An intelligent power supply and distribution management system and method for a large-scale power grid

Abstract

The present application provides an intelligent power supply and distribution management system and method for a large-scale power grid. The historical power supply information is divided into internal power supply loads and external power supply loads according to the access mode of each power supply source in the power microgrid, and the load isolation characteristics of the power microgrid in the grid-connected mode are determined through the external power supply loads and the internal power supply loads; the short-term load and long-term load of the power microgrid in the off-grid mode are predicted based on the distribution load information, the load margin of the power microgrid in the off-grid mode is determined according to the long-term load and the load isolation characteristics, and the load amplitude of the power microgrid in the off-grid mode is determined through the load margin and the short-term load; when the load amplitude is lower than the load upper limit of the power microgrid, the scheduling mode of the power microgrid in the large-scale power grid is automatically switched from the grid-connected mode to the off-grid mode. Based on the above solution, the automatic switching of the scheduling mode of the power microgrid in the large-scale power grid can be realized, thereby improving the energy utilization rate in the power microgrid.

Description

Technical Field

[0001] This application relates to the technical field of power supply and distribution scheduling for large-scale power grids. More specifically, this application relates to an intelligent power supply and distribution management system and method for large-scale power grids. Background Art

[0002] With the rapid growth of power demand and the wide access of renewable energy, the power supply and distribution systems of large-scale power grids have become increasingly complex. Traditional power grid scheduling methods mainly rely on centralized control systems to conduct overall scheduling for the power generation side, transmission side, and distribution side. However, with the wide application of distributed energy sources such as wind energy and solar energy, the traditional centralized scheduling method faces various challenges. There are a large number of power microgrids in large-scale power grids. Power microgrids can operate independently or supply power in parallel with the main power grid, forming a highly flexible power supply mode.

[0003] In the prior art, the switching of the scheduling mode (grid-connected to off-grid) of power microgrids in large-scale power grids mostly relies on manual intervention or simple control logic based on preset conditions. Due to the simplicity of manual operation or preset conditions, power microgrids cannot respond quickly when external grid failures or load fluctuations occur, which may lead to power outages or load instability. The traditional mode cannot flexibly switch according to the real-time operating status of power microgrids, internal power supply capabilities, and external load changes, resulting in low energy resource utilization rates. There may be over-reliance on the main power grid or neglect of the renewable energy potential within power microgrids. Therefore, how to achieve automatic switching of the scheduling mode of power microgrids in large-scale power grids to improve the energy utilization rate in power microgrids is a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides an intelligent power supply and distribution management system and method for large-scale power grids, which can achieve automatic switching of the scheduling mode of power microgrids in large-scale power grids, thereby improving the energy utilization rate in power microgrids.

[0005] In a first aspect, this application provides a method for switching the scheduling mode of a power microgrid in a large-scale power grid, including:

[0006] Monitoring the power microgrid in the grid-connected mode of the large-scale power grid, and collecting the historical power supply information and distribution load information of the power microgrid within a specified time period;

[0007] Dividing the historical power supply information into internal power supply loads and external power supply loads according to the access mode of each power source in the power microgrid, and determining the load isolation characteristics of the power microgrid in the grid-connected mode through the external power supply loads and the internal power supply loads;

[0008] Predicting the near-term load and the long-term load of the power microgrid in the off-grid mode based on the distribution load information and the grid load prediction model, determining the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristic, and determining the load amplitude of the power microgrid in the off-grid mode according to the load margin and the near-term load;

[0009] When the load amplitude is lower than the load upper limit of the power microgrid, the dispatching mode of the power microgrid in the large-scale power grid is automatically switched from the grid-connected mode to the off-grid mode.

[0010] In some embodiments, dividing the historical power supply information into internal power supply loads and external power supply loads according to the access mode of each power supply source in the power microgrid specifically includes:

[0011] For each power supply in the power microgrid, the power source of the power supply is determined according to the access mode of the power supply;

[0012] If the power source is from within the power microgrid, the power supply of the power supply source in the historical power supply information is used as the internal power supply;

[0013] If the power source is outside the power microgrid, the power supply of the power supply source in the historical power supply information is used as the external power supply;

[0014] Then all internal power supplies and all external power supplies are obtained;

[0015] The internal power supply load is determined based on all internal power supplies, and the external power supply load is determined based on all external power supplies.

[0016] In some embodiments, determining the load isolation characteristics of the power microgrid in the grid-connected mode by using the externally powered load and the internally powered load specifically includes:

[0017] determining a temporal correlation between the externally powered load and the internally powered load;

[0018] determining the external dependency of the power microgrid in the grid-connected mode based on the external power supply load;

[0019] A load isolation characteristic of the power microgrid in a grid-connected mode is determined according to the time correlation and the external dependency.

[0020] In some embodiments, predicting the short-term load and long-term load of the power microgrid in the off-grid mode based on the distribution load information and the grid load forecasting model specifically includes:

[0021] Determining a dispatching load of the power microgrid in a grid-connected mode according to the distribution load information;

[0022] determining a load stability value of the power microgrid based on the dispatching load and the load isolation characteristic;

[0023] When the load stability value is lower than the load upper limit of the power microgrid, a grid load prediction model is constructed according to the distribution load information;

[0024] Based on the grid load prediction model, the short-term load and long-term load of the power microgrid in the off-grid mode are predicted.

[0025] In some embodiments, determining the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristics specifically includes:

[0026] Obtain the internal power supply of the power microgrid in the off-grid mode;

[0027] Determine the power supply capacity of the power microgrid according to the internal power supply and the load isolation characteristics;

[0028] Determine the load margin of the power microgrid in the off-grid mode based on the power supply capacity and the long-term load.

[0029] In some embodiments, determining the load amplitude of the power microgrid in the off-grid mode through the load margin and the short-term load specifically includes:

[0030] Obtain the load stability of the power microgrid in the off-grid mode;

[0031] Determine the limit fluctuation value of the load of the power microgrid in the off-grid mode according to the load stability and the load margin;

[0032] Determine the load amplitude of the power microgrid in the off-grid mode through the limit fluctuation value and the short-term load.

[0033] In some embodiments, the large-scale power grid is a 750 kV AC power transmission network based on wind power generation.

[0034] In a second aspect, the present application provides an intelligent power supply and distribution management system for a large-scale power grid, including a scheduling mode switching unit, and the scheduling mode switching unit includes:

[0035] An acquisition module, configured to acquire the historical power supply information and distribution load information of the power microgrid within a specified time period;

[0036] A processing module, configured to divide the historical power supply information into internal power supply loads and external power supply loads according to the access mode of each power supply source in the power microgrid, and determine the load isolation characteristics of the power microgrid in the grid-connected mode through the external power supply loads and the internal power supply loads;

[0037] The processing module is further configured to predict the short-term load and long-term load of the power microgrid in the off-grid mode based on the distribution load information in combination with the grid load prediction model, determine the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristic, and determine the load amplitude of the power microgrid in the off-grid mode through the load margin and the short-term load.

[0038] An execution module, configured to automatically switch the scheduling mode of the power microgrid in the large-scale power grid from the grid-connected mode to the off-grid mode when the load amplitude is lower than the load upper limit of the power microgrid.

[0039] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned scheduling mode switching method of the power microgrid in the large-scale power grid.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer is enabled to execute the above-mentioned scheduling mode switching method of the power microgrid in the large-scale power grid.

[0041] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects:

[0042] In an intelligent power supply and distribution management system and method for a large-scale power grid provided by the present application, the power microgrid in the large-scale power grid in the grid-connected mode is monitored, and the historical power supply information and distribution load information of the power microgrid within a specified time period are collected;

[0043] The historical power supply information is divided into internal power supply loads and external power supply loads according to the access modes of each power supply source in the power microgrid, and the load isolation characteristic of the power microgrid in the grid-connected mode is determined through the external power supply loads and the internal power supply loads;

[0044] Based on the distribution load information in combination with the grid load prediction model, the short-term load and long-term load of the power microgrid in the off-grid mode are predicted, the load margin of the power microgrid in the off-grid mode is determined according to the long-term load and the load isolation characteristic, and the load amplitude of the power microgrid in the off-grid mode is determined through the load margin and the short-term load;

[0045] When the load amplitude is lower than the load upper limit of the power microgrid, the scheduling mode of the power microgrid in the large-scale power grid is automatically switched from the grid-connected mode to the off-grid mode.

[0046] It can be seen that in the present application, when the load amplitude is lower than the load upper limit of the power microgrid, the scheduling mode of the power microgrid in the large-scale power grid is automatically switched from the grid-connected mode to the off-grid mode; First, the determination of the load isolation characteristic is based on the analysis of the internal power supply load and the external power supply load. Through the evaluation of time correlation and external dependence, the independence of the power microgrid in the grid-connected mode can be quantified. The load isolation characteristic determines whether the power microgrid can maintain stable operation in the face of external grid fluctuations and under what conditions it can operate independently (off-grid mode). The stronger the load isolation characteristic, the less dependent the power microgrid is on external power supply, which helps to reduce the impact of external fluctuations on the internal system and improve the independent operation ability and energy utilization efficiency of the power microgrid; Then, the determination of the load amplitude depends on the calculation of the load margin and the recent load. The load amplitude reflects the load fluctuation range that the power microgrid can withstand in the off-grid mode. Through reasonable amplitude management, the power microgrid can maintain stable operation during load fluctuations, not only avoiding excessive energy consumption but also ensuring the optimal allocation of energy. When the load amplitude is lower than the load upper limit of the power microgrid, the power microgrid can be automatically switched to the off-grid mode, so as to make full use of the internal resources of the power microgrid and reduce the dependence of the power microgrid on the external power grid; In summary, based on the above scheme, the automatic switching of the scheduling mode of the power microgrid in the large-scale power grid can be realized, thereby improving the energy utilization rate in the power microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0048] Figure 1 is an exemplary flowchart of a method for switching the scheduling mode of a power microgrid in a large-scale power grid according to some embodiments of the present application;

[0049] Figure 2 is a logic diagram of the scheduling mode switching shown in some embodiments of the present application;

[0050] Figure 3 is a schematic flow diagram of power grid load forecasting shown in some embodiments of the present application;

[0051] Figure 4 is a schematic structural diagram of exemplary hardware and / or software of a scheduling mode switching unit according to some embodiments of the present application;

[0052] Figure 5Schematic diagram of a computer device for implementing a dispatching mode switching method of a power microgrid in a large-scale power grid according to some embodiments of the present application. Detailed implementation manners

[0053] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0054] To better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0055] Refer to Figure 1 , which is an exemplary flowchart of a dispatching mode switching method of a power microgrid in a large-scale power grid according to some embodiments of the present application. The dispatching mode switching method of the power microgrid in the large-scale power grid mainly includes the following steps:

[0056] In step 101, monitor the power microgrid in the grid-connected mode of the large-scale power grid, and collect the historical power supply information and distribution load information of the power microgrid within a specified time period.

[0057] It should be noted that in the present application, the historical power supply information refers to the data set of the power supply amount of the power microgrid within a specified time period; the distribution load information refers to the data set of the power distribution amount of the power microgrid within a specified time period; the power microgrid is a small power system that can supply power to loads in the area through local power generation equipment and can operate independently or be connected to the main grid in the large-scale power grid to flexibly respond to external power fluctuations and demands; the grid-connected mode refers to the operation mode in which the power microgrid is connected to the main grid and obtains power resources from the main grid to jointly supply power to the load. In the grid-connected mode, the power microgrid is connected to the large-scale power grid and jointly participates in power supply and load sharing to ensure power supply stability; the off-grid mode refers to the mode in which the power microgrid is disconnected from the main grid and operates independently and is powered by an internal power source and an energy storage system. In the off-grid mode, the power microgrid is disconnected from the large-scale power grid, and at this time, the power microgrid independently conducts internal power supply and load.

[0058] Specifically, when implementing, monitor the power microgrid in the grid-connected mode of the large-scale power grid, and collect the power supply amount of each power supply source and the power distribution amount of each load device of the power microgrid within a specified time period (default is the most recent one month) at fixed time intervals (default is 5 minutes). The set of all power supply amounts can be used as the historical power supply information, and the set of all power distribution amounts can be used as the distribution load information.

[0059] In some embodiments, referring to Figure 2 , this figure is a logic diagram of the switching of the scheduling mode shown in some embodiments of the present application. This logic diagram describes the switching logic of the power system between different operating modes. First, the power microgrid operates in the "connected to the distribution network and operating in parallel" mode. At this time, the power microgrid is connected to the main power grid. If a voltage anomaly is detected, the power microgrid will enter the "energy storage voltage regulation" state and attempt to stabilize the voltage through energy storage devices. If the voltage is restored, the system will return to the grid-connected operation state; if a manual power outage is caused by a fault, or the power microgrid loses power and the distribution network is fault-free, the power microgrid will attempt to start the grid-connected source and resume grid-connected operation.

[0060] If the power microgrid cannot resume grid-connected operation, or based on the black start of the energy storage main power source, the power microgrid will switch to the "island operation mode based on the energy storage main power source". In the island mode, the power microgrid does not depend on the main power grid but uses the energy storage device as the main power source. When the main power grid returns to normal, the power microgrid will perform a logical judgment. If the conditions are met, it will execute the "switch from island operation to grid-connected operation" and reconnect to the grid and resume grid-connected operation.

[0061] In step 102, the historical power supply information is divided into internal power supply loads and external power supply loads according to the access mode of each power supply source in the power microgrid, and the load isolation characteristics of the power microgrid in the grid-connected mode are determined through the external power supply loads and the internal power supply loads.

[0062] It should be noted that in the present application, the access mode of the power supply source refers to the way the power source is connected to the load, including the device identifier and access point data. Among them, the access point data includes the access interface, access mode, and access source; in specific implementation, the device identifier and access point data of each power supply source can be obtained from the topological structure diagram of the power microgrid, and the set of the device identifier and access point data can be used as the access mode of the corresponding power supply source, so as to obtain the access mode of each power supply source in the power microgrid.

[0063] In some embodiments, the division of the historical power supply information into internal power supply loads and external power supply loads according to the access mode of each power supply source in the power microgrid can be implemented by the following steps:

[0064] For each power supply source in the power microgrid, determine the power source of the power supply source according to the access mode of the power supply source;

[0065] If the power source is inside the power microgrid, the power supply amount of the power supply source in the historical power supply information is used as the internal power supply amount;

[0066] If the power source is outside the power microgrid, the power supply amount of the power supply source in the historical power supply information is used as the external power supply amount;

[0067] Furthermore, all internal power supplies and all external power supplies are obtained;

[0068] The internal power supply load is determined according to all internal power supplies, and the external power supply load is determined according to all external power supplies.

[0069] It should be noted that in this application, the external power supply load refers to the electrical load supplied by the large-scale power grid, that is, the load within the power microgrid is powered by the external power grid, and the internal power supply load refers to the electrical load supplied by the internal power source, that is, the load within the power microgrid is powered by the local power generation equipment; specifically, when implemented, first, for each power supply in the power microgrid, the power supply is regarded as a node in the network by using the network topology analysis algorithm based on graph theory, and the equipment identifier and access point data of the power supply are analyzed. The analysis results can be used as the power source of the power supply, and the power source includes within the power microgrid and within the power microgrid; secondly, if the power source is within the power microgrid, the power supply of the power supply in the historical power supply information is used as the internal power supply; then, if the power source is outside the power microgrid, the power supply of the power supply in the historical power supply information is used as the external power supply; furthermore, all internal power supplies and all external power supplies can be obtained in the above manner; finally, the set of all internal power supplies can be used as the internal power supply load, and the set of all external power supplies can be used as the external power supply load.

[0070] In some embodiments, determining the load isolation characteristics of the power microgrid in the grid-connected mode by the external power supply load and the internal power supply load can be implemented by the following steps:

[0071] Determine the time correlation between the external power supply load and the internal power supply load;

[0072] Determine the external dependence of the power microgrid in the grid-connected mode according to the external power supply load;

[0073] Determine the load isolation characteristics of the power microgrid in the grid-connected mode according to the time correlation and the external dependence.

[0074] It should be noted that in this application, the load isolation characteristic refers to the power supply independence between the internal load and the external load of the power microgrid. The stronger the load isolation characteristic, the smaller the mutual influence between the internal and external power supply loads of the power microgrid; the time correlation represents the consistency of the change trends of the external power supply load and the internal power supply load in the time dimension; the external dependence represents the degree of dependence of the power microgrid on the external power grid for power supply.

[0075] In specific implementation, first, initialize a time series model. Take each external power supply quantity and the corresponding acquisition time in the external power supply load as the external power supply parameters in this time series model, and take each internal power supply quantity and the corresponding acquisition time in the internal power supply load as the internal power supply parameters in this time series model. Use this time series model to conduct time correlation analysis on the external power supply load and the internal power supply load, and the correlation coefficient obtained from the time correlation analysis can be used as the time correlation degree between the external power supply load and the internal power supply load. Then, take the sum of the external power supply quantities in the external power supply load as the external load value, take the sum of the internal power supply quantities in the internal power supply load as the internal load value, and take the sum of the external load value and the internal load value as the total load value. The ratio of the external load value to the total load value can be used as the external dependence degree of the power microgrid in the grid-connected mode. Finally, since the higher the time correlation degree and the external dependence degree, the lower the load isolation characteristic, the reciprocal of the product of the time correlation degree and the external dependence degree can be used as the load isolation characteristic of the power microgrid in the grid-connected mode.

[0076] In step 103, based on the distribution load information and combined with the grid load prediction model, predict the short-term load and long-term load of the power microgrid in the off-grid mode. Determine the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristic, and determine the load amplitude of the power microgrid in the off-grid mode through the load margin and the short-term load.

[0077] In some embodiments, based on the distribution load information and combined with the grid load prediction model, predict the short-term load and long-term load of the power microgrid in the off-grid mode. Refer to Figure 3 As described above, this figure is a schematic flow chart of grid load prediction in some embodiments of the present application. The grid load prediction in this embodiment can be implemented by the following steps:

[0078] In step 1031, determine the dispatching load of the power microgrid in the grid-connected mode according to the distribution load information.

[0079] In step 1032, determine the load stability value of the power microgrid through the dispatching load and the load isolation characteristic.

[0080] In step 1033, when the load stability value is lower than the load upper limit of the power microgrid, construct a grid load prediction model according to the distribution load information.

[0081] In step 1034, based on the grid load prediction model, predict the short-term load and long-term load of the power microgrid in the off-grid mode.

[0082] It should be noted that in this application, the short-term load refers to the load demand of the power microgrid within a short period of time (by default, within 1 day); the long-term load refers to the load demand of the power microgrid within a relatively long period of time (by default, within 1 month); the dispatching load reflects the dispatching volume of the internal and external power supply sources of the power microgrid to meet the load demand; the load stability value reflects the ability of the power microgrid to maintain stable power supply among different loads; the load upper limit of the power microgrid refers to the maximum load capacity that the power microgrid can withstand, that is, under normal operating conditions, the maximum power output that the power microgrid can provide for the loads connected thereto. The total power capacity of the power generation equipment, energy storage system, and conversion equipment in the power microgrid can be counted as the load upper limit of the power microgrid.

[0083] In specific implementation, first, the average value of all power distribution amounts in the power distribution load information can be used as the dispatching load of the power microgrid in the grid-connected mode; second, the ratio of the dispatching load to the load isolation characteristic can be used as the load stability value of the power microgrid; then, when the load stability value is less than or equal to the load upper limit of the power microgrid, the current grid-connected mode is continued. When the load stability value is lower than the load upper limit of the power microgrid, a grid load prediction model is initialized, and all the load amounts in the power distribution load information are used as the training basis of the grid load prediction model; finally, the trained grid load prediction model is used to predict the load of the power microgrid in the off-grid mode within a specified time period. The prediction result of the power microgrid within a short time (by default, within 1 day) can be used as the short-term load, and the prediction result within a relatively long time (by default, within 1 month) can be used as the long-term load. Among them, the prediction result of the grid load prediction model is the load amount per hour.

[0084] In some embodiments, the load margin of the power microgrid in the off-grid mode can be determined according to the long-term load and the load isolation characteristic by the following steps:

[0085] Obtain the internal power supply amount of the power microgrid in the off-grid mode;

[0086] Determine the power supply capacity of the power microgrid according to the internal power supply amount and the load isolation characteristic;

[0087] Determine the load margin of the power microgrid in the off-grid mode based on the power supply capacity and the long-term load.

[0088] It should be noted that in this application, the load margin represents the redundant space where the power supply capacity of the power microgrid exceeds the long-term load demand, reflecting the power margin of the power microgrid under load changes or abnormal conditions. The higher the load margin, the stronger the power supply guarantee of the power microgrid; the power supply capacity represents the overall power supply guarantee ability of the power microgrid in the isolated operation state.

[0089] In specific implementation, first, the power supply demand of the power microgrid within a unit time can be obtained in the dispatching console of the large-scale power grid as the internal power supply of the power microgrid in the off-grid mode; then, the product of the internal power supply and the load isolation characteristic can be used as the power supply capacity of the power microgrid; finally, the opposite of the difference between the average value of all load amounts in the long-term load and the power supply capacity can be used as the load margin of the power microgrid in the off-grid mode.

[0090] It should be noted that the load margin is a key indicator for evaluating whether the power microgrid can continuously and stably supply power during isolated operation. Through accurate calculation of the power supply capacity and load, the power microgrid can achieve safer and more efficient power dispatching, ensuring that the power microgrid can still maintain a stable power supply capacity in the future load growth or unexpected situations.

[0091] In some embodiments, determining the load amplitude of the power microgrid in the off-grid mode based on the load margin and the short-term load can be achieved by the following steps:

[0092] Determine the load stability of the power microgrid in the off-grid mode;

[0093] Determine the limit fluctuation value of the load of the power microgrid in the off-grid mode according to the load stability and the load margin;

[0094] Determine the load amplitude of the power microgrid in the off-grid mode through the limit fluctuation value and the short-term load.

[0095] It should be noted that in this application, the load amplitude represents the load upper limit of the power microgrid in the short term in the off-grid mode; the load stability refers to the stable ability of the power microgrid to face load fluctuations in the off-grid mode; the limit fluctuation value reflects the maximum fluctuation range of the load under different conditions, and this limit fluctuation value is usually a proportional factor.

[0096] In specific implementation, first, the standard deviation of all load amounts in the long-term load can be used as the load stability of the power microgrid in the off-grid mode; then, initialize a Monte Carlo simulation model, use the load stability as the distribution parameter of this Monte Carlo simulation model, use the load margin as the limiting parameter of the load (processing variable) in this Monte Carlo simulation model, and use this Monte Carlo simulation model to simulate the load of the power microgrid in the off-grid mode. The maximum value of the load fluctuations obtained from multiple simulations can be used as the limit fluctuation value of the load of the power microgrid in the off-grid mode; finally, the sum of the short-term load and the limit fluctuation value can be used as the load amplitude of the power microgrid in the off-grid mode.

[0097] It should be noted that the Monte Carlo simulation model is a stochastic simulation model based on probability theory, which is used to evaluate the performance of a system under uncertain conditions. This method can simulate the load fluctuation range of a power microgrid under different load scenarios. In a power microgrid, the Monte Carlo simulation model can be used to simulate different load conditions and fluctuations to calculate the load stability and load margin of the power microgrid. The maximum value of the load fluctuation obtained from multiple simulations is the limit fluctuation value.

[0098] In step 104, when the load amplitude is lower than the load upper limit of the power microgrid, the scheduling mode of the power microgrid in the large-scale power grid is automatically switched from the grid-connected mode to the off-grid mode.

[0099] It should be noted that in this application, the load upper limit of the power microgrid refers to the maximum load capacity that the power microgrid can withstand, that is, under normal operating conditions, the maximum power output that the power microgrid can provide for the loads connected to it. The total power capacity of the power generation equipment, energy storage system, and conversion equipment in the power microgrid can be counted as the load upper limit of the power microgrid.

[0100] In some embodiments, the automatic switching of the scheduling mode of the power microgrid in the large-scale power grid from the grid-connected mode to the off-grid mode can be achieved by the following steps:

[0101] Disconnect the power microgrid from the grid-connected mode of the large-scale power grid;

[0102] Start the independent operation mode of the power microgrid to complete the automatic switching from the grid-connected mode to the off-grid mode.

[0103] Specifically, first, use a circuit breaker or power electronic equipment to cut off the physical connection (grid-connected mode) between the power microgrid and the large-scale power grid within milliseconds to ensure stable system switching; then, the power generation equipment and energy storage system inside the power microgrid start to supply power to the load independently, switch to the independent operation mode, and monitor indicators such as the internal voltage and frequency of the power microgrid in real time to complete the automatic switching from the grid-connected mode to the off-grid mode.

[0104] In this application, when the load amplitude is lower than the load upper limit of the power microgrid, the scheduling mode of the power microgrid in the large-scale power grid is automatically switched from the grid-connected mode to the off-grid mode. First, the determination of the load isolation characteristic is based on the analysis of the internal power supply load and the external power supply load. Through the evaluation of time correlation and external dependence, the independence of the power microgrid in the grid-connected mode can be quantified. The load isolation characteristic determines whether the power microgrid can maintain stable operation in the face of external grid fluctuations and under what conditions it can operate independently (off-grid mode). The stronger the load isolation characteristic, the less dependent the power microgrid is on external power supply, which helps to reduce the impact of external fluctuations on the internal system and improve the independent operation ability and energy utilization efficiency of the power microgrid. Then, the determination of the load amplitude depends on the calculation of the load margin and the recent load. The load amplitude reflects the load fluctuation range that the power microgrid can withstand in the off-grid mode. Through reasonable amplitude management, the power microgrid can maintain stable operation during load fluctuations, not only avoiding excessive energy consumption but also ensuring the optimal allocation of energy. When the load amplitude is lower than the load upper limit of the power microgrid, the power microgrid can be automatically switched to the off-grid mode, so as to make full use of the internal resources of the power microgrid and reduce the dependence of the power microgrid on the external power grid. In summary, based on the above solution, the automatic switching of the scheduling mode of the power microgrid in the large-scale power grid can be realized, thereby improving the energy utilization rate in the power microgrid.

[0105] In addition, on the other hand of this application, in some embodiments, this application provides an intelligent power supply and distribution management system for a large-scale power grid. The intelligent power supply and distribution management system for the large-scale power grid further includes a scheduling mode switching unit. Refer to Figure 4 , which is a schematic diagram of the exemplary hardware and / or software structure of the scheduling mode switching unit shown in some embodiments of this application. The scheduling mode switching unit includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described as follows:

[0106] The acquisition module 201 is mainly used in this application to acquire the historical power supply information and distribution load information of the power microgrid within a specified time period.

[0107] The processing module 202 is used in this application to divide the historical power supply information into internal power supply loads and external power supply loads according to the access mode of each power source in the power microgrid, and determine the load isolation characteristic of the power microgrid in the grid-connected mode through the external power supply load and the internal power supply load.

[0108] It should be noted that the processing module 202 is further configured to predict the short-term load and long-term load of the power microgrid in the off-grid mode based on the distribution load information in combination with the power grid load prediction model, determine the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristics, and determine the load amplitude of the power microgrid in the off-grid mode through the load margin and the short-term load.

[0109] The execution module 203. In this application, the execution module 203 is mainly configured to automatically switch the scheduling mode of the power microgrid in the large-scale power grid from the grid-connected mode to the off-grid mode when the load amplitude is lower than the load upper limit of the power microgrid.

[0110] The above has introduced in detail the examples of the intelligent power supply and distribution management system and method for a large-scale power grid provided by the embodiments of the present application. It can be understood that, in order to implement the above functions, the corresponding device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0111] In some embodiments, the present application further provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned method for switching the scheduling mode of the power microgrid in the large-scale power grid.

[0112] In some embodiments, refer to Figure 5 , the dotted line in this figure indicates that the unit or the module is optional. This figure is a schematic structural diagram of a computer device for implementing the method for switching the scheduling mode of the power microgrid in the large-scale power grid provided by the embodiments of the present application. The method for switching the scheduling mode of the power microgrid in the large-scale power grid described in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device includes at least one processor 301, a memory 302, and at least one communication unit 305. The computer device can be a terminal device, a server, or a chip.

[0113] The processor 301 can be a general-purpose processor or a special-purpose processor. For example, the processor 301 can be a central processing unit (CPU), and the CPU can be used to control a computer device, execute software programs, and process data of the software programs. The computer device can also include a communication unit 305 for implementing signal input (reception) and output (transmission).

[0114] For example, the computer device can be a chip, and the communication unit 305 can be the input and / or output circuit of the chip, or the communication unit 305 can be the communication interface of the chip. The chip can be a component of a terminal device, a network device, or other devices.

[0115] For another example, the computer device can be a terminal device or a server, and the communication unit 305 can be the transceiver of the terminal device or the server, or the communication unit 305 can be the transceiver circuit of the terminal device or the server.

[0116] The computer device can include one or more memories 302, on which a program 304 is stored. The program 304 can be run by the processor 301 to generate instructions 303, enabling the processor 301 to execute the methods described in the above method embodiments according to the instructions 303. Optionally, data such as a target audit model can also be stored in the memory 302. Optionally, the processor 301 can also read the data stored in the memory 302. The data can be stored at the same storage address as the program 304, or it can be stored at a different storage address from the program 304.

[0117] The processor 301 and the memory 302 can be set separately or integrated together. For example, they can be integrated on a system on chip (SOC) of a terminal device.

[0118] It should be understood that the steps of the above method embodiments can be completed by the logic circuit in the form of hardware or instructions in the form of software in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices. For example, discrete gate, transistor logic devices, or discrete hardware components.

[0119] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] For example, in some embodiments, the present application further provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer is caused to execute the above-mentioned method for switching the scheduling mode of the power microgrid in the large-scale power grid.

[0121] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0122] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A scheduling mode switching method for a power microgrid in a large-scale power grid, which is used for a smart power supply and distribution management system to perform scheduling mode switching, and is characterized in that, It includes the following steps: Monitor the power microgrid of the large-scale power grid in the grid-connected mode, and collect the historical power supply information and distribution load information of the power microgrid within a specified time period; Divide the historical power supply information into internal power supply loads and external power supply loads according to the access mode of each power supply source in the power microgrid, and determine the load isolation characteristics of the power microgrid in the grid-connected mode through the external power supply loads and the internal power supply loads, where the load isolation characteristics represent the power supply independence between the internal loads and external loads of the power microgrid; Based on the distribution load information and combined with the grid load prediction model, predict the short-term load and long-term load of the power microgrid in the off-grid mode, determine the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristics, and determine the load amplitude of the power microgrid in the off-grid mode through the load margin and the short-term load, where the load amplitude represents the load upper limit of the power microgrid in the off-grid mode; When the load amplitude is lower than the load upper limit of the power microgrid, automatically switch the scheduling mode of the power microgrid in the large-scale power grid from the grid-connected mode to the off-grid mode; Among them, determining the load isolation characteristics of the power microgrid in the grid-connected mode through the external power supply loads and the internal power supply loads specifically includes: Determine the time correlation between the external power supply loads and the internal power supply loads; Determine the external dependence of the power microgrid in the grid-connected mode based on the external power supply loads; Determine the load isolation characteristics of the power microgrid in the grid-connected mode according to the time correlation and the external dependence.

2. The method according to claim 1, wherein Dividing the historical power supply information into internal power supply loads and external power supply loads according to the access mode of each power supply source in the power microgrid specifically includes: For each power supply source in the power microgrid, determine the power source of the power supply source according to the access mode of the power supply source; If the power source is inside the power microgrid, take the power supply amount of the power supply source in the historical power supply information as the internal power supply amount; If the power source is outside the power microgrid, take the power supply amount of the power supply source in the historical power supply information as the external power supply amount; Furthermore, obtain all internal power supply amounts and all external power supply amounts; Determine the internal power supply load according to all internal power supply amounts, and determine the external power supply load according to all external power supply amounts.

3. The method according to claim 1, characterized in that, Based on the distribution load information and combined with the grid load prediction model, predicting the short-term load and long-term load of the power microgrid in the off-grid mode specifically includes: Determine the scheduling load of the power microgrid in the grid-connected mode according to the distribution load information; Determine the load stability value of the power microgrid through the scheduling load and the load isolation characteristics; When the load stability value is lower than the load upper limit of the power microgrid, construct a grid load prediction model according to the distribution load information; Based on the grid load prediction model, predict the short-term load and long-term load of the power microgrid in the off-grid mode.

4. The method according to claim 1, wherein Determining the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristics specifically includes: Obtain the internal power supply amount of the power microgrid in the off-grid mode; Determine the power supply capacity of the power microgrid according to the internal power supply amount and the load isolation characteristics; Determine the load margin of the power microgrid in the off-grid mode based on the power supply capacity and the long-term load.

5. The method according to claim 1, wherein Determining the load amplitude of the power microgrid in the off-grid mode through the load margin and the short-term load specifically includes: Obtain the load stability of the power microgrid in the off-grid mode; Determine the limit fluctuation value of the load of the power microgrid in the off-grid mode according to the load stability and the load margin; Determine the load amplitude of the power microgrid in the off-grid mode through the limit fluctuation value and the short-term load.

6. The method according to claim 1, wherein The large-scale power grid is a 750 kV AC power transmission network based on wind power generation.

7. An intelligent power supply and distribution management system for a large-scale power grid, which uses the method described in any one of claims 1 to 6 to perform the switching of the dispatching mode of the power microgrid. The intelligent power supply and distribution management system for the large-scale power grid includes a dispatching mode switching unit, and is characterized in that, The scheduling mode switching unit includes: A collection module for collecting the historical power supply information and distribution load information of the power microgrid within a specified time period; A processing module for dividing the historical power supply information into internal power supply loads and external power supply loads according to the access modes of each power source in the power microgrid, and determining the load isolation characteristics of the power microgrid in the grid-connected mode through the external power supply loads and the internal power supply loads; The processing module is further configured to predict the short-term load and long-term load of the power microgrid in the off-grid mode based on the distribution load information in combination with the power grid load prediction model, determine the load margin of the power microgrid in the off-grid mode according to the long-term load and the load isolation characteristics, and determine the load amplitude of the power microgrid in the off-grid mode through the load margin and the short-term load; An execution module for automatically switching the scheduling mode of the power microgrid in the large-scale power grid from the grid-connected mode to the off-grid mode when the load amplitude is lower than the load upper limit of the power microgrid.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the scheduling mode switching method of the power microgrid in the large-scale power grid according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Instructions or codes are stored in the computer-readable storage medium. When the instructions or codes run on a computer, the computer is caused to execute the scheduling mode switching method of the power microgrid in the large-scale power grid according to any one of claims 1 to 6.

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