Power distribution network source network load storage scheduling management system and method thereof

Abstract

The invention relates to an electric power resource management and control technology, and discloses a power distribution network load storage scheduling management system and a method thereof, and the method comprises the steps: obtaining power generation data in a power distribution network, calculating a power source fluctuation index according to the power generation data, calculating a weight index of a power distribution node, and judging whether the power source fluctuation index is greater than a first fluctuation threshold value or not; if the power source fluctuation index is greater than a first fluctuation threshold value, determining the number of cut-off nodes according to the power source fluctuation index, and cutting off the power distribution nodes of the power distribution network according to the number of cut-off nodes and a weight index, otherwise, obtaining power utilization data of the power distribution network, and calculating a power utilization fluctuation index according to the power utilization data, and judging whether the power utilization fluctuation index is greater than a second fluctuation threshold value, if the power utilization fluctuation index is greater than the second fluctuation threshold value, performing load dynamic compensation on the power distribution network by using preset energy storage equipment, otherwise, re-acquiring the power generation data of the power distribution network. According to the invention, the efficiency and safety of power distribution network load storage scheduling can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power resource management and control, and in particular to a distribution network source-grid-load-storage dispatching management system and method. Background Art

[0002] In modern power systems, the distribution network is a key link between power production and consumption. Its operation stability and efficiency are crucial to ensure the normal operation of the social economy. With the continuous adjustment and optimization of the energy structure, the large-scale access of distributed power sources (such as solar photovoltaics, wind power generation, etc.), and the gradual popularization of energy storage equipment, the operation mode of the distribution network is undergoing profound changes, from the traditional single power supply mode to a complex system of coordinated interaction between source, grid, load and storage.

[0003] At present, the traditional distribution network dispatching and management system mainly focuses on the monitoring of power grid equipment and the simple regulation of power flow, which is difficult to effectively deal with the intermittent and random nature of distributed power output. For example, solar photovoltaic power generation depends on light intensity, and wind power generation is subject to wind speed and wind direction. These factors cause their power generation to fluctuate greatly on different time scales, which brings great challenges to the voltage stability and frequency control of the distribution network. When the output of distributed power exceeds the carrying capacity of the distribution network, it is easy to cause problems such as voltage over-limit and power reverse transmission, which seriously affect the safe and reliable operation of the distribution network. In terms of dispatching methods, the traditional centralized dispatching mode has high computational complexity, heavy communication burden, and extremely demanding requirements on the real-time performance of the system. With the continuous expansion of the scale of the distribution network and the increasing complexity of the structure, the centralized dispatching mode seems to be unable to cope with massive data processing and rapid decision-making, and it is difficult to meet the needs of coordinated dispatching of source, grid, load and storage. Moreover, the distribution networks in different regions have significant differences in topology, power distribution, load characteristics, etc. The general dispatching method often cannot take into account the actual conditions of various places, resulting in poor dispatching effect. Summary of the invention

[0004] The present invention provides a distribution network source-grid-load-storage dispatching management system and method, the main purpose of which is to solve the problem of low efficiency and low safety of the existing power grid source-grid-load-storage dispatching management system.

[0005] To achieve the above-mentioned object, the present invention provides a distribution network source-grid-load-storage dispatching management system, characterized in that the system comprises a data acquisition module, a data calculation module, a first judgment module and a second judgment module, wherein: The data acquisition module is used to acquire power generation data of distributed power sources in the distribution network within a preset time period, and calculate the power source fluctuation index based on the power generation data; The data calculation module is used to calculate the weight index of each power distribution node to obtain a weight index set; The first judgment module is used to judge whether the power source fluctuation index is greater than a preset first fluctuation threshold value. If the power source fluctuation index is greater than the first fluctuation threshold value, the number of cut-off nodes is determined according to the power source fluctuation index, and the distribution nodes of the distribution network are cut off according to the number of cut-off nodes and the weight index set. Otherwise, the power consumption data within a preset time period of the distribution network is obtained, and the power consumption fluctuation index is calculated according to the power consumption data; The second judgment module is used to judge whether the electricity consumption fluctuation index is greater than a preset second fluctuation threshold. If the electricity consumption fluctuation index is greater than the second fluctuation threshold, the preset energy storage device is used to dynamically compensate the load of the distribution network. Otherwise, the first judgment module is returned to obtain the power generation data of the distributed power source in the distribution network within a preset time period, and the power source fluctuation index is calculated according to the power generation data.

[0006] Optionally, when calculating the power source fluctuation index according to the power generation data, the data acquisition module is specifically used to: Acquiring wind power generation data, hydropower generation data, and photovoltaic power generation data included in the power generation data; Respectively calculating the average power generation of the wind power generation data, the hydropower generation data, and the photovoltaic power generation data within the preset time period to obtain average power data; The electric power source fluctuation index is calculated according to the wind power generation data, the hydropower generation data, the photovoltaic power generation data, and the average power data.

[0007] Optionally, the calculation formula of the power source fluctuation index in the data acquisition module is as follows: ; in, is the power source fluctuation index, is the preset duration, The wind power generation data Power generation data, The hydropower generation data Power generation data, The photovoltaic power generation data Power generation data, is the average wind power generation power included in the average power data, is the average hydroelectric power included in the average power data, It is the average photovoltaic power generation power included in the average power data.

[0008] Optionally, when calculating the weight index of each power distribution node, the data calculation module is specifically used to: Obtaining total power load data of the distribution network within the preset time period; Obtain node power load data and node preset data for each power distribution node within a preset time period; The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data.

[0009] Optionally, when the data calculation module calculates the weight index of each power distribution node according to the total power load data, the node power load data and the node preset data, it is specifically used to: The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data using the following formula: ; in, is the weight index, The node importance parameters included in the preset data for the node, is the number of samples in the node power load data, represents the last power load data in the power load data of the node, represents the first power load data in the power load data of the node, The number of node branches included in the preset data for the node, Indicates the node power load data Power load data, Indicates the total power load data The power load data.

[0010] Optionally, when the first judgment module confirms the number of cut-off nodes according to the power source fluctuation index, it is specifically used to: Determining whether the power source fluctuation index is less than a preset first threshold; If the power source fluctuation index is less than the first threshold, determining the number of cut-off nodes to be a preset first number; If the power source fluctuation index is greater than or equal to the first threshold, determining whether the power source fluctuation index is less than a preset second threshold; If the power source fluctuation index is less than the second threshold, determining the number of cut-off nodes to be a preset second number; If the power source fluctuation index is greater than or equal to the second threshold, determining whether the power source fluctuation index is less than a preset third threshold; If the power source fluctuation index is less than the third threshold, determining the number of cut-off nodes to be a preset third number; If the power source fluctuation index is greater than or equal to the third threshold, the number of cut-off nodes is determined to be a preset fourth number.

[0011] Optionally, when calculating the power consumption fluctuation index according to the power consumption data, the first judgment module is specifically used to: The electricity consumption fluctuation index is calculated using the following formula: ; in, is the electricity consumption fluctuation index, is the number of distribution nodes included in the power consumption data, The first The power change of each power distribution node within the preset time period, Indicates The node importance parameters of the distribution nodes, represents the preset duration, Indicates the first The total power consumption at a time point, Indicates the average power consumption included in the power consumption data.

[0012] Optionally, when the second judgment module uses a preset energy storage device to dynamically compensate for the load of the distribution network, it is specifically used to: Extracting features from the electricity usage data to obtain features of the electricity usage data; Acquire multimodal information of the area where the distribution network is located, perform feature extraction on the multimodal information, and obtain multimodal features; Performing feature fusion on the electricity consumption data feature and the multimodal feature to obtain fused information features; Using a pre-trained electricity consumption data prediction model to predict electricity consumption data according to the fusion information features to obtain future electricity consumption data; Calculating dynamic compensation power data according to the future power consumption data; The load dynamic compensation is performed on the distribution network based on the dynamic compensation power data.

[0013] Optionally, when calculating the dynamic compensation power data according to the future power consumption data, the second judgment module is specifically used to: Calculate the average value of the future electricity consumption data to obtain the future electricity consumption mean value; Replacing values ​​in the future electricity consumption data that are less than or equal to the future electricity consumption mean value with 0 to obtain an initial compensation data set; The dynamic compensation power data is calculated based on the initial compensation data set using the following formula: ; in, is the first The compensation power, is the first Compensation data, is the pre-acquired harmonic current reactive power, is the preset standard power of the power distribution node. To obtain the real-time impedance in advance, is the transmission loss rate obtained in advance.

[0014] In order to solve the above problems, an embodiment of the present invention provides a distribution network source-grid-load-storage scheduling management method, including: Acquire power generation data of distributed power sources in the distribution network within a preset time period, and calculate a power source fluctuation index based on the power generation data; Calculate the weight index of each distribution node to obtain a weight index set; Determining whether the power source fluctuation index is greater than a preset first fluctuation threshold; If the power source fluctuation index is greater than the first fluctuation threshold, determining the number of cut-off nodes according to the power source fluctuation index, and cutting off the distribution nodes of the distribution network according to the number of cut-off nodes and the weight index set; If the power source fluctuation index is less than or equal to the first fluctuation threshold, obtaining power consumption data within a preset time period of the power distribution network, and calculating the power consumption fluctuation index according to the power consumption data; Determining whether the power consumption fluctuation index is greater than a preset second fluctuation threshold; If the power consumption fluctuation index is greater than the second fluctuation threshold, dynamically compensating the load of the distribution network by using a preset energy storage device; If the electricity consumption fluctuation index is less than or equal to the second fluctuation threshold, the method returns to the step of obtaining the power generation data of the distributed power sources in the distribution network within a preset time period and calculating the power source fluctuation index according to the power generation data.

[0015] The embodiment of the present invention obtains the power generation data of the distributed power source in the distribution network within a preset time length, calculates the power source fluctuation index according to the power generation data, calculates the weight index of each distribution node, obtains the weight index set, and judges whether the power source fluctuation index is greater than a preset first fluctuation threshold. If the power source fluctuation index is greater than the first fluctuation threshold, the number of cut-off nodes is confirmed according to the power source fluctuation index, and the distribution nodes of the distribution network are cut off according to the number of cut-off nodes and the weight index set. If the power source fluctuation index is less than or equal to the first fluctuation threshold, the power consumption data within the preset time length of the distribution network is obtained, and the power consumption fluctuation index is calculated according to the power consumption data. It is judged whether the power consumption fluctuation index is greater than a preset second fluctuation threshold. If the power consumption fluctuation index is greater than the second fluctuation threshold, the preset energy storage device is used to dynamically compensate the load of the distribution network. If the power consumption fluctuation index is less than or equal to the second fluctuation threshold, the step of obtaining the power generation data of the distributed power source in the distribution network within the preset time length and calculating the power source fluctuation index according to the power generation data is returned. The efficiency and safety of distribution network load-storage scheduling can be improved, and the stable operation of the distribution network can be ensured. Therefore, the distribution network source-grid-load-storage scheduling management system and method proposed in the present invention can solve the problems of low efficiency and low safety of existing power grid source-grid-load-storage scheduling management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A functional module diagram of a distribution network source-grid-load-storage scheduling and management system provided by an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a distribution network source-grid-load-storage scheduling management method provided in one embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0018] The embodiment of the present application provides a distribution network source network load storage scheduling management method. The execution subject of the distribution network source network load storage scheduling management method includes but is not limited to at least one of the electronic devices such as the server and the terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the distribution network source network load storage scheduling management method can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0019] like Figure 1 , is a functional module diagram of a distribution network source-grid-load-storage scheduling and management system provided by an embodiment of the present invention.

[0020] The distribution network source network load storage scheduling management system 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the distribution network source network load storage scheduling management system 100 can include a data acquisition module 101, a data calculation module 102, a first judgment module 103 and a second judgment module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0021] In this embodiment, the functions of each module / unit are as follows: The data acquisition module 101 is used to acquire power generation data of distributed power sources in the distribution network within a preset time period, and calculate the power source fluctuation index according to the power generation data; The data calculation module 102 is used to calculate the weight index of each power distribution node to obtain a weight index set; The first judgment module 103 is used to judge whether the power source fluctuation index is greater than a preset first fluctuation threshold value. If the power source fluctuation index is greater than the first fluctuation threshold value, the number of cut-off nodes is determined according to the power source fluctuation index, and the distribution nodes of the distribution network are cut off according to the number of cut-off nodes and the weight index set. Otherwise, the power consumption data within a preset time period of the distribution network is obtained, and the power consumption fluctuation index is calculated according to the power consumption data; The second judgment module 104 is used to judge whether the power consumption fluctuation index is greater than a preset second fluctuation threshold. If the power consumption fluctuation index is greater than the second fluctuation threshold, the preset energy storage device is used to dynamically compensate the load of the distribution network. Otherwise, the first judgment module 103 is returned to obtain the power generation data of the distributed power source in the distribution network within a preset time period, and the power source fluctuation index is calculated according to the power generation data.

[0022] The data acquisition module 101 is used to acquire power generation data of distributed power sources in the distribution network within a preset time period, and calculate the power source fluctuation index according to the power generation data.

[0023] In an embodiment of the present invention, the preset time length needs to be set according to actual business needs. The longer the preset time length is, the higher the accuracy of subsequent analysis based on the power generation data will be, but the timeliness will be sacrificed, thereby reducing the effect of data analysis. Therefore, the preset time length can be one minute.

[0024] In the embodiment of the present invention, after obtaining the power generation data of the distributed power sources in the distribution network within a preset time period, it is necessary to perform denoising on the power generation data to remove abnormal data in the power generation data to improve the quality of the data and improve the efficiency of subsequent analysis.

[0025] In the embodiment of the present invention, the distribution network refers to a power network that receives electric energy from a transmission network or a regional power plant and distributes it locally or step by step according to voltage to various types of users through distribution facilities.

[0026] In the embodiment of the present invention, the power source connected to the distribution network may be a distributed power source, which refers to a small or medium-sized power generation device or energy storage system that is dispersedly arranged on the distribution network or user side and close to the load center. Different from traditional centralized power plants, distributed power sources have the characteristics of small capacity, flexible access, and multi-energy complementarity, and are an important technical means to achieve low-carbon energy transformation and smart grids.

[0027] In detail, the distributed power source may include a wind power plant, a hydropower plant and a photovoltaic power plant.

[0028] In the embodiment of the present invention, when the data acquisition module 101 calculates the power source fluctuation index according to the power generation data, it is specifically used to: Acquiring wind power generation data, hydropower generation data, and photovoltaic power generation data included in the power generation data; Respectively calculating the average power generation of the wind power generation data, the hydropower generation data, and the photovoltaic power generation data within the preset time period to obtain average power data; The electric power source fluctuation index is calculated according to the wind power generation data, the hydropower generation data, the photovoltaic power generation data, and the average power data.

[0029] In an embodiment of the present invention, the electric power source fluctuation index is calculated based on the wind power generation data, the hydropower generation data, the photovoltaic power generation data and the average power data. It is an indicator calculated based on the analysis of the changing characteristics of the wind power generation data, the hydropower generation data, the photovoltaic power generation data and the average power data within the preset time period, and can show the stability of the power generation of the distributed power source.

[0030] In the embodiment of the present invention, the calculation formula of the power source fluctuation index in the data acquisition module 101 is as follows: ; in, is the power source fluctuation index, is the preset duration, The wind power generation data Power generation data, The hydropower generation data Power generation data, The photovoltaic power generation data Power generation data, is the average wind power generation power included in the average power data, is the average hydroelectric power included in the average power data, It is the average photovoltaic power generation power included in the average power data.

[0031] In the embodiment of the present invention, by acquiring the power generation data of the distributed power sources in the distribution network within a preset time period, the accuracy of calculating the power source fluctuation index can be improved.

[0032] The data calculation module 102 is used to calculate the weight index of each power distribution node to obtain a weight index set.

[0033] In the embodiment of the present invention, the weight index represents the importance of a distribution node in the distribution network.

[0034] In the embodiment of the present invention, when calculating the weight index of each power distribution node, the data calculation module 102 is specifically used to: Obtaining total power load data of the distribution network within the preset time period; Obtain node power load data and node preset data for each power distribution node within a preset time period; The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data.

[0035] In the embodiment of the present invention, when the data calculation module 102 calculates the weight index of each distribution node according to the total power load data, the node power load data and the node preset data, it is specifically used to: The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data using the following formula: ; in, is the weight index, The node importance parameters included in the preset data for the node, is the number of samples in the node power load data, represents the last power load data in the power load data of the node, represents the first power load data in the power load data of the node, The number of node branches included in the preset data for the node, Indicates the node power load data Power load data, Indicates the total power load data The power load data.

[0036] In detail, the node importance parameter is a pre-set parameter and can be set according to the characteristics of the unit powered by a distribution node. For example, the node importance parameter of a distribution node used to supply power to ordinary residents may be 1, and the node importance parameter of a distribution node used to supply power to hospitals may be 100, which can ensure the power supply of important units such as hospitals in subsequent processing.

[0037] In the embodiment of the present invention, by calculating the weight index of each distribution node to obtain a weight index set, the efficiency of subsequent confirmation of the number of cut-off nodes can be improved, and the power supply of important distribution nodes can be guaranteed.

[0038] The first judgment module 103 is used to judge whether the power source fluctuation index is greater than a preset first fluctuation threshold. If the power source fluctuation index is greater than the first fluctuation threshold, the number of cut-off nodes is confirmed according to the power source fluctuation index, and the distribution nodes of the distribution network are cut off according to the number of cut-off nodes and the weight index set. Otherwise, the power consumption data within a preset time period of the distribution network is obtained, and the power consumption fluctuation index is calculated based on the power consumption data.

[0039] In an embodiment of the present invention, when the power source fluctuation index is greater than the first fluctuation threshold, it indicates that the power generation stability of the distributed power source is poor and the power generation fluctuation is large, making it difficult to maintain a stable power supply to the distribution network. Therefore, part of the distribution network needs to be powered off.

[0040] In the embodiment of the present invention, when the first determination module 103 determines the number of cut-off nodes according to the power source fluctuation index, it is specifically used to: Determining whether the power source fluctuation index is less than a preset first threshold; If the power source fluctuation index is less than the first threshold, determining the number of cut-off nodes to be a preset first number; If the power source fluctuation index is greater than or equal to the first threshold, determining whether the power source fluctuation index is less than a preset second threshold; If the power source fluctuation index is less than the second threshold, determining the number of cut-off nodes to be a preset second number; If the power source fluctuation index is greater than or equal to the second threshold, determining whether the power source fluctuation index is less than a preset third threshold; If the power source fluctuation index is less than the third threshold, determining the number of cut-off nodes to be a preset third number; If the power source fluctuation index is greater than or equal to the third threshold, the number of cut-off nodes is determined to be a preset fourth number.

[0041] In the embodiment of the present invention, the first threshold may be 1, the second threshold may be 2, the third threshold may be 3, and the fourth threshold may be 4.

[0042] In the embodiment of the present invention, the power consumption fluctuation index is a parameter reflecting the degree of fluctuation of the overall power consumption in the distribution network.

[0043] In the embodiment of the present invention, when the first determination module 103 calculates the power consumption fluctuation index according to the power consumption data, it is specifically used to: The electricity consumption fluctuation index is calculated using the following formula: ; in, is the electricity consumption fluctuation index, is the number of distribution nodes included in the power consumption data, The first The power change of each power distribution node within the preset time period, Indicates The node importance parameters of the distribution nodes, represents the preset duration, Indicates the first The total power consumption at a time point, Indicates the average power consumption included in the power consumption data.

[0044] The second judgment module 104 is used to judge whether the power consumption fluctuation index is greater than a preset second fluctuation threshold. If the power consumption fluctuation index is greater than the second fluctuation threshold, the preset energy storage device is used to dynamically compensate the load of the distribution network. Otherwise, the first judgment module 103 is returned to obtain the power generation data of the distributed power source in the distribution network within a preset time period, and the power source fluctuation index is calculated according to the power generation data.

[0045] In the embodiment of the present invention, when the power consumption fluctuation index is greater than the second fluctuation threshold, it indicates that the power consumption in the distribution network fluctuates greatly. In order to maintain the power supply stability of the distribution network, it is necessary to call the energy storage device to compensate for the power supply power of the fluctuating part of the distribution network. In an embodiment of the present invention, the dynamic load compensation of the distribution network using a preset energy storage device is based on a neural network model to predict future power consumption data according to power consumption data within a preset time period, and dynamically calculate dynamic compensation power data according to the predicted data. The energy storage device then dynamically outputs power according to the dynamic compensation power data to ensure the stability of the distribution network.

[0046] In the embodiment of the present invention, when the second determination module 104 uses the preset energy storage device to dynamically compensate the load of the distribution network, it is specifically used to: Extracting features from the electricity usage data to obtain features of the electricity usage data; Acquire multimodal information of the area where the distribution network is located, perform feature extraction on the multimodal information, and obtain multimodal features; Performing feature fusion on the electricity consumption data feature and the multimodal feature to obtain fused information features; Using a pre-trained electricity consumption data prediction model to predict electricity consumption data according to the fusion information features to obtain future electricity consumption data; Calculating dynamic compensation power data according to the future power consumption data; The load dynamic compensation is performed on the distribution network based on the dynamic compensation power data.

[0047] In an embodiment of the present invention, the power consumption data prediction model is a neural network model, which can predict the fluctuation of future power consumption data by capturing the fluctuation law of the fused information features. It can accurately capture the fluctuation law of the fused information features in the time series. By repeatedly learning and deeply mining the fluctuation patterns of various information features in historical data, the model can keenly perceive the impact of different factors on the fluctuation of power consumption data. When faced with the task of predicting future power consumption data, the model will make scientific and reasonable predictions on the fluctuation trend of power consumption data in the future period based on the fluctuation laws that have been mastered. For example, the fluctuation characteristics presented by the fused information features such as seasonal changes, daily power consumption time differences, special events, etc. are comprehensively considered, and the fluctuation of future power consumption data is more accurately estimated, providing strong support for relevant power decision-making and resource allocation.

[0048] In an embodiment of the present invention, the multimodal information may include temperature information, weather information and season information.

[0049] In the embodiment of the present invention, when the second determination module 104 calculates the dynamic compensation power data according to the future power consumption data, it is specifically used to: Calculate the average value of the future electricity consumption data to obtain the future electricity consumption mean value; Replacing values ​​in the future electricity consumption data that are less than or equal to the future electricity consumption mean value with 0 to obtain an initial compensation data set; The dynamic compensation power data is calculated based on the initial compensation data set using the following formula: ; in, is the first The compensation power, is the first Compensation data, is the pre-acquired harmonic current reactive power, is the preset standard power of the power distribution node. To obtain the real-time impedance in advance, is the transmission loss rate obtained in advance.

[0050] In the embodiment of the present invention, when the power consumption fluctuation index is greater than the second fluctuation threshold, the power supply stability of the distribution network is ensured by dynamically compensating the load of the distribution network using a preset energy storage device.

[0051] In an embodiment of the present invention, when the power consumption fluctuation index is less than or equal to the second fluctuation threshold, it means that the power consumption in the distribution network is stable and the power generation power of the distributed power source is stable. Therefore, it is possible to return to the step of obtaining the power generation data of the distributed power sources in the distribution network within a preset time period, and calculate the power source fluctuation index according to the power generation data, and calculate the power source fluctuation index for the next preset time period to achieve continuous monitoring and management of the distribution network.

[0052] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0053] In detail, each module described in the distribution network source network load storage scheduling management system 100 described in the embodiment of the present invention is used in the same manner as described above. Figure 2 The technical means are the same as the distribution network source, grid, load and storage dispatching management method described in, and can produce the same technical effects, so they will not be repeated here.

[0054] Reference Figure 2 FIG. 1 is a flow chart of a distribution network source-grid-load-storage scheduling management method provided by an embodiment of the present invention. In this embodiment, the distribution network source-grid-load-storage scheduling management method includes: S1. Obtain power generation data of distributed power sources in a distribution network within a preset time period, and calculate a power source fluctuation index based on the power generation data.

[0055] In an embodiment of the present invention, the preset time length needs to be set according to actual business needs. The longer the preset time length is, the higher the accuracy of subsequent analysis based on the power generation data will be, but the timeliness will be sacrificed, thereby reducing the effect of data analysis. Therefore, the preset time length can be one minute.

[0056] In the embodiment of the present invention, after obtaining the power generation data of the distributed power sources in the distribution network within a preset time period, it is necessary to perform denoising on the power generation data to remove abnormal data in the power generation data to improve the quality of the data and improve the efficiency of subsequent analysis.

[0057] In the embodiment of the present invention, the distribution network refers to a power network that receives electric energy from a transmission network or a regional power plant and distributes it locally or step by step according to voltage to various types of users through distribution facilities.

[0058] In the embodiment of the present invention, the power source connected to the distribution network may be a distributed power source, which refers to a small or medium-sized power generation device or energy storage system that is dispersedly arranged on the distribution network or user side and close to the load center. Different from traditional centralized power plants, distributed power sources have the characteristics of small capacity, flexible access, and multi-energy complementarity, and are an important technical means to achieve low-carbon energy transformation and smart grids.

[0059] In detail, the distributed power source may include a wind power plant, a hydropower plant and a photovoltaic power plant.

[0060] In an embodiment of the present invention, the step of calculating the power source fluctuation index according to the power generation data includes: Acquiring wind power generation data, hydropower generation data, and photovoltaic power generation data included in the power generation data; Respectively calculating the average power generation of the wind power generation data, the hydropower generation data, and the photovoltaic power generation data within the preset time period to obtain average power data; The electric power source fluctuation index is calculated according to the wind power generation data, the hydropower generation data, the photovoltaic power generation data, and the average power data.

[0061] In an embodiment of the present invention, the electric power source fluctuation index is calculated based on the wind power generation data, the hydropower generation data, the photovoltaic power generation data and the average power data. It is an indicator calculated based on the analysis of the changing characteristics of the wind power generation data, the hydropower generation data, the photovoltaic power generation data and the average power data within the preset time period, and can show the stability of the power generation of the distributed power source.

[0062] In the embodiment of the present invention, the calculating of the power source fluctuation index according to the wind power generation data, the hydropower generation data, the photovoltaic power generation data and the average power data includes: The power source fluctuation index is calculated using the following formula: ; in, is the power source fluctuation index, is the preset duration, The wind power generation data Power generation data, The hydropower generation data Power generation data, The photovoltaic power generation data Power generation data, is the average wind power generation power included in the average power data, is the average hydroelectric power included in the average power data, It is the average photovoltaic power generation power included in the average power data.

[0063] In the embodiment of the present invention, by acquiring the power generation data of the distributed power sources in the distribution network within a preset time period, the accuracy of calculating the power source fluctuation index can be improved.

[0064] S2. Calculate the weight index of each distribution node to obtain a weight index set.

[0065] In the embodiment of the present invention, the weight index represents the importance of a distribution node in the distribution network.

[0066] In the embodiment of the present invention, the step of calculating the weight index of each power distribution node includes: Obtaining total power load data of the distribution network within the preset time period; Obtain node power load data and node preset data for each power distribution node within a preset time period; The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data.

[0067] In the embodiment of the present invention, the weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data, including: The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data using the following formula: ; in, is the weight index, The node importance parameters included in the preset data for the node, is the number of samples in the node power load data, represents the last power load data in the power load data of the node, represents the first power load data in the power load data of the node, The number of node branches included in the preset data for the node, Indicates the node power load data Power load data, Indicates the total power load data The power load data.

[0068] In detail, the node importance parameter is a pre-set parameter and can be set according to the characteristics of the unit powered by a distribution node. For example, the node importance parameter of a distribution node used to supply power to ordinary residents may be 1, and the node importance parameter of a distribution node used to supply power to hospitals may be 100, which can ensure the power supply of important units such as hospitals in subsequent processing.

[0069] In the embodiment of the present invention, by calculating the weight index of each distribution node to obtain a weight index set, the efficiency of subsequent confirmation of the number of cut-off nodes can be improved, and the power supply of important distribution nodes can be guaranteed.

[0070] S3. Determine whether the power source fluctuation index is greater than a preset first fluctuation threshold.

[0071] If the power source fluctuation index is greater than the first fluctuation threshold, execute S4, confirm the number of cut-off nodes according to the power source fluctuation index, and cut off the distribution nodes of the distribution network according to the number of cut-off nodes and the weight index set.

[0072] In an embodiment of the present invention, when the power source fluctuation index is greater than the first fluctuation threshold, it indicates that the power generation stability of the distributed power source is poor and the power generation fluctuation is large, making it difficult to maintain a stable power supply to the distribution network. Therefore, part of the distribution network needs to be powered off.

[0073] In the embodiment of the present invention, the determining the number of cut-off nodes according to the power source fluctuation index includes: Determining whether the power source fluctuation index is less than a preset first threshold; If the power source fluctuation index is less than the first threshold, determining the number of cut-off nodes to be a preset first number; If the power source fluctuation index is greater than or equal to the first threshold, determining whether the power source fluctuation index is less than a preset second threshold; If the power source fluctuation index is less than the second threshold, determining the number of cut-off nodes to be a preset second number; If the power source fluctuation index is greater than or equal to the second threshold, determining whether the power source fluctuation index is less than a preset third threshold; If the power source fluctuation index is less than the third threshold, determining the number of cut-off nodes to be a preset third number; If the power source fluctuation index is greater than or equal to the third threshold, the number of cut-off nodes is determined to be a preset fourth number.

[0074] In the embodiment of the present invention, the first threshold may be 1, the second threshold may be 2, the third threshold may be 3, and the fourth threshold may be 4.

[0075] If the power source fluctuation index is less than or equal to the first fluctuation threshold, execute S5, obtain power consumption data within a preset time period of the distribution network, and calculate the power consumption fluctuation index according to the power consumption data.

[0076] In the embodiment of the present invention, the power consumption fluctuation index is a parameter reflecting the degree of fluctuation of the overall power consumption in the distribution network.

[0077] In the embodiment of the present invention, the step of calculating the power consumption fluctuation index according to the power consumption data includes: The electricity consumption fluctuation index is calculated using the following formula: ; in, is the electricity consumption fluctuation index, is the number of distribution nodes included in the power consumption data, The first The power change of each power distribution node within the preset time period, Indicates The node importance parameters of the distribution nodes, represents the preset duration, Indicates the first The total power consumption at a time point, Indicates the average power consumption included in the power consumption data.

[0078] S6. Determine whether the electricity consumption fluctuation index is greater than a preset second fluctuation threshold.

[0079] If the power consumption fluctuation index is greater than the second fluctuation threshold, execute S7 and use a preset energy storage device to dynamically compensate for the load of the distribution network.

[0080] In the embodiment of the present invention, when the power consumption fluctuation index is greater than the second fluctuation threshold, it indicates that the power consumption in the distribution network fluctuates greatly. In order to maintain the power supply stability of the distribution network, it is necessary to call the energy storage device to compensate for the power supply power of the fluctuating part of the distribution network. In an embodiment of the present invention, the dynamic load compensation of the distribution network using a preset energy storage device is based on a neural network model to predict future power consumption data according to power consumption data within a preset time period, and dynamically calculate dynamic compensation power data according to the predicted data. The energy storage device then dynamically outputs power according to the dynamic compensation power data to ensure the stability of the distribution network.

[0081] In an embodiment of the present invention, the method of dynamically compensating the load of the distribution network by using a preset energy storage device includes: Extracting features from the electricity usage data to obtain features of the electricity usage data; Acquire multimodal information of the area where the distribution network is located, perform feature extraction on the multimodal information, and obtain multimodal features; Performing feature fusion on the electricity consumption data feature and the multimodal feature to obtain fused information features; Using a pre-trained electricity consumption data prediction model to predict electricity consumption data according to the fusion information features to obtain future electricity consumption data; Calculating dynamic compensation power data according to the future power consumption data; The load dynamic compensation is performed on the distribution network based on the dynamic compensation power data.

[0082] In an embodiment of the present invention, the power consumption data prediction model is a neural network model, which can predict the fluctuation of future power consumption data by capturing the fluctuation law of the fused information features. It can accurately capture the fluctuation law of the fused information features in the time series. By repeatedly learning and deeply mining the fluctuation patterns of various information features in historical data, the model can keenly perceive the impact of different factors on the fluctuation of power consumption data. When faced with the task of predicting future power consumption data, the model will make scientific and reasonable predictions on the fluctuation trend of power consumption data in the future period based on the fluctuation laws that have been mastered. For example, the fluctuation characteristics presented by the fused information features such as seasonal changes, daily power consumption time differences, special events, etc. are comprehensively considered, and the fluctuation of future power consumption data is more accurately estimated, providing strong support for relevant power decision-making and resource allocation.

[0083] In an embodiment of the present invention, the multimodal information may include temperature information, weather information and season information.

[0084] In the embodiment of the present invention, the step of calculating the dynamic compensation power data according to the future power consumption data includes: Calculate the average value of the future electricity consumption data to obtain the future electricity consumption mean value; Replacing values ​​in the future electricity consumption data that are less than or equal to the future electricity consumption mean value with 0 to obtain an initial compensation data set; The dynamic compensation power data is calculated based on the initial compensation data set using the following formula: ; in, is the first The compensation power, is the first Compensation data, is the pre-acquired harmonic current reactive power, is the preset standard power of the power distribution node. To obtain the real-time impedance in advance, is the transmission loss rate obtained in advance.

[0085] In the embodiment of the present invention, when the power consumption fluctuation index is greater than the second fluctuation threshold, the power supply stability of the distribution network is ensured by dynamically compensating the load of the distribution network using a preset energy storage device.

[0086] If the electricity consumption fluctuation index is less than or equal to the second fluctuation threshold, return to S1, obtain the power generation data of the distributed power sources in the distribution network within a preset time period, and calculate the power source fluctuation index according to the power generation data.

[0087] In an embodiment of the present invention, when the power consumption fluctuation index is less than or equal to the second fluctuation threshold, it means that the power consumption in the distribution network is stable and the power generation power of the distributed power source is stable. Therefore, it is possible to return to the step of obtaining the power generation data of the distributed power sources in the distribution network within a preset time period, and calculate the power source fluctuation index according to the power generation data, and calculate the power source fluctuation index for the next preset time period to achieve continuous monitoring and management of the distribution network.

[0088] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0089] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0091] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0092] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0093] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0094] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A distribution network source-grid-load-storage dispatching and management system, characterized in that: The system includes a data acquisition module, a data calculation module, a first judgment module and a second judgment module, wherein: The data acquisition module is used to acquire power generation data of distributed power sources in the distribution network within a preset time period, and calculate the power source fluctuation index based on the power generation data; The data calculation module is used to calculate the weight index of each power distribution node to obtain a weight index set; The first judgment module is used to judge whether the power source fluctuation index is greater than a preset first fluctuation threshold value. If the power source fluctuation index is greater than the first fluctuation threshold value, the number of cut-off nodes is determined according to the power source fluctuation index, and the distribution nodes of the distribution network are cut off according to the number of cut-off nodes and the weight index set. Otherwise, the power consumption data within a preset time period of the distribution network is obtained, and the power consumption fluctuation index is calculated according to the power consumption data; The second judgment module is used to judge whether the electricity consumption fluctuation index is greater than a preset second fluctuation threshold. If the electricity consumption fluctuation index is greater than the second fluctuation threshold, the preset energy storage device is used to dynamically compensate the load of the distribution network. Otherwise, the first judgment module is returned to obtain the power generation data of the distributed power source in the distribution network within a preset time period, and the power source fluctuation index is calculated according to the power generation data.

2. The distribution network source-grid-load-storage dispatching and management system according to claim 1, characterized in that: When the data acquisition module calculates the power source fluctuation index according to the power generation data, it is specifically used to: Acquiring wind power generation data, hydropower generation data, and photovoltaic power generation data included in the power generation data; Respectively calculating the average power generation of the wind power generation data, the hydropower generation data, and the photovoltaic power generation data within the preset time period to obtain average power data; The electric power source fluctuation index is calculated according to the wind power generation data, the hydropower generation data, the photovoltaic power generation data, and the average power data.

3. The distribution network source-grid-load-storage dispatching and management system according to claim 2, characterized in that: The calculation formula of the power source fluctuation index in the data acquisition module is as follows: ； in, is the power source fluctuation index, is the preset duration, The wind power generation data is Power generation data, The hydropower generation data Power generation data, The photovoltaic power generation data Power generation data, is the average wind power generation power included in the average power data, is the average hydroelectric power included in the average power data, It is the average photovoltaic power generation power included in the average power data.

4. The distribution network source-grid-load-storage dispatching and management system according to claim 3, characterized in that: When calculating the weight index of each power distribution node, the data calculation module is specifically used to: Obtaining total power load data of the distribution network within the preset time period; Obtain node power load data and node preset data for each power distribution node within a preset time period; The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data.

5. The distribution network source-grid-load-storage dispatching and management system according to claim 4, characterized in that: When the data calculation module calculates the weight index of each power distribution node according to the total power load data, the node power load data and the node preset data, it is specifically used to: The weight index of each power distribution node is calculated according to the total power load data, the node power load data and the node preset data using the following formula: ； in, is the weight index, The node importance parameters included in the preset data for the node, is the number of samples in the node power load data, represents the last power load data in the power load data of the node, represents the first power load data in the power load data of the node, The number of node branches included in the preset data for the node, Indicates the node power load data Power load data, Indicates the total power load data The power load data.

6. The distribution network source-grid-load-storage dispatching and management system according to claim 5, characterized in that: When the first determination module determines the number of cut-off nodes according to the power source fluctuation index, it is specifically used to: Determining whether the power source fluctuation index is less than a preset first threshold; If the power source fluctuation index is less than the first threshold, determining the number of cut-off nodes to be a preset first number; If the power source fluctuation index is greater than or equal to the first threshold, determining whether the power source fluctuation index is less than a preset second threshold; If the power source fluctuation index is less than the second threshold, determining the number of cut-off nodes to be a preset second number; If the power source fluctuation index is greater than or equal to the second threshold, determining whether the power source fluctuation index is less than a preset third threshold; If the power source fluctuation index is less than the third threshold, determining the number of cut-off nodes to be a preset third number; If the power source fluctuation index is greater than or equal to the third threshold, the number of cut-off nodes is determined to be a preset fourth number.

7. The distribution network source-grid-load-storage dispatching and management system according to claim 6, characterized in that: When the first judgment module calculates the power consumption fluctuation index according to the power consumption data, it is specifically used to: The electricity consumption fluctuation index is calculated using the following formula: ； in, is the electricity consumption fluctuation index, is the number of distribution nodes included in the power consumption data, The first The power change of each power distribution node within the preset time period, Indicates The node importance parameters of the distribution nodes, represents the preset duration, Indicates the first The total power consumption at a time point, Indicates the average power consumption included in the power consumption data.

8. The distribution network source-grid-load-storage dispatching and management system according to claim 7, characterized in that: When the second judgment module uses the preset energy storage device to dynamically compensate the load of the distribution network, it is specifically used to: Extracting features from the electricity usage data to obtain features of the electricity usage data; Acquire multimodal information of the area where the distribution network is located, perform feature extraction on the multimodal information, and obtain multimodal features; Performing feature fusion on the electricity consumption data feature and the multimodal feature to obtain fused information features; Using a pre-trained electricity consumption data prediction model to predict electricity consumption data according to the fusion information features to obtain future electricity consumption data; Calculating dynamic compensation power data according to the future power consumption data; The load dynamic compensation is performed on the distribution network based on the dynamic compensation power data.

9. The distribution network source-grid-load-storage dispatching and management system according to claim 8, characterized in that: When the second judgment module calculates the dynamic compensation power data according to the future power consumption data, it is specifically used to: Calculate the average value of the future electricity consumption data to obtain the future electricity consumption mean value; Replacing the values ​​in the future electricity consumption data that are less than or equal to the future electricity consumption mean value with 0 to obtain an initial compensation data set; The dynamic compensation power data is calculated based on the initial compensation data set using the following formula: ； in, is the first The compensation power, is the first Compensation data, is the pre-acquired harmonic current reactive power, is the preset standard power of the power distribution node. To obtain the real-time impedance in advance, is the transmission loss rate obtained in advance.

10. A distribution network source-grid-load-storage dispatching management method, characterized in that: The method comprises: Acquire power generation data of distributed power sources in the distribution network within a preset time period, and calculate a power source fluctuation index based on the power generation data; Calculate the weight index of each distribution node to obtain a weight index set; Determining whether the power source fluctuation index is greater than a preset first fluctuation threshold; If the power source fluctuation index is greater than the first fluctuation threshold, determining the number of cut-off nodes according to the power source fluctuation index, and cutting off the distribution nodes of the distribution network according to the number of cut-off nodes and the weight index set; If the power source fluctuation index is less than or equal to the first fluctuation threshold, obtaining power consumption data within a preset time period of the power distribution network, and calculating the power consumption fluctuation index according to the power consumption data; Determining whether the power consumption fluctuation index is greater than a preset second fluctuation threshold; If the power consumption fluctuation index is greater than the second fluctuation threshold, dynamically compensating the load of the distribution network by using a preset energy storage device; If the electricity consumption fluctuation index is less than or equal to the second fluctuation threshold, the method returns to the step of obtaining the power generation data of the distributed power sources in the distribution network within a preset time period and calculating the power source fluctuation index according to the power generation data.

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