Regional power grid dispatching method and system based on multi-source data

Abstract

The invention discloses a regional power grid dispatching method and system based on multi-source data, and relates to the technical field of power grid dispatching, and the method comprises the steps: obtaining regional power grid distribution information from a state grid GIS platform, deploying a dispatching system at a cloud end based on an OpenFMB framework, and collecting basic parameters, operation history, equipment history state, history environment and history power consumption information. And then, constructing a power grid load constraint model, carrying out power limitation to obtain node constraint information, and predicting future power utilization information by using an LSTM (Long Short Term Memory) model. And finally, determining a scheduling strategy by combining the real-time information, the node constraint information and the future electricity consumption information, such as starting SVG reactive compensation or transferring load according to the power condition. The system comprises an acquisition module, a master control module, a scheduling module and a display module which are clear in division of labor and collaboratively realize power grid scheduling. According to the method and the system, the problems of data transmission delay, high single-point fault risk, low dispatching efficiency and the like of centralized data storage and processing in power dispatching are effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power systems, and in particular relates to a regional power grid dispatching method and system based on multi-source data. Background Art

[0002] In the field of power systems, the rationality and efficiency of regional power grid dispatching are crucial to the stability and reliability of power supply. Traditional centralized data storage and processing methods expose many problems when dealing with large-scale and complex power demand, such as data transmission delays, which lead to delayed dispatching decisions; high risk of single point failures, where one failure may affect the entire dispatching system; low dispatching efficiency, making it difficult to quickly respond to changing power demand. These problems have seriously restricted the development of regional power grid dispatching, and new dispatching methods and systems are urgently needed to solve them.

[0003] When dealing with large-scale and complex electricity demand, existing regional power grid dispatching methods and systems rely on empirical thresholds and cannot dynamically reflect the impact of equipment aging and environmental changes on current carrying capacity, resulting in an increase in the overload risk missed detection rate. Therefore, it is necessary to provide a regional power grid dispatching method and system based on multi-source data to solve the above-mentioned problems. Summary of the invention

[0004] In order to solve the above technical problems, a regional power grid dispatching method and system based on multi-source data are provided. This technical solution solves the problem that the existing regional power grid dispatching method and system rely on empirical thresholds when dealing with large-scale and complex power demand, and cannot dynamically reflect the impact of equipment aging and environmental changes on current carrying capacity, resulting in an increase in the overload risk missed detection rate.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A regional power grid dispatching method based on multi-source data, comprising: S1. Obtain regional power grid distribution information, and deploy a regional power grid dispatching system based on the regional power grid distribution information, and use the regional power grid dispatching system to collect basic power grid parameter data, power grid operation history information, power grid equipment history status information, and history environment information of each node in the regional power grid; Wherein, each node in the regional power grid is each feeder terminal in the regional power grid; S2. Based on the basic parameter data of the power grid of each node, the historical information of the power grid operation, the historical status information of the power grid equipment and the historical environmental information, a power grid load constraint model corresponding to each node is constructed, and then the power of each node is restricted by the power grid load constraint model to obtain the power node constraint information; S3, obtaining the historical power consumption information of each node in the regional power grid, importing the historical power consumption information into the regional power grid dispatching system, and predicting the future power consumption information of each node in the current regional power grid; S4. Collect the real-time operation information, real-time status information and real-time environmental information of each node in the regional power grid, combine the power node constraint information corresponding to each node and the future power consumption information of each node in the current regional power grid, determine the optimal scheduling strategy, and then schedule the regional power grid according to the optimal scheduling strategy.

[0006] In an optional embodiment, step S1 specifically includes: Obtain three-dimensional geographic information of transmission lines from the State Grid GIS platform to determine regional power grid distribution information; According to the regional power grid distribution information, the core server is deployed in the cloud, and the regional power grid dispatching system is built on the core server based on the OpenFMB framework; The regional power grid dispatching system is used to collect the basic power grid parameter data, power grid operation history information, power grid equipment historical status information and historical environmental information of all feeder terminals in the regional power grid as the basic power grid parameter data, power grid operation history information, power grid equipment historical status information and historical environmental information of each node in the regional power grid.

[0007] In an optional embodiment, the grid load constraint model corresponding to each node is constructed based on the grid basic parameter data, grid operation history information, grid equipment history status information and history environment information of each node, specifically including: S2.1. Get any two adjacent nodes and record them as the first node and the second node; S2.2. According to the basic parameter data of the power grid of each node, the material information, cross-sectional area and laying method information of the conductor between the first node and the second node are obtained. According to the power grid operation history information of each node, the current carrying capacity of the conductor between the first node and the second node under standard conditions is obtained, and then the current carrying capacity of the conductor between the first node and the second node is determined; The calculation formula for the allowable current carrying capacity of the conductor is: In the formula, The allowable current carrying capacity of the wire between the first node and the second node, is the current carrying capacity of the wire between the first node and the second node under standard conditions, is the temperature correction coefficient, is the conductor cross-sectional area correction factor, is the laying method correction coefficient, is the environmental correction factor; S2.3. Constructing thermodynamic constraint model: In the formula, The allowable current carrying capacity of the wire between the first node and the second node, is the rated apparent power of the equipment at the first node, is the grid load limit voltage between the first node and the second node, is the temperature correction coefficient, is the historical ambient temperature, is the reference temperature, The safe current carrying capacity of the wire between the first node and the second node; S2.4, obtaining a power grid load limit voltage between the first node and the second node through a thermodynamic constraint model; S2.5. According to the historical status information of the power grid equipment at each node, the historical load loss of the transformer between the first node and the second node, the rated capacity of the transformer and the maximum allowable temperature of the transformer winding are obtained, and then the thermal stability limit of the transformer between the first node and the second node is determined; The thermal stability limit of the transformer between the first node and the second node is expressed as: In the formula, is the historical load loss of the transformer between the first node and the second node, is the maximum loss allowed in the transformer between the first node and the second node, is the rated capacity of the transformer between the first node and the second node, is the maximum allowable temperature of the transformer winding between the first node and the second node, is the historical ambient temperature, is the loss coefficient; S2.6. Obtain the maximum allowable loss of the transformer between the first node and the second node by using the expression formula of the thermal stability limit of the transformer between the first node and the second node; S2.7. Define a power grid load constraint model corresponding to the first node. The power grid load constraint model of the first node includes a calculation formula for the allowable current carrying capacity of the conductor, a thermodynamic constraint model, and an expression formula for the thermal stability limit of the transformer. After determining the calculation formula for the allowable current carrying capacity of the conductor between the first node and the second node, the thermodynamic constraint model, and the expression formula for the thermal stability limit of the transformer, the construction of the power grid load constraint model corresponding to the first node is completed, and the power grid load constraint model corresponding to each node is constructed in turn.

[0008] In an optional embodiment, the power restriction of each node is performed through the power grid load constraint model to obtain power node constraint information, specifically including: S3.1. Obtain the calculation formula of the allowable current carrying capacity of the conductor corresponding to each node, the thermodynamic constraint model and the expression formula of the thermal stability limit of the transformer through the power grid load constraint model, and then obtain the power node constraint model: In the formula, is the stability limit current of the dth node, is the allowable current carrying capacity of the wire at the dth node, is the grid load limit voltage of the dth node, is the maximum allowable loss of the transformer at the dth node, is the load power factor; S3.2, obtaining the line impedance and line reactance corresponding to the dth node through the basic parameter data of the power grid of each node; S3.3. Obtain the power node constraint information corresponding to the dth node by combining the line impedance and line reactance corresponding to the dth node through the power node constraint model: In the formula, is the measured real-time line power of the current d-th node, is the maximum power allowed for thermal stability of the dth node, is the line impedance corresponding to the dth node, is the line reactance corresponding to the dth node, , is the load power factor.

[0009] In an optional embodiment, the acquiring of historical power consumption information of each node in the regional power grid, importing the historical power consumption information into the regional power grid dispatching system, and predicting future power consumption information of each node in the current regional power grid specifically includes: Through the feeder terminals of each node in the regional power grid, historical power consumption information corresponding to each node for at least 7 days is collected in sequence, and the historical power consumption information includes historical power, voltage, current, temperature and holiday tags within the past 24 hours; Build an LSTM model and use historical electricity consumption information to train the LSTM model to obtain a future electricity consumption prediction model; The temperature and holiday labels of the area where any node is located in the next 24 hours are obtained, and the temperature and holiday labels of the area where any node is located in the next 24 hours are input into the future power consumption prediction model to obtain the power prediction value of the node in the next hour output by the future power consumption prediction model, and then determine the future power consumption information of each node in the current regional power grid.

[0010] In an optional embodiment, step S4 specifically includes: Through the feeder terminals of each node in the regional power grid, the real-time operation information, real-time status information of the power grid equipment and real-time environmental information corresponding to each node are collected; Obtain the grid load constraint model corresponding to each node through the real-time operation information, real-time status information of the grid equipment and real-time environmental information corresponding to each node; The power node constraint model corresponding to each node is obtained through the power grid load constraint model corresponding to each node, and then the line impedance and line reactance corresponding to each node are obtained through the basic parameter data of the power grid of each node, and then the power node constraint information corresponding to each node is obtained; Based on the future power consumption information of each node in the current regional power grid, determine the power forecast value of each node in the next hour ; Obtain the real-time power of the line corresponding to each node through the power node constraint information corresponding to each node and thermal stability allowable maximum power ; like , then start SVG reactive power compensation to improve voltage stability; like , then the user load in the overloaded area corresponding to the node is transferred to The adjacent lines of the node are monitored and the users in the overloaded area corresponding to the node are observed to see whether the overload continues. If so, the non-critical loads are remotely cut off. If not, the monitoring continues.

[0011] Furthermore, a regional power grid dispatching system based on multi-source data is proposed to implement the above-mentioned dispatching method, including: The acquisition module is used to obtain regional power grid distribution information and collect information about each The basic parameter data of the node, the historical information of the grid operation, the historical status information of the grid equipment and the historical environmental information are also used to collect the real-time operation information, real-time status information and real-time environmental information of each node in the regional grid; A main control module, which is used to perform data preprocessing on the data and information collected by the collection module, and to build a power grid load constraint model corresponding to each node based on the basic parameter data of the power grid, the historical information of the power grid operation, the historical status information of the power grid equipment and the historical environmental information of each node; The scheduling module is used to limit the power consumption of each node through the power grid load constraint model. The power node constraint information is obtained by combining the power node constraint information corresponding to each node and the future power consumption information of each node in the current regional power grid to determine the optimal dispatching strategy, and then the regional power grid is dispatched according to the optimal dispatching strategy; The display module is used to display the acquisition process and results of the acquisition module, and to display the main The data processing process and results of the control module are used to display the constraint process and constraint results of the scheduling module, as well as the scheduling process and results of the scheduling module.

[0012] In an optional embodiment, the acquisition module includes: The first acquisition unit is used to obtain regional power grid distribution information, collect regional power grid distribution information, Basic grid parameter data, grid operation history information, grid equipment history status information and historical environment information of each node in the grid; The second collection unit is used to collect real-time operation information of each node in the regional power grid, real-time status information of power grid equipment and real-time environmental information.

[0013] In an optional embodiment, the main control module includes: A data processing unit is used to process the data and information collected by the acquisition module. Pretreatment; The model building unit is used to build a power grid load constraint model corresponding to each node based on the basic parameter data of the power grid, the historical information of the power grid operation, the historical status information of the power grid equipment and the historical environmental information of each node.

[0014] In an optional embodiment, the scheduling module includes: The constraint unit is used to limit the power of each node through the power grid load constraint model. Control, obtain power node constraint information; The scheduling unit is used to combine the power node constraint information corresponding to each node and the current The future electricity consumption information of each node in the regional power grid is used to determine the optimal dispatching strategy, and then the regional power grid is dispatched according to the optimal dispatching strategy.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This scheme proposes a regional power grid dispatching method and system based on multi-source data. By integrating GIS three-dimensional geographic information, historical status of power grid equipment, ambient temperature and other multi-dimensional data, a node-level power node constraint model including conductor current carrying capacity (including temperature / laying method correction), transformer thermal stability limit, and thermodynamic constraints is constructed to achieve quantitative evaluation of all working conditions of power grid equipment. During dispatching, it does not rely on empirical thresholds, dynamically reflects the impact of equipment aging and environmental changes on current carrying capacity, and reduces overload risks and missed detection rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a regional power grid dispatching method based on multi-source data proposed in this scheme; Figure 2 This is a flow chart for constructing the power grid load constraint model in this scheme; Figure 3 This is a flowchart for obtaining power node constraint information in this solution; Figure 4 This is a system block diagram of a regional power grid dispatching system based on multi-source data proposed for this scheme. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 - Figure 4 As shown, a regional power grid dispatching method based on multi-source data includes: S1. Obtain regional power grid distribution information, and deploy a regional power grid dispatching system based on the regional power grid distribution information, and use the regional power grid dispatching system to collect basic power grid parameter data, power grid operation history information, power grid equipment history status information, and history environment information of each node in the regional power grid; Among them, each node in the regional power grid is each feeder terminal in the regional power grid; S2. Based on the basic parameter data of the power grid of each node, the historical information of the power grid operation, the historical status information of the power grid equipment and the historical environmental information, a power grid load constraint model corresponding to each node is constructed, and then the power of each node is restricted by the power grid load constraint model to obtain the power node constraint information; S3, obtaining the historical power consumption information of each node in the regional power grid, importing the historical power consumption information into the regional power grid dispatching system, and predicting the future power consumption information of each node in the current regional power grid; S4. Collect the real-time operation information, real-time status information and real-time environmental information of each node in the regional power grid, combine the power node constraint information corresponding to each node and the future power consumption information of each node in the current regional power grid, determine the optimal scheduling strategy, and then schedule the regional power grid according to the optimal scheduling strategy.

[0019] Specifically, the feeder terminal is a device installed near the switchgear of the feeder loop of the distribution network to monitor, control and protect the feeder switch. It can collect the voltage, current, power and other operating parameters of the feeder, realize the fault detection, location and isolation of the feeder, and remote control of the switch, so as to improve the reliability and automation level of the distribution network.

[0020] Furthermore, step S1 specifically includes: Obtain three-dimensional geographic information of transmission lines from the State Grid GIS platform to determine regional power grid distribution information; According to the regional power grid distribution information, the core server is deployed in the cloud, and the regional power grid dispatching system is built on the core server based on the OpenFMB framework; The regional power grid dispatching system is used to collect the basic power grid parameter data, power grid operation history information, power grid equipment historical status information and historical environmental information of all feeder terminals in the regional power grid as the basic power grid parameter data, power grid operation history information, power grid equipment historical status information and historical environmental information of each node in the regional power grid.

[0021] Furthermore, based on the basic parameter data of the power grid, the historical information of the power grid operation, the historical status information of the power grid equipment and the historical environmental information of each node, a power grid load constraint model corresponding to each node is constructed, specifically including: S2.1. Get any two adjacent nodes and record them as the first node and the second node; S2.2. According to the basic parameter data of the power grid of each node, the material information, cross-sectional area and laying method information of the conductor between the first node and the second node are obtained. According to the power grid operation history information of each node, the current carrying capacity of the conductor between the first node and the second node under standard conditions is obtained, and then the current carrying capacity of the conductor between the first node and the second node is determined; The calculation formula for the allowable current carrying capacity of the conductor is: In the formula, The allowable current carrying capacity of the wire between the first node and the second node, is the current carrying capacity of the wire between the first node and the second node under standard conditions, is the temperature correction coefficient, is the conductor cross-sectional area correction factor, is the laying method correction coefficient, is the environmental correction factor; Specifically, the current carrying capacity of conductors under standard conditions can be referred to IEC 60364 or GB 50545. is the temperature correction coefficient, ,in, is the conductor resistance temperature coefficient of the wire, is the actual ambient temperature in the historical environmental information. It is the wire cross-sectional area correction factor, which is related to the shape of the wire, such as round wire. . Correction factor for installation method, e.g. overhead line , direct buried line . It is the environmental correction factor, which is related to wind speed, sunshine, etc. For example, when there is no wind and no sunshine, .

[0022] For example, if a node uses LGJ-120 aluminum core steel stranded wire, its standard current carrying capacity is , assuming that the ambient temperature in the historical environmental information , conductor resistance temperature coefficient ,but .

[0023] S2.3. Constructing thermodynamic constraint model: In the formula, The allowable current carrying capacity of the wire between the first node and the second node, is the rated apparent power of the equipment at the first node, is the grid load limit voltage between the first node and the second node, is the temperature correction coefficient, is the historical ambient temperature, is the reference temperature, The safe current carrying capacity of the wire between the first node and the second node; Specifically, the safe current carrying capacity of the wire is determined by the material properties of the wire, and the rated apparent power of the equipment at the first node is the rated apparent power of the transformer closest to the first node.

[0024] For example, the rated apparent power S of the transformer closest to the first node is max =630kVA, the allowable current carrying capacity of the wire between the first node and the second node , reference temperature T ref =20℃, the calculated grid load limit voltage V=10kV, S2.4, obtaining a power grid load limit voltage between the first node and the second node through a thermodynamic constraint model; S2.5. According to the historical status information of the power grid equipment at each node, the historical load loss of the transformer between the first node and the second node, the rated capacity of the transformer and the maximum allowable temperature of the transformer winding are obtained, and then the thermal stability limit of the transformer between the first node and the second node is determined; The thermal stability limit of the transformer between the first node and the second node is expressed as: In the formula, is the historical load loss of the transformer between the first node and the second node, is the maximum loss allowed in the transformer between the first node and the second node, is the rated capacity of the transformer between the first node and the second node, is the maximum allowable temperature of the transformer winding between the first node and the second node, is the historical ambient temperature, is the loss coefficient; Specifically, the historical load loss of the transformer between the first node and the second node is collected from the transformer closest to the first node. Similarly, the rated capacity of the transformer between the first node and the second node is also collected from the transformer closest to the first node.

[0025] For example, if a 10kV / 0.4kV transformer (S base =1000kVA), historical ambient temperature 30 , the transformer historical load loss P between the first node and the second node loss =12kW: then P max =3⋅1000⋅(95−30)=202,500W=202.5kW. Since 12kW≤202.5kW, the transformer is in a safe state.

[0026] S2.6. Obtain the maximum allowable loss of the transformer between the first node and the second node by using the expression formula of the thermal stability limit of the transformer between the first node and the second node; S2.7. Define the grid load constraint model corresponding to the first node. The grid load constraint model of the first node includes a calculation formula for the allowable current carrying capacity of the conductor, a thermodynamic constraint model, and an expression formula for the thermal stability limit of the transformer. After determining the calculation formula for the allowable current carrying capacity of the conductor between the first node and the second node, the thermodynamic constraint model, and the expression formula for the thermal stability limit of the transformer, the construction of the grid load constraint model corresponding to the first node is completed, and the grid load constraint model corresponding to each node is constructed in turn.

[0027] Furthermore, the power of each node is restricted by the power grid load constraint model to obtain power node constraint information, including: S3.1. Obtain the calculation formula of the allowable current carrying capacity of the conductor corresponding to each node, the thermodynamic constraint model and the expression formula of the thermal stability limit of the transformer through the power grid load constraint model, and then obtain the power node constraint model: In the formula, is the stability limit current of the dth node, is the allowable current carrying capacity of the wire at the dth node, is the grid load limit voltage of the dth node, is the maximum allowable loss of the transformer at the dth node, is the load power factor; For example, the wire between the first node and the second node: Model LGJ-70 (standard current carrying capacity I z0 =320A, temperature coefficient α=0.004 / ℃), ambient temperature: T=35℃, transformer: capacity S=630kVA, load loss =8kW, power factor , calculate: =320⋅(1+0.004⋅15)≈342A, =8kW, =10kV, .

[0028] S3.2, obtaining the line impedance and line reactance corresponding to the dth node through the basic parameter data of the power grid of each node; S3.3. Obtain the power node constraint information corresponding to the dth node by combining the line impedance and line reactance corresponding to the dth node through the power node constraint model: In the formula, is the measured real-time line power of the current d-th node, is the maximum power allowed for thermal stability of the dth node, is the line impedance corresponding to the dth node, is the line reactance corresponding to the dth node, , is the load power factor.

[0029] Specifically, the maximum thermal power allowed by the line (determined by the conductor current carrying capacity and transformer loss, unit: kW), is the measured real-time power of the line at the current dth node, which is determined by the combined effect of resistance and reactance of the line: , in, is the measured real-time line current of the current d-th node.

[0030] Furthermore, the historical power consumption information of each node in the regional power grid is obtained, and the historical power consumption information is imported into the regional power grid dispatching system to predict the future power consumption information of each node in the current regional power grid, including: Through the feeder terminals of each node in the regional power grid, historical power consumption information corresponding to each node for at least 7 days is collected in turn. The historical power consumption information includes historical power, voltage, current, temperature and holiday tags within the past 24 hours; Build an LSTM model and use historical electricity consumption information to train the LSTM model to obtain a future electricity consumption prediction model; The temperature and holiday labels of the area where any node is located in the next 24 hours are obtained, and the temperature and holiday labels of the area where any node is located in the next 24 hours are input into the future power consumption prediction model to obtain the power prediction value of the node in the next hour output by the future power consumption prediction model, and then determine the future power consumption information of each node in the current regional power grid.

[0031] Furthermore, step S4 specifically includes: Through the feeder terminals of each node in the regional power grid, the real-time operation information, real-time status information of the power grid equipment and real-time environmental information corresponding to each node are collected; Obtain the grid load constraint model corresponding to each node through the real-time operation information, real-time status information of the grid equipment and real-time environmental information corresponding to each node; The power node constraint model corresponding to each node is obtained through the power grid load constraint model corresponding to each node, and then the line impedance and line reactance corresponding to each node are obtained through the basic parameter data of the power grid of each node, and then the power node constraint information corresponding to each node is obtained; Based on the future power consumption information of each node in the current regional power grid, determine the power forecast value of each node in the next hour ; Obtain the real-time power of the line corresponding to each node through the power node constraint information corresponding to each node and thermal stability allowable maximum power ; like , then start SVG reactive power compensation to improve voltage stability; like , then the user load in the overloaded area corresponding to the node is transferred to The adjacent lines of the node are monitored and the users in the overloaded area corresponding to the node are observed to see whether the overload continues. If so, the non-critical loads are remotely cut off. If not, the monitoring continues.

[0032] Specifically, the optimal scheduling strategy formulation needs to combine real-time data with prediction results to optimize scheduling, and obtain real-time data such as current voltage, current, and ambient temperature through the feeder terminal. For example, the real-time current of a line is 320A, which exceeds the thermal stability limit of 300A, triggering an overload alarm. Constraint verification and strategy selection: When the voltage is unstable, the power prediction condition is met. , start SVG reactive power compensation and increase voltage. When the line is overloaded, the condition is met , transfer the overloaded area load to the adjacent line and cut off non-critical loads (such as air conditioners). For example, when a feeder terminal at a node finds that a line is predicted to be overloaded, it automatically switches 100kW load to the backup line to avoid tripping.

[0033] Furthermore, a regional power grid dispatching system based on multi-source data is proposed to implement the above-mentioned dispatching method, including: The acquisition module is used to obtain regional power grid distribution information, collect basic power grid parameter data, power grid operation history information, power grid equipment historical status information and historical environmental information of each node in the regional power grid, and is also used to collect real-time operation information, real-time status information of power grid equipment and real-time environmental information of each node in the regional power grid; The main control module is used to pre-process the data and information collected by the acquisition module, and build a grid load constraint model corresponding to each node based on the basic grid parameter data, grid operation history information, grid equipment history status information and historical environment information of each node; The dispatching module is used to restrict the power of each node through the power grid load constraint model to obtain the power node constraint information, and is also used to combine the power node constraint information corresponding to each node and the future power consumption information of each node in the current regional power grid to determine the optimal dispatching strategy, and then dispatch the regional power grid according to the optimal dispatching strategy; Display module, the display module is used to display the acquisition process and results of the acquisition module, to display the data processing process and results of the main control module, to display the constraint process and constraint results of the scheduling module, and to display the scheduling process and results of the scheduling module.

[0034] Furthermore, the acquisition module includes: The first acquisition unit is used to obtain regional power grid distribution information and collect information about the regional power grid. Basic grid parameter data of each node, historical grid operation information, historical grid equipment status information and historical environmental information; The second acquisition unit is used to collect real-time operation information of each node in the regional power grid, real-time status information of power grid equipment and real-time environmental information.

[0035] Furthermore, the main control module includes: A data processing unit, the data processing unit is used to perform data preprocessing on the data and information collected by the acquisition module; Model building unit, the model building unit is used to build the basic parameter data of the power grid based on each node, the power grid Operation history information, power grid equipment historical status information and historical environment information, build the power grid corresponding to each node Network load constraint model.

[0036] Furthermore, the scheduling module includes: Constraint unit, which is used to restrict the power of each node through the grid load constraint model. Obtain power node constraint information; The dispatching unit is used to combine the power node constraint information corresponding to each node and the current area The future electricity consumption information of each node in the power grid is used to determine the optimal dispatching strategy, and then the regional power grid is dispatched according to the optimal dispatching strategy.

[0037] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A regional power grid dispatching method based on multi-source data, characterized in that: include: S1. Obtain regional power grid distribution information, and deploy a regional power grid dispatching system based on the regional power grid distribution information, and use the regional power grid dispatching system to collect basic power grid parameter data, power grid operation history information, power grid equipment history status information, and history environment information of each node in the regional power grid; Wherein, each node in the regional power grid is each feeder terminal in the regional power grid; S2. Based on the basic parameter data of the power grid of each node, the historical information of the power grid operation, the historical status information of the power grid equipment and the historical environmental information, a power grid load constraint model corresponding to each node is constructed, and then the power of each node is restricted by the power grid load constraint model to obtain the power node constraint information; S3, obtaining the historical power consumption information of each node in the regional power grid, importing the historical power consumption information into the regional power grid dispatching system, and predicting the future power consumption information of each node in the current regional power grid; S4. Collect the real-time operation information, real-time status information and real-time environmental information of each node in the regional power grid, combine the power node constraint information corresponding to each node and the future power consumption information of each node in the current regional power grid, determine the optimal scheduling strategy, and then schedule the regional power grid according to the optimal scheduling strategy.

2. A regional power grid dispatching method based on multi-source data according to claim 1, characterized in that: Step S1 specifically includes: Obtain three-dimensional geographic information of transmission lines from the State Grid GIS platform to determine regional power grid distribution information; According to the regional power grid distribution information, the core server is deployed in the cloud, and the regional power grid dispatching system is built on the core server based on the OpenFMB framework; The regional power grid dispatching system is used to collect the basic power grid parameter data, power grid operation history information, power grid equipment historical status information and historical environmental information of all feeder terminals in the regional power grid as the basic power grid parameter data, power grid operation history information, power grid equipment historical status information and historical environmental information of each node in the regional power grid.

3. A regional power grid dispatching method based on multi-source data according to claim 1, characterized in that: The grid load constraint model corresponding to each node is constructed based on the grid basic parameter data, grid operation history information, grid equipment history status information and history environment information of each node, specifically including: S2.

1. Get any two adjacent nodes and record them as the first node and the second node; S2.

2. According to the basic parameter data of the power grid of each node, the material information, cross-sectional area and laying method information of the conductor between the first node and the second node are obtained. According to the power grid operation history information of each node, the current carrying capacity of the conductor between the first node and the second node under standard conditions is obtained, and then the current carrying capacity of the conductor between the first node and the second node is determined; The calculation formula for the allowable current carrying capacity of the conductor is: In the formula, The allowable current carrying capacity of the wire between the first node and the second node, is the current carrying capacity of the wire between the first node and the second node under standard conditions, is the temperature correction coefficient, is the conductor cross-sectional area correction factor, is the laying method correction coefficient, is the environmental correction factor; S2.

3. Constructing thermodynamic constraint model: In the formula, The allowable current carrying capacity of the wire between the first node and the second node, is the rated apparent power of the equipment at the first node, is the grid load limit voltage between the first node and the second node, is the temperature correction coefficient, is the historical ambient temperature, is the reference temperature, The safe current carrying capacity of the wire between the first node and the second node; S2.4, obtaining a power grid load limit voltage between the first node and the second node through a thermodynamic constraint model; S2.

5. According to the historical status information of the power grid equipment at each node, the historical load loss of the transformer between the first node and the second node, the rated capacity of the transformer and the maximum allowable temperature of the transformer winding are obtained, and then the thermal stability limit of the transformer between the first node and the second node is determined; The thermal stability limit of the transformer between the first node and the second node is expressed as: In the formula, is the historical load loss of the transformer between the first node and the second node, is the maximum loss allowed in the transformer between the first node and the second node, is the rated capacity of the transformer between the first node and the second node, is the maximum allowable temperature of the transformer winding between the first node and the second node, is the historical ambient temperature, is the loss coefficient; S2.

6. Obtain the maximum allowable loss of the transformer between the first node and the second node by using the expression formula of the thermal stability limit of the transformer between the first node and the second node; S2.

7. Define a power grid load constraint model corresponding to the first node. The power grid load constraint model of the first node includes a calculation formula for the allowable current carrying capacity of the conductor, a thermodynamic constraint model, and an expression formula for the thermal stability limit of the transformer. After determining the calculation formula for the allowable current carrying capacity of the conductor between the first node and the second node, the thermodynamic constraint model, and the expression formula for the thermal stability limit of the transformer, the construction of the power grid load constraint model corresponding to the first node is completed, and the power grid load constraint model corresponding to each node is constructed in turn.

4. A regional power grid dispatching method based on multi-source data according to claim 3, characterized in that: The power restriction of each node is performed through the power grid load constraint model to obtain power node constraint information, specifically including: S3.

1. Obtain the calculation formula of the allowable current carrying capacity of the conductor corresponding to each node, the thermodynamic constraint model and the expression formula of the thermal stability limit of the transformer through the power grid load constraint model, and then obtain the power node constraint model: In the formula, is the stability limit current of the dth node, is the allowable current carrying capacity of the wire at the dth node, is the grid load limit voltage of the dth node, is the maximum allowable loss of the transformer at the dth node, is the load power factor; S3.2, obtaining the line impedance and line reactance corresponding to the dth node through the basic parameter data of the power grid of each node; S3.

3. Obtain the power node constraint information corresponding to the dth node by combining the line impedance and line reactance corresponding to the dth node through the power node constraint model: In the formula, is the measured real-time line power of the current d-th node, is the maximum power allowed for thermal stability of the dth node, is the line impedance corresponding to the dth node, is the line reactance corresponding to the dth node, , is the load power factor.

5. A regional power grid dispatching method based on multi-source data according to claim 1, characterized in that: The obtaining of historical power consumption information of each node in the regional power grid, importing the historical power consumption information into the regional power grid dispatching system, and predicting the future power consumption information of each node in the current regional power grid specifically includes: Through the feeder terminals of each node in the regional power grid, historical power consumption information corresponding to each node for at least 7 days is collected in sequence, and the historical power consumption information includes historical power, voltage, current, temperature and holiday tags within the past 24 hours; Build an LSTM model and use historical electricity consumption information to train the LSTM model to obtain a future electricity consumption prediction model; The temperature and holiday labels of the area where any node is located in the next 24 hours are obtained, and the temperature and holiday labels of the area where any node is located in the next 24 hours are input into the future power consumption prediction model to obtain the power prediction value of the node in the next hour output by the future power consumption prediction model, and then determine the future power consumption information of each node in the current regional power grid.

6. A regional power grid dispatching method based on multi-source data according to claim 4, characterized in that: Step S4 specifically includes: Through the feeder terminals of each node in the regional power grid, the real-time operation information, real-time status information of the power grid equipment and real-time environmental information corresponding to each node are collected; Obtain the grid load constraint model corresponding to each node through the real-time operation information, real-time status information of the grid equipment and real-time environmental information corresponding to each node; The power node constraint model corresponding to each node is obtained through the power grid load constraint model corresponding to each node, and then the line impedance and line reactance corresponding to each node are obtained through the basic parameter data of the power grid of each node, and then the power node constraint information corresponding to each node is obtained; Based on the future power consumption information of each node in the current regional power grid, determine the power forecast value of each node in the next hour ; Obtain the real-time power of the line corresponding to each node through the power node constraint information corresponding to each node and thermal stability allowable maximum power ; like , then start SVG reactive power compensation to improve voltage stability; like , then the user load in the overloaded area corresponding to the node is transferred to The adjacent lines of the node are monitored and the users in the overloaded area corresponding to the node are observed to see whether the overload continues. If so, the non-critical loads are remotely cut off. If not, the monitoring continues.

7. A regional power grid dispatching system based on multi-source data, characterized in that: A method for implementing a regional power grid dispatching method as claimed in any one of claims 1 to 6, comprising: The acquisition module is used to obtain regional power grid distribution information and collect information about each The node's basic grid parameter data, grid operation history information, grid equipment historical status information and historical environmental information are also used to collect real-time operation information, grid equipment real-time status information and real-time environmental information of each node in the regional grid; A main control module, which is used to perform data preprocessing on the data and information collected by the collection module, and to build a power grid load constraint model corresponding to each node based on the basic parameter data of the power grid, the historical information of the power grid operation, the historical status information of the power grid equipment and the historical environmental information of each node; The scheduling module is used to limit the power consumption of each node through the power grid load constraint model. The power node constraint information is obtained by combining the power node constraint information corresponding to each node and the future power consumption information of each node in the current regional power grid to determine the optimal dispatching strategy, and then the regional power grid is dispatched according to the optimal dispatching strategy; The display module is used to display the acquisition process and results of the acquisition module, and to display the main The data processing process and results of the control module are used to display the constraint process and constraint results of the scheduling module, as well as the scheduling process and results of the scheduling module.

8. A regional power grid dispatching system based on multi-source data according to claim 7, characterized in that: The acquisition module comprises: The first acquisition unit is used to obtain regional power grid distribution information, collect regional power grid distribution information, Basic grid parameter data, grid operation history information, grid equipment history status information and historical environment information of each node in the grid; The second collection unit is used to collect real-time operation information of each node in the regional power grid, real-time status information of power grid equipment and real-time environmental information.

9. A regional power grid dispatching system based on multi-source data according to claim 7, characterized in that: The main control module comprises: A data processing unit is used to process the data and information collected by the acquisition module. Pretreatment; A model building unit, the model building unit is used to build a grid based on basic parameter data of each node, The grid operation history information, grid equipment status history information and historical environment information are used to build a grid load constraint model corresponding to each node.

10. A regional power grid dispatching system based on multi-source data according to claim 7, characterized in that: The scheduling module includes: The constraint unit is used to limit the power of each node through the power grid load constraint model. Control, obtain power node constraint information; The scheduling unit is used to combine the power node constraint information corresponding to each node and the current The future electricity consumption information of each node in the regional power grid is used to determine the optimal dispatching strategy, and then the regional power grid is dispatched according to the optimal dispatching strategy.

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