linear features and seasonal features, and demand forecasting is performed based on the linear features and seasonal features to determine the first load forecasting information. Furthermore, a second feature extraction is performed based on the target demand data to determine nonlinear features, and demand forecasting is performed based on the nonlinear features to determine the second load forecasting information. Finally, a combined forecast is performed based on the first load forecasting information and the second load forecasting information to determine the load forecasting information. In order to improve the accuracy of load forecasting, in the process of executing load forecasting, the linear features, nonlinear features and seasonal features of the power demand data are comprehensively considered, and a method of combining multiple models for forecasting is used to perform load forecasting, thereby providing accurate and reliable load forecasting information for the power system.

[0053] Furthermore, in order to improve the operating efficiency of the entire power system and timely discover the imbalance between supply and demand in the power system, in an embodiment of the present application, supply and demand analysis is performed based on each power generation plan and load forecast information to determine the supply and demand analysis results, including: Perform power supply calculation based on each power generation plan to determine power supply information; Based on the supply and demand analysis of the power supply information and the load forecast information, the supply and demand analysis results are determined, wherein the supply and demand analysis results include: oversupply, shortage of supply and supply and demand balance.

[0054] For the embodiments of the present application, through supply and demand analysis, the supply and demand imbalance in the power system can be discovered in a timely manner, and based on the supply and demand analysis results, it helps to more reasonably allocate and dispatch electric energy, avoid waste and shortage of electric power resources, and improve the operating efficiency of the entire power system.

[0055] Specifically, the power generation plan includes the expected power generation of power resources in each time period (for example, hour, day, week, month), and the expected power generation corresponding to each power resource is recorded and summarized to obtain the total power supply information of the distribution network. Then, preprocessing is performed based on the power supply information and load forecast information to ensure that the two maintain consistency in time scale and data format, and the power supply diagram and power consumption diagram corresponding to the power supply information and load forecast information are drawn, wherein the horizontal axis of the power supply diagram is time and the vertical axis is power supply; the horizontal axis of the power consumption diagram is time and the vertical axis is power consumption. Further, the first area corresponding to the supply and demand analysis period is calculated in the power supply diagram, and the second area corresponding to the supply and demand analysis period is calculated in the power consumption diagram, wherein the two end points of the supply and demand analysis period are selected on the horizontal axis, and two straight lines are drawn perpendicularly to the horizontal axis through the two end points, intersecting with the power supply curve above, and the area enclosed by the two straight lines, the power supply curve, and the horizontal axis is calculated, which is recorded as the first area; the calculation method of the second area is similar to that of the first area, which will not be repeated here. Then, supply and demand analysis is performed based on the first area and the second area to determine the supply and demand analysis result, that is, the electronic device pre-stores the corresponding relationship between the first area, the second area and the supply and demand analysis result. That is, when the first area is larger than the second area, and the difference between the two is larger than the power supply surplus threshold, the supply and demand analysis result is determined to be supply exceeding demand; when the first area is smaller than the second area, and the difference between the two is larger than the power supply shortage threshold, the supply and demand analysis result is determined to be supply insufficient; otherwise, the supply and demand analysis result is determined to be supply and demand balance.

[0056] It can be seen that in the embodiment of the present application, the power supply calculation is performed based on each power generation plan to determine the power supply information, and then the supply and demand analysis is performed based on the power supply information and the load forecast information to determine the supply and demand analysis results, wherein the supply and demand analysis results include: oversupply, undersupply and supply and demand balance. Through supply and demand analysis, the supply and demand imbalance in the power system can be discovered in a timely manner, and based on the supply and demand analysis results, it is helpful to more reasonably allocate and dispatch electric energy, avoid waste and shortage of electric resources, and improve the operating efficiency of the entire power system.

[0057] The above-mentioned embodiment introduces a distribution network dispatching method from the perspective of method flow, and the following embodiment introduces a distribution network dispatching device from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiment.

[0058] The present application embodiment provides a distribution network dispatching device, such as Figure 2 As shown, the distribution network dispatching device may specifically include: The load forecasting module 210 is used to obtain real-time load data, auxiliary forecasting data and historical demand data of the distribution network, perform load forecasting based on the historical demand data, real-time load data and auxiliary forecasting data, and determine load forecasting information; The supply and demand analysis module 220 is used to obtain the power generation plan corresponding to each power resource in the distribution network, perform supply and demand analysis based on each power generation plan and load forecast information, and determine the supply and demand analysis results; The scheduling analysis module 230 is used to perform optimized scheduling analysis based on each power generation plan and load forecast information when the supply and demand analysis result is that supply exceeds demand, determine emergency scheduling information, and send the emergency scheduling information to the target power resources to control the target power resources to perform power scheduling according to the emergency scheduling information.

[0059] For the embodiment of the present application, the real-time load data, auxiliary prediction data and historical demand data of the distribution network are obtained, and the load prediction is performed based on the historical demand data, the real-time load data and the auxiliary prediction data to determine the load prediction information. The load prediction helps the power department to reasonably arrange the power generation plan, optimize the resource allocation, and improve the stability and safety of the power supply of the power grid. Then, the power generation plan corresponding to each power resource in the distribution network is obtained, and the supply and demand analysis is performed based on each power generation plan and load prediction information. The supply and demand analysis results are determined, and the supply and demand analysis of electric energy is performed, which helps to improve the reliability of power supply and ensure the stable operation of the power system during peak hours or emergency situations. When the supply and demand analysis result is that the supply is insufficient to meet the demand, the optimization scheduling analysis is performed based on each power generation plan and load prediction information, the emergency scheduling information is determined, and the emergency scheduling information is sent to the target power resource, and the target power resource is controlled to perform power scheduling according to the emergency scheduling information. The formulation and implementation of emergency scheduling information helps to optimize the configuration of power resources, avoid power outages caused by insufficient power supply, and improve the emergency response capability of the power system, so that it can respond to various emergencies in a short time, and improve the quality and stability of power supply.

[0060] In a possible implementation of the embodiment of the present application, the dispatch analysis module 230, when performing optimized dispatch analysis based on each power generation plan and load forecast information to determine emergency dispatch information, is used to: Obtain scheduling constraints, prioritize each power resource based on the scheduling constraints, and determine the power resource scheduling sequence; Obtain the power resource information corresponding to each power resource in the distribution network, perform incremental power analysis based on each power resource information and the power generation plan, and determine the additional power corresponding to each power resource; Based on the power resource dispatch sequence and the additional power corresponding to each power resource, an optimized dispatch analysis is performed to determine the emergency dispatch information.

[0061] A possible implementation of the embodiment of the present application also includes: The simulation module is used to obtain multi-dimensional data corresponding to the distribution network, build a model based on the multi-dimensional data, and obtain a virtual model of the distribution network, wherein the multi-dimensional data includes: grid structure data, load distribution data, and power generation resource data; Obtain meteorological data corresponding to the emergency dispatch period, perform simulation tests based on load forecast information, meteorological data, emergency dispatch information and distribution network virtual model, and obtain simulation test results; When the simulation test result is passed, the emergency dispatch information is sent to the target power resource; when the simulation test result is failed, a dispatch information abnormality warning is generated to facilitate re-execution of the optimized dispatch analysis.

[0062] In a possible implementation of the embodiment of the present application, the scheduling analysis module 230, when performing incremental power analysis based on each power resource information and power generation plan to determine the additional power corresponding to each power resource, is used to: Calculate theoretical power generation capacity based on installed capacity in power resource information to determine theoretical power generation capacity; Estimating equipment health based on equipment status information in power resource information, determining equipment health coefficient, obtaining meteorological data corresponding to the emergency dispatch period, estimating environmental impact based on meteorological data, and determining environmental impact coefficient; Calculate the actual power generation capacity based on the theoretical power generation capacity, equipment health factor and environmental impact factor to determine the actual power generation capacity; An incremental power analysis is performed based on the actual power generation capacity and power generation plan corresponding to each power resource to determine the additional power generated by each power resource.

[0063] In a possible implementation of the embodiment of the present application, the load forecasting module 210, when performing demand forecasting based on historical demand data, real-time load data, and auxiliary forecasting data to determine load forecasting information, is used to: Integrate historical demand data, real-time load data and auxiliary forecast data to obtain target demand data; Extracting a first feature based on the target demand data to determine a linear feature and a seasonal feature, and forecasting demand based on the linear feature and the seasonal feature to determine first load forecast information; Extracting a second feature based on the target demand data to determine a nonlinear feature, and performing demand forecasting based on the nonlinear feature to determine second load forecasting information; A combined prediction is performed based on the first load prediction information and the second load prediction information to determine the load prediction information.

[0064] In a possible implementation of the embodiment of the present application, the supply and demand analysis module 220, when performing supply and demand analysis based on each power generation plan and load forecast information and determining the supply and demand analysis result, is used to: Perform power supply calculation based on each power generation plan to determine power supply information; Based on the supply and demand analysis of the power supply information and the load forecast information, the supply and demand analysis results are determined, wherein the supply and demand analysis results include: oversupply, shortage of supply and supply and demand balance.

[0065] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of a distribution network dispatching device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0066] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0067] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0068] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0069] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0070] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0071] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0072] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0073] The embodiment of the present application provides a computer program product, which includes a computer program, which implements the method in any of the above embodiments when executed by a processor. Compared with the related art, the embodiment of the present application obtains real-time load data, auxiliary prediction data and historical demand data of the distribution network, performs load prediction based on historical demand data, real-time load data and auxiliary prediction data, and determines load prediction information. Load prediction helps the power department to reasonably arrange power generation plans, optimize resource allocation, and improve the stability and safety of power supply of the power grid. Then, the power generation plan corresponding to each power resource in the distribution network is obtained, and supply and demand analysis is performed based on each power generation plan and load prediction information, and the supply and demand analysis results are determined. The supply and demand analysis of electric energy is performed, which helps to improve the reliability of power supply and ensure the stable operation of the power system during peak hours or in emergency situations. When the supply and demand analysis result is that the supply is insufficient to meet the demand, an optimization scheduling analysis is performed based on each power generation plan and load prediction information, emergency scheduling information is determined, and the emergency scheduling information is sent to the target power resource, and the target power resource is controlled to perform power scheduling according to the emergency scheduling information. The formulation and implementation of emergency dispatch information helps to optimize the allocation of power resources, avoid power outages caused by insufficient power supply, enhance the emergency response capability of the power system, enable it to cope with various emergencies in a short period of time, and improve the quality and stability of power supply.

[0074] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings 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 their execution order is not necessarily sequential, 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.

[0075] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A distribution network dispatching method, characterized in that: include: Acquire real-time load data, auxiliary prediction data and historical demand data of the distribution network, perform load prediction based on the historical demand data, the real-time load data and the auxiliary prediction data, and determine load prediction information; Obtaining a power generation plan corresponding to each power resource in the distribution network, performing supply and demand analysis based on each of the power generation plans and the load forecast information, and determining a supply and demand analysis result; When the supply and demand analysis result is that supply exceeds demand, an optimized scheduling analysis is performed based on each of the power generation plans and the load forecast information, emergency scheduling information is determined, and the emergency scheduling information is sent to the target power resources, and the target power resources are controlled to perform power scheduling according to the emergency scheduling information.

2. The distribution network dispatching method according to claim 1, characterized in that: The optimizing dispatching analysis based on each of the power generation plans and the load forecasting information to determine the emergency dispatching information includes: Obtaining a scheduling constraint condition, and based on the scheduling constraint condition, prioritizing each of the power resources to determine a scheduling sequence of the power resources; Acquire power resource information corresponding to each of the power resources in the distribution network, perform incremental power analysis based on each of the power resource information and the power generation plan, and determine the additional power corresponding to each of the power resources; Based on the power resource scheduling sequence and the additional power corresponding to each of the power resources, an optimization scheduling analysis is performed to determine emergency scheduling information.

3. The distribution network dispatching method according to claim 2, characterized in that: After performing the optimization scheduling analysis based on the power resource scheduling sequence and the maximum additional power corresponding to each power resource to determine the emergency scheduling information, the method further includes: Acquire multi-dimensional data corresponding to the distribution network, and construct a model based on the multi-dimensional data to obtain a virtual model of the distribution network, wherein the multi-dimensional data includes: grid structure data, load distribution data, and power generation resource data; Acquire meteorological data corresponding to the emergency dispatch period, perform simulation test based on the load forecast information, the meteorological data, the emergency dispatch information and the distribution network virtual model, and obtain simulation test results; When the simulation test result is passed, the emergency dispatch information is sent to the target power resource; when the simulation test result is failed, a dispatch information abnormality warning is generated to facilitate re-execution of the optimized dispatch analysis.

4. The distribution network dispatching method according to claim 2, characterized in that: The performing of incremental power analysis based on each of the power resource information and the power generation plan to determine the additional power corresponding to each of the power resources includes: Calculate theoretical power generation capacity based on the installed capacity in the power resource information to determine the theoretical power generation capacity; Performing equipment health estimation based on the equipment status information in the power resource information to determine the equipment health coefficient, and obtaining meteorological data corresponding to the emergency dispatch period, performing environmental impact estimation based on the meteorological data to determine the environmental impact coefficient; Calculate the actual power generation capacity based on the theoretical power generation capacity, the equipment health coefficient and the environmental impact coefficient to determine the actual power generation capacity; Incremental power analysis is performed based on the actual power generation capacity corresponding to each of the power resources and the power generation plan to determine the additional power corresponding to each of the power resources.

5. The distribution network dispatching method according to claim 1, characterized in that: The performing demand forecasting based on the historical demand data, the real-time load data and the auxiliary forecasting data to determine load forecasting information includes: Performing data integration based on the historical demand data, the real-time load data and the auxiliary forecast data to obtain target demand data; Performing a first feature extraction based on the target demand data to determine a linear feature and a seasonal feature, and performing a demand forecast based on the linear feature and the seasonal feature to determine first load forecast information; Performing a second feature extraction based on the target demand data to determine a nonlinear feature, and performing demand forecasting based on the nonlinear feature to determine second load forecasting information; A combined prediction is performed based on the first load prediction information and the second load prediction information to determine load prediction information.

6. The distribution network dispatching method according to claim 1, characterized in that: The performing supply and demand analysis based on each of the power generation plans and the load forecast information to determine the supply and demand analysis result includes: Performing power supply calculation based on each of the power generation plans to determine power supply information; Based on the supply and demand analysis of the electric energy supply information and the load forecast information, a supply and demand analysis result is determined, wherein the supply and demand analysis result includes: oversupply, shortage of supply and supply and demand balance.

7. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the distribution network scheduling method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the distribution network dispatching method according to any one of claims 1 to 6.

9. A computer program product, characterized in that The method comprises a computer program, wherein the computer program is executed by a processor to implement the distribution network dispatching method according to any one of claims 1 to 6.

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