A method and device for energy flow balance control based on full-category power supply access model

Abstract

The present invention discloses a method and device for energy flow balance control based on a full-category power supply access model. It includes: determining the first predicted power data of the first category power supply based on the power change amount and initial power data of each power supply node in the first category power supply corresponding to the target grid-connected node; using the target power data of the target load as the target output power data of the target grid-connected node, and performing energy flow balance processing on the target output power data, the first predicted power data, and the voltage data and current data of the power supply nodes of the first category power supply based on the pre-built full-category power supply access model to obtain the power supply parameter data of the second category power supply; used to adjust the number of power supply nodes in the second category power supply to control the number of grid-connected power supply nodes of the second category power supply. This solution ensures that the output power data of the target grid-connected node meets the power demand of the target load while making the first category power supply and the second category power supply in a balanced state.

Description

Technical Field

[0001] The present invention relates to the field of power grid control technology, and in particular to an energy flow balance control method and device based on a full-category power supply access model. Background Art

[0002] The output of renewable energy power generation has a strong uncertainty. The access of large-scale renewable energy power generation to the power system will bring about operational risks such as over-limit of power flow and over-limit of voltage.

[0003] With the increasing penetration of new energy power sources in different categories of power sources, it is necessary to study the energy flow balance based on the access of all categories of power sources in order to effectively deal with the uncertainty of the output of new energy power sources. In the prior art, the balance relationship between the electric power output of the generator set and the power load demand is usually studied. The energy flow balance at the power access end is not controlled, and the scheduling and control of different categories of power sources by the power access end cannot be determined, which makes the power access end have the risk of instability during the grid connection process and cannot accurately schedule various categories of power sources. Summary of the invention

[0004] The present invention provides an energy flow balance control method and device based on a full-category power supply access model to solve the problem of poor scheduling and control of category power supplies at the power supply access end caused by the uncertainty of the output of new energy power supplies, and is used to improve the stability of grid access of various categories of power supplies and the accuracy of scheduling and control.

[0005] According to one aspect of the present invention, there is provided an energy flow balance control method based on a full-category power supply access model, comprising:

[0006] Obtaining initial power data of each power supply node in the first category of power supplies corresponding to the target grid-connected node, and determining a power change of each power supply node in the first category of power supplies; determining first predicted power data of the first category of power supplies based on the power change of each power supply node in the first category of power supplies and the initial power data;

[0007] Obtain target power data of the target load and voltage data and current data of the power supply node of the first category power supply, use the target power data of the target load as the target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power supply node of the first category power supply based on a pre-built full-category power supply access model, and obtain power supply parameter data of the second category power supply;

[0008] The number of power supply nodes in the second category power supply is adjusted based on the power supply parameter data, and the number of network-connected power supply nodes of the second category power supply is controlled based on the number of power supply nodes.

[0009] Optionally, determining the power change of each power node in the first category of power supply includes: obtaining real-time output power data of the target grid-connected node and real-time input power data of the target load; when the real-time output power data is not equal to the real-time input power data, determining the power change of each power node in the first category of power supply within a first preset time period; or, when the real-time output power data is equal to the real-time input power data, determining the power change of each power node in the first category of power supply within a second preset time period, wherein the first preset time period is less than the second preset time period.

[0010] Optionally, determining the power change of each power node in the first category of power supply within a first preset time period or a second preset time period includes: obtaining environmental variable information of each power node in the first category of power supply within the first preset time period or the second preset time period, and determining an initial power change of each power node within the first preset time period or the second preset time period based on the environmental variable information; obtaining energy conversion rate data corresponding to each power node in the first category of power supply, and determining the power change of each power node in the first category of power supply within the first preset time period or the second preset time period based on the energy conversion rate data of each power node and the initial power change of the corresponding power node within the first preset time period or the second preset time period.

[0011] Optionally, first predicted power data of the first category of power supply is determined based on the power change and initial power data of each power supply node in the first category of power supply, including: adjusting the initial power data of the corresponding power supply nodes based on the power change of each power supply node to obtain the power adjustment result of each power supply node; and determining the first predicted power data of the first category of power supply based on the sum of the power adjustment results of each power supply node.

[0012] Optionally, constructing a full-category power supply access model includes: obtaining historical voltage data and historical current data of at least one power supply node in the first category of power supplies of the target grid-connected node within a preset time period, and historical voltage data and historical current data of at least one power supply node in the second category of power supplies of the target grid-connected node within a preset time period; fitting the historical voltage data of each power supply node in the first category of power supplies with the historical voltage data of each power supply node in the second category of power supplies to obtain a voltage balance sub-model of the target grid-connected node, and fitting the historical current data of each power supply node in the first category of power supplies with the historical voltage data of each power supply node in the second category of power supplies to obtain a voltage balance sub-model of the target grid-connected node. The historical current data is fitted to obtain the current balance submodel of the target grid-connected node; the pre-configured power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply are obtained, and the power parameters of the first category power supply and the second category power supply are weighted summed based on the power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply, and the weighted summation result is equal to the output power data of the target grid-connected node, and the obtained equation is set as the power balance submodel of the target grid-connected node; a full-category power supply access model is formed based on the voltage balance submodel, the current balance submodel and the power balance submodel.

[0013] Optionally, based on a pre-built full-category power supply access model, energy flow balancing is performed on the target output power data, the first predicted power data, and the voltage data and current data of the power supply nodes of the first category power supply to obtain power supply parameter data of the second category power supply, including: inputting the first predicted power data and the target output power data into a power balance sub-model to obtain second predicted power data of the second category power supply corresponding to the target grid-connected node; inputting the voltage data and current data of the power supply nodes of the first category power supply into the voltage balance sub-model and the current balance sub-model in the full-category power supply access model respectively to obtain the target voltage data and target current data of the power supply nodes of the second category power supply.

[0014] Optionally, the power supply parameter data includes second predicted power data, target voltage data and target current data of power supply nodes in the second category of power supply; and adjusting the number of power supply nodes in the second category of power supply based on the power supply parameter data includes:

[0015] Determine the power data of a single power node based on the target current data and target voltage data of the power node in the power parameter data; determine the ratio of the second predicted power data in the power parameter data to the power data of the single power node, and determine the number of power nodes of the second category of power supply based on the ratio.

[0016] According to another aspect of the present invention, there is provided an energy flow balance control device based on a full-category power supply access model, comprising:

[0017] A first predicted power data determination module is used to obtain initial power data of each power supply node in the first category of power supplies corresponding to the target grid-connected node, and determine the power change of each power supply node in the first category of power supplies; based on the power change of each power supply node in the first category of power supplies and the initial power data, determine the first predicted power data of the first category of power supplies;

[0018] A power parameter data determination module is used to obtain target power data of a target load and voltage data and current data of a power node of a first category of power supply, use the target power data of the target load as target output power data of a target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power node of the first category of power supply based on a pre-built full-category power supply access model, and obtain power parameter data of a second category of power supply;

[0019] The second category power supply adjustment module is used to adjust the number of power supply nodes in the second category power supply based on the power supply parameter data, and control the number of network-connected power supply nodes of the second category power supply based on the number of power supply nodes.

[0020] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0021] at least one processor; and

[0022] a memory communicatively connected to at least one processor; wherein,

[0023] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the energy flow balance control method based on the full-category power supply access model of any embodiment of the present invention.

[0024] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement the energy flow balance control method based on the full-category power supply access model of any embodiment of the present invention when executed.

[0025] The technical solution of the embodiment of the present invention is to obtain the initial power data of each power node in the first category of power supply corresponding to the target grid-connected node, and determine the power change of each power node in the first category of power supply; determine the first predicted power data of the first category of power supply based on the power change and the initial power data of each power node in the first category of power supply; obtain the target power data of the target load and the voltage data and current data of the power node of the first category of power supply, use the target power data of the target load as the target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data and the voltage data and current data of the power node of the first category of power supply based on a pre-built full-category power supply access model, and obtain the power parameter data of the second category of power supply; adjust the number of power nodes in the second category of power supply based on the power parameter data, and control the number of grid-connected power nodes of the second category of power supply based on the number of power nodes. This technical solution uses the target power data of the target load as the target output power data of the target grid-connected node, and processes the predicted power data, voltage data and current data of the first category of power supply through a pre-built full-category power supply access model to determine the power parameter data of the second category of power supply, so as to obtain the number of power supply nodes of the second category of power supply, and complete the scheduling and control of the power supply nodes of the second category of power supply, thereby solving the problem of poor scheduling and control of category power supplies at the power access end caused by the uncertainty of the output of new energy power supplies, so that while ensuring that the output power data of the target grid-connected node meets the power demand of the target load, it ensures that the first category of power supply and the second category of power supply are also in a balanced state, avoiding the problem of grid connection abnormalities caused by voltage and current imbalance at the power access end, reducing the risk of energy flow imbalance, and improving the stability, safety and reliability of the access of various categories of power supplies to the grid.

[0026] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a flow chart of an energy flow balance control method based on a full-category power supply access model provided in the first embodiment of the present invention;

[0029] Figure 2It is a flow chart of an energy flow balance control method based on a full-category power supply access model provided in the second embodiment of the present invention;

[0030] Figure 3 It is a structural schematic diagram of an energy flow balance control device based on a full-category power supply access model provided in the third embodiment of the present invention;

[0031] Figure 4 It is a structural schematic diagram of an electronic device that implements the energy flow balance control method based on the full-category power supply access model of an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 This is a flow chart of a method for energy flow balance control based on a full-category power supply access model provided by the first embodiment of the present invention. This embodiment is applicable to the case of energy flow balance processing. The method can be executed by an energy flow balance control device based on a full-category power supply access model. The energy flow balance control device based on a full-category power supply access model can be implemented in the form of hardware and / or software. The energy flow balance control device based on a full-category power supply access model can be configured in electronic devices such as controllers and computers. Figure 1 As shown, the method includes:

[0036] S110. Obtain initial power data of each power supply node in the first category of power supply corresponding to the target grid-connected node, and determine the power change of each power supply node in the first category of power supply; determine the first predicted power data of the first category of power supply based on the power change of each power supply node in the first category of power supply and the initial power data.

[0037] It is understandable that the complementary use of different types of energy can be achieved through grid connection technology, such as the complementary use of different types of power sources obtained by wind power generation and solar power generation, thereby improving the efficiency of the overall power supply system, and setting grid connection nodes to complete the integration of different types of power sources. The grid connection point is the connection point where the power source is connected to the power grid, or the connection point between the power user's electrical equipment and the power grid. In this embodiment, the grid connection node specifically refers to the connection point where the power source is connected to the power grid. The target grid connection node can be specifically understood as any node in the power grid system that needs to be connected to the grid for different types of connected power sources.

[0038] The degree to which different energy generation processes are affected by environmental factors is different. Generally, the degree to which new energy generation processes are affected by environmental factors is high. Among them, new energy generation uses energy sources including but not limited to solar energy, wind energy, geothermal energy and ocean energy for power generation. The principle of power classification can pre-classify the power obtained by each energy generation according to the degree to which different energy generation processes are affected by environmental factors. Specifically, the power can be divided into two categories. One is the category of power that is highly affected by environmental factors, which can be represented by the first category of power, and the other is the category of power that is less affected by environmental factors, which can be represented by the second category of power. For example, the power generated by wind power generation, photovoltaic power generation, tidal power generation and hydropower generation can be called the first category of power, and the power obtained by thermal power generation can be called the second category of power. The types of power obtained by various energy generation can be classified by enumeration, and the classification results can be stored. When the power is connected, the classification results can be directly called to determine whether the connected power belongs to the first category of power or the second category of power. In this way, the classification and energy flow balance of all categories of power can be realized. The full category of power includes the power generated by all energy generation. It should be noted that it is possible to set whether the first category power supply represents a power supply that is highly affected by environmental factors or a power supply that is less affected by environmental factors. In this embodiment, the first category power supply specifically refers to a power supply that is highly affected by environmental factors.

[0039] The power supply node can be understood as a node that generates and outputs power, specifically including but not limited to a wind power station and a hydroelectric power station. The initial power data of each power supply node can be understood as the output power data of the power supply node that is not affected by environmental factors, and can be detected by setting corresponding detection equipment. The power variation can be understood as the power variation caused by the power supply of the power supply node being affected by environmental factors. For example, for photovoltaic power generation, the power variation is related to the light intensity, and the environmental variable information can be the light intensity. Preferably, different environmental variable information and corresponding power variations can be calibrated in advance to form a mapping relationship between the two, and a mapping relationship table corresponding to the calibrated environmental variable information and the corresponding power variation is stored. When the environmental variable information is determined, the mapping relationship table can be called to match the power variation corresponding to the acquired environmental variable information in the mapping relationship table, and each power supply node corresponds to a power variation. It should be noted that the environmental variable information corresponding to the power nodes of different power generation methods is different. For example, for the power nodes generated by wind power generation, the environmental change is related to the wind speed, and the environmental variable information can be the wind speed. For photovoltaic power generation, the power change is related to the light intensity, and the environmental variable information can be the light intensity.

[0040] Specifically, the power source connected to the power access terminal of the target grid-connected node includes a first category power source, and the first category power source includes at least one power node. The information of the power node is collected by the information collection device corresponding to each power node. The information of the power node includes but is not limited to current information, voltage information, initial power information and environmental variable information. The information of the power node collected by the information collection device corresponding to each power node can be received in real time. The voltage information and current information in the information of each power node can be used to determine the power data of each power node. It should be noted that the historical information corresponding to each power node can be retrieved from the preset data storage space. The mapping relationship table between the environmental variable information and the corresponding power change is called, and the power change of the corresponding power node is matched from the mapping relationship table according to the environmental variable information of each power node in the first category power source obtained. For each power node, the corresponding initial power data is corrected by the power change of the power node to obtain the corrected power data corresponding to each power node, and the first predicted power data of the first category power source is determined according to the corrected power data corresponding to each power node.

[0041] In this embodiment, the predicted power data of the first category of power supply is first determined for subsequent scheduling and control of the second category of power supply, so as to achieve energy flow balance processing for all categories of power supply, thereby ensuring that all categories of power supply at the power access end are in a balanced state, which helps to improve the stability, safety and reliability of all categories of power supply access to the network.

[0042] Optionally, determining the power change of each power node in the first category of power supply includes: obtaining real-time output power data of the target grid-connected node and real-time input power data of the target load; when the real-time output power data is not equal to the real-time input power data, determining the power change of each power node in the first category of power supply within a first preset time period; or, when the real-time output power data is equal to the real-time input power data, determining the power change of each power node in the first category of power supply within a second preset time period, wherein the first preset time period is less than the second preset time period.

[0043] Among them, in order to determine the predicted power data of the first category of power supply, it is necessary to set the time period information, which is used to control the time period information required for the power change. The setting of the time period information is determined according to the equal relationship between the real-time output power data of the target grid-connected node and the real-time input power data of the target load. In the case where the real-time output power data is not equal to the real-time input power data, the time period information can be set to the first preset time period. In the case where the real-time output power data is equal to the real-time input power data, the time period information can be set to the second preset time period, wherein the first preset time period is less than the second preset time period. The first preset time period information can be set to one day, one hour or two hours, and can be set according to actual conditions. Correspondingly, the second preset time period can also be set according to actual conditions, and it is necessary to meet certain constraints, that is, the first preset time period is less than the second preset time period.

[0044] Specifically, the real-time output power data of the target grid-connected node is obtained through the collection device of the target grid-connected node, and the real-time input power data of the target load is obtained through the information collection device at the target load end, and it is determined whether the real-time output power data is equal to the real-time input power data. When the real-time output power data is not equal to the real-time input power data, the time period information is set to the first preset time period, and it is necessary to determine the power change of each power supply node in the first category of power supply within the first preset time period; when the real-time output power data is equal to the real-time input power data, the time period information is set to the second preset time period, and it is necessary to determine the power change of each power supply node in the first category of power supply within the second preset time period.

[0045] Optionally, determining the power change of each power node in the first category of power supply within a first preset time period or a second preset time period includes: obtaining environmental variable information of each power node in the first category of power supply within the first preset time period or the second preset time period, and determining an initial power change of each power node within the first preset time period or the second preset time period based on the environmental variable information; obtaining energy conversion rate data corresponding to each power node in the first category of power supply, and determining the power change of each power node in the first category of power supply within the first preset time period or the second preset time period based on the energy conversion rate data of each power node and the initial power change of the corresponding power node within the first preset time period or the second preset time period.

[0046] Among them, environmental variable information can be detected in real time through meteorological monitoring equipment, or it can be obtained from meteorological forecast results.

[0047] Specifically, the environmental variable information of each power node in the first preset time period or the second preset time period can be obtained from the meteorological forecast result, and the corresponding power change rate can be determined according to the environmental variable information. By integrating the power change rate in the first preset time period or the second preset time period, the initial power change amount of each power node in the first preset time period or the second preset time period can be obtained. The calculation formula is as follows:

[0048]

[0049] in, The first category of power supply is numbered The initial power change of the power node is the change in output power caused by the change in environmental variables. The first category of power supply is numbered The power variable value corresponding to the environmental variable information of the power supply node, T is the preset time period information. When the real-time output power data is not equal to the real-time input power data, T is the first preset time period. When the real-time output power data is equal to the real-time input power data, T is the second preset time period. The preset time period can be adjusted according to actual conditions.

[0050] Among them, the energy conversion rate data is related to the properties of the power node itself, that is, it is related to the device that generates the power itself. The energy conversion rate data corresponding to each power node can be set in advance. Each power node is set with corresponding energy conversion rate data. It should be noted that the initial power change is the change in output power caused by changes in environmental variables, but not all changes are used for output. The actual power data corresponding to the change needs to be determined through the energy conversion rate data to determine the accurate power change of the power node. Specifically, the power change of each power node is determined by the following formula, such as:

[0051]

[0052] in, It is the first in the first category of power supplies. Energy conversion rate data of each power node.

[0053] S120. Obtain the target power data of the target load and the voltage data and current data of the power supply nodes of the first category of power supply, use the target power data of the target load as the target output power data of the target grid-connected node, and perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power supply nodes of the first category of power supply based on a pre-built full-category power supply access model to obtain the power supply parameter data of the second category of power supply.

[0054] Among them, the target load can be specifically understood as a load that forms a supply relationship with the target grid-connected node, that is, the target load is a load for receiving the electric energy output by the target grid-connected node. The target power data can be obtained by querying the preset power supply plan of the power grid system. The voltage data and current data of the power supply node of the first category power supply can be collected by the corresponding information collection equipment.

[0055] Specifically, during the power supply process, it is necessary to maintain the balance of the power system, which refers to the balance between the electric power output by the generator set and the power load demand, that is, the output power data of the target grid-connected node is equal to the power data required by the target load. In order to ensure that the output power data of the target grid-connected node and the target power data of the target load are in a balanced state, the target power data of the target load is used as the target output power data of the target grid-connected node. Under the premise of ensuring that the target load and the target grid-connected node are in a power balance state, energy flow balancing processing is performed based on the relevant information of the first category power supply to determine the power supply parameter data of the second category power supply. Specifically, the target output power data, the first predicted power data, and the voltage data and current data of the power supply node of the first category power supply are subjected to energy flow balancing processing through the pre-built full-category power supply access model to obtain the power supply parameter data of the second category power supply.

[0056] Optionally, constructing a full-category power supply access model includes: obtaining historical voltage data and historical current data of at least one power supply node in the first category of power supplies of the target grid-connected node within a preset time period, and historical voltage data and historical current data of at least one power supply node in the second category of power supplies of the target grid-connected node within a preset time period; fitting the historical voltage data of each power supply node in the first category of power supplies with the historical voltage data of each power supply node in the second category of power supplies to obtain a voltage balance sub-model of the target grid-connected node, and fitting the historical current data of each power supply node in the first category of power supplies with the historical voltage data of each power supply node in the second category of power supplies to obtain a voltage balance sub-model of the target grid-connected node. The historical current data is fitted to obtain the current balance submodel of the target grid-connected node; the pre-configured power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply are obtained, and the power parameters of the first category power supply and the second category power supply are weighted summed based on the power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply, and the weighted summation result is equal to the output power data of the target grid-connected node, and the obtained equation is set as the power balance submodel of the target grid-connected node; a full-category power supply access model is formed based on the voltage balance submodel, the current balance submodel and the power balance submodel.

[0057] Specifically, for the same target grid-connected node, the voltage data of the corresponding first category power supply and the voltage data of the second category power supply need to satisfy a certain balance relationship, and the current data of the first category power supply and the current data of the second category power supply need to satisfy a certain balance relationship. The voltage balance relationship and the current balance relationship are as follows:

[0058]

[0059] in, is the output voltage of the power node of the first category power supply, is the adjustment parameter of the output voltage of the power node of the second category power supply, is the output voltage of the power node of the second category power supply, is the output current of the power node of the first category power supply, The adjustment parameter for the output current of the power node of the second category power supply, The output current of the power supply node of the second category power supply is obtained by obtaining the historical power supply data and historical current data of the power supply node of the first category power supply within a preset time period, and obtaining the historical power supply data and historical current data of the power supply node of the second category power supply within a preset time period, and performing fitting processing based on the historical voltage data of each power supply node in the first category power supply and the historical voltage data of each power supply node in the second category power supply to obtain Based on the historical current data of each power node in the first category of power supply and the historical current data of each power node in the second category of power supply, the value of The voltage balance sub-model and the current balance sub-model are obtained. It should be noted that the preset time interval is used to trigger the and Perform an update process, that is, re-acquire the voltage data and current data within the most recent preset time period for re-fitting to update and ; It can also be determined whether the adjustment accuracy of the power node meets the preset accuracy threshold. Once it is detected that it does not meet the threshold, the and Perform update processing.

[0060] The power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply are pre-configured according to the empirical data, and the power parameters of the first category power supply and the second category power supply are weightedly summed by the power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply. The obtained weighted summation result is equal to the output power data of the target grid-connected node, and the obtained equation is set as the power balance sub-model of the target grid-connected node; the equation is as follows:

[0061]

[0062] in, is the output power of the target grid-connected node, is the output power of the first category of power supply connected, is the output power of the second category power supply, The output power weight adjustment parameters for the first category power supply, The output power weight adjustment parameters for the second category power supply; it should be noted that, and It can be set according to actual conditions, or it can be dynamically adjusted based on the ratio of the output power of the first category power supply to the target power data of the target load. If it is not 0, it must satisfy , make full use of the first category power supply as much as possible; When it is 0, , you need to adjust The weight adjustment parameter It should be noted that the power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply can be adjusted in real time according to the grid connection status of the actual power supply node.

[0063] After the voltage balance sub-model, the current balance sub-model and the power balance sub-model are determined, a full-category power supply access model is constructed by the voltage balance sub-model, the current balance sub-model and the power balance sub-model.

[0064] Optionally, based on a pre-built full-category power supply access model, energy flow balancing is performed on the target output power data, the first predicted power data, and the voltage data and current data of the power supply nodes of the first category power supply to obtain power supply parameter data of the second category power supply, including: inputting the first predicted power data and the target output power data into a power balance sub-model to obtain second predicted power data of the second category power supply corresponding to the target grid-connected node; inputting the voltage data and current data of the power supply nodes of the first category power supply into the voltage balance sub-model and the current balance sub-model in the full-category power supply access model respectively to obtain the target voltage data and target current data of the power supply nodes of the second category power supply.

[0065] Specifically, the first predicted power data of the first category power supply, the voltage data and current data corresponding to the power supply nodes in the first category power supply are used as input data of the pre-built full-category power supply access model to obtain the power supply parameter data of the second category power supply, so as to realize the regulation of the second category power supply based on the predicted power data, voltage data and current data of the first category power supply. By inputting the first predicted power data and the target output power data into the power balance sub-model, the power balance sub-model calculates the second predicted power data of the second category power supply corresponding to the target grid-connected node; the voltage data of the power supply nodes of the first category power supply are used as input data of the voltage balance sub-model, and the voltage balance sub-model calculates the target voltage data of the power supply nodes of the second category power supply; the current data of the power supply nodes of the first category power supply are used as input data of the current balance sub-model, and the current balance sub-model calculates the target current data of the power supply nodes of the second category power supply, and the obtained second predicted power data, target voltage data and target current data are the power supply parameter data of the second category power supply.

[0066] In this embodiment, the power characteristic data such as power data, voltage data and current data of the first category power supply are processed through the pre-built full-category power supply access model to obtain the power parameter data of the second category power supply. The power parameter data of the second category power supply is determined based on the power characteristic data of the first category power supply, and the power parameter data of the easily controllable second category power supply is adjusted to adapt to the first category power supply, so as to complete the energy flow balance of the power access end of the target grid-connected node, so as to improve the stability of the power grid operation and effectively deal with the uncertainty of the output of new energy power supply.

[0067] S130: Adjust the number of power supply nodes in the second category of power supply based on the power supply parameter data, and control the number of network-connected power supply nodes of the second category of power supply based on the number of power supply nodes.

[0068] The power parameter data includes but is not limited to the second predicted power data of the second category power supply, the voltage data and the current data of the power supply nodes in the second category power supply. By setting a mapping relationship table between the power parameter data and the number of power supply nodes, when adjusting the power supply nodes based on the power parameter data, the mapping relationship table can be directly called to determine the number of power supply nodes; the number of power supply nodes can also be determined according to the preset reorganization rules based on the various data in the power parameter data, and the preset reorganization rules include a reorganization algorithm based on power data.

[0069] Specifically, the power parameter data can be processed by calling a reorganization algorithm based on power data to obtain the number of power nodes in the second category power supply, and corresponding control instructions can be generated based on the number of power nodes in the second category power supply, and sent to the hardware devices of the power nodes in the second category power supply to control the number of network-connected power nodes of the second category power supply, that is, the number of hardware devices used to control the power nodes to be started.

[0070] Optionally, the power supply parameter data includes second predicted power data, target voltage data and target current data of power supply nodes in the second category of power supply; adjusting the number of power supply nodes in the second category of power supply based on the power supply parameter data, including: determining the power data of a single power supply node based on the target current data and target voltage data of the power supply nodes in the power supply parameter data; determining the ratio of the second predicted power data in the power supply parameter data to the power data of the single power supply node, and determining the number of power supply nodes of the second category of power supply based on the ratio.

[0071] Specifically, the target current data and target voltage data of the power supply node are determined under the premise that the voltage and current of the first category power supply are in a balanced state. The target current data and the target voltage data of the power supply node are multiplied to obtain the output power data of the power supply node. The ratio of the second predicted power data to the output power data of the power supply node is calculated, and the ratio is rounded up to obtain the target ratio. The target ratio is the number of power supply nodes of the second category power supply.

[0072] The technical solution of this embodiment is to obtain the initial power data of each power node in the first category of power supply corresponding to the target grid-connected node, and determine the power change of each power node in the first category of power supply; determine the first predicted power data of the first category of power supply based on the power change and the initial power data of each power node in the first category of power supply; obtain the target power data of the target load and the voltage data and current data of the power node of the first category of power supply, use the target power data of the target load as the target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data and the voltage data and current data of the power node of the first category of power supply based on a pre-built full-category power supply access model, and obtain the power parameter data of the second category of power supply; adjust the number of power nodes in the second category of power supply based on the power parameter data, and control the number of grid-connected power nodes of the second category of power supply based on the number of power nodes. This technical solution uses the target power data of the target load as the target output power data of the target grid-connected node, and processes the predicted power data, voltage data and current data of the first category of power supply through a pre-built full-category power supply access model to determine the power parameter data of the second category of power supply, so as to obtain the number of power supply nodes of the second category of power supply, and to complete the scheduling and control of the power supply nodes of the second category of power supply, thereby solving the problem of poor scheduling and control of category power supplies at the power access end caused by the uncertainty of the output of new energy power sources, so that while ensuring that the output power data of the target grid-connected node meets the power demand of the target load, it ensures that the first category of power supply and the second category of power supply are also in a balanced state, avoiding the problem of grid connection abnormality caused by voltage and current imbalance at the power access end of the grid-connected node, reducing the risk of energy flow imbalance, and improving the safety and reliability of new energy grid connection.

[0073] Embodiment 2

[0074] Figure 2 This is a flow chart of a method for energy flow balance control based on a full-category power supply access model provided in the second embodiment of the present invention. This embodiment is a further optimization of the method in the above embodiment. Optionally, the initial power data of the corresponding power supply nodes are adjusted based on the power change of each power supply node to obtain the power adjustment result of each power supply node; the first predicted power data of the first category power supply is determined based on the sum of the power adjustment results of each power supply node. Figure 2 As shown, the method includes:

[0075] S210: Obtain initial power data of each power supply node in the first category of power supply corresponding to the target grid-connected node, and determine a power change of each power supply node in the first category of power supply.

[0076] S220 , adjusting the initial power data of the corresponding power nodes based on the power variation of each power node, so as to obtain the power adjustment result of each power node.

[0077] The power change of each power node is generated by the change of the environmental variable information corresponding to the power node. It should be noted that the initial power data of the i-th power node in the first category of power is The corresponding power change It is based on the time series correspondence, that is, when the environmental variable information changes, the corresponding , and further the initial power data Produce impact, thus calculate , to obtain the power adjustment result of the corresponding power node.

[0078] S230 . Determine first predicted power data of a first category of power supply based on a sum of power adjustment results of each power supply node.

[0079] Specifically, the power adjustment results of each power supply node are summed up, and the summed result is the first predicted power data of the first category power supply. The first predicted power data of the first category power supply is determined according to the following formula:

[0080]

[0081] in, represents the first predicted power data of the first category of power supply, It is the first in the first category of power supplies The initial power data of the power supply nodes, n represents the total number of power supply nodes in the first category of power supply. It should be noted that The value may also be 0, indicating that there is no first-category power supply. Should be 0.

[0082] S240. Obtain the target power data of the target load and the voltage data and current data of the power supply nodes of the first category of power supply, use the target power data of the target load as the target output power data of the target grid-connected node, and perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power supply nodes of the first category of power supply based on a pre-built full-category power supply access model to obtain the power supply parameter data of the second category of power supply.

[0083] S250: Adjust the number of power supply nodes in the second category power supply based on the power supply parameter data, and control the number of network-connected power supply nodes of the second category power supply based on the number of power supply nodes.

[0084] The technical solution of this embodiment is to obtain the initial power data of each power node in the first category of power supply corresponding to the target grid-connected node, and determine the power change of each power node in the first category of power supply; adjust the initial power data of the corresponding power node based on the power change of each power node to obtain the power adjustment result of each power node; determine the first predicted power data of the first category of power supply based on the sum of the power adjustment results of each power node; obtain the target power data of the target load and the voltage data and current data of the power node of the first category of power supply, use the target power data of the target load as the target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data and the voltage data and current data of the power node of the first category of power supply based on a pre-built full-category power supply access model, and obtain the power parameter data of the second category of power supply; adjust the number of power nodes in the second category of power supply based on the power parameter data, and control the number of grid-connected power nodes of the second category of power supply based on the number of power nodes. This technical solution uses the target power data of the target load as the target output power data of the target grid-connected node, and processes the predicted power data, voltage data and current data of the first category of power supply through a pre-built full-category power supply access model to determine the power parameter data of the second category of power supply, so as to obtain the number of power supply nodes of the second category of power supply, and to complete the scheduling and control of the power supply nodes of the second category of power supply, thereby solving the problem of poor scheduling and control of category power supplies at the power access end caused by the uncertainty of the output of new energy power sources, so that while ensuring that the output power data of the target grid-connected node meets the power demand of the target load, it ensures that the first category of power supply and the second category of power supply are also in a balanced state, avoiding the problem of grid connection abnormality caused by voltage and current imbalance at the power access end of the grid-connected node, reducing the risk of energy flow imbalance, and improving the safety and reliability of new energy grid connection.

[0085] Embodiment 3

[0086] Figure 3 Schematic diagram of the structure of an energy flow balance control device based on a full-category power supply access model provided by the third embodiment of the present invention. Figure 3 As shown, the device comprises:

[0087] The first predicted power data determination module 310 is used to obtain the initial power data of each power supply node in the first category of power supply corresponding to the target grid-connected node, and determine the power change of each power supply node in the first category of power supply; determine the first predicted power data of the first category of power supply based on the power change of each power supply node in the first category of power supply and the initial power data;

[0088] The power parameter data determination module 320 is used to obtain the target power data of the target load and the voltage data and current data of the power node of the first category power supply, use the target power data of the target load as the target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power node of the first category power supply based on the pre-built full-category power supply access model, and obtain the power parameter data of the second category power supply;

[0089] The second category power supply adjustment module 330 is used to adjust the number of power supply nodes in the second category power supply based on the power supply parameter data, and control the number of network-connected power supply nodes of the second category power supply based on the number of power supply nodes.

[0090] The technical solution of this embodiment is to obtain the initial power data of each power supply node in the first category of power supply corresponding to the target grid-connected node through the first predicted power data determination module, and determine the power change of each power supply node in the first category of power supply; determine the first predicted power data of the first category of power supply based on the power change of each power supply node in the first category of power supply and the initial power data; the power parameter data determination module obtains the target power data of the target load and the voltage data and current data of the power supply node of the first category of power supply, takes the target power data of the target load as the target output power data of the target grid-connected node, and performs energy flow balancing processing on the target output power data, the first predicted power data and the voltage data and current data of the power supply node of the first category of power supply based on the pre-built full-category power supply access model to obtain the power supply parameter data of the second category of power supply; the second category power supply adjustment module adjusts the number of power supply nodes in the second category of power supply based on the power supply parameter data, and controls the number of grid-connected power supply nodes of the second category of power supply based on the number of power supply nodes. This technical solution uses the target power data of the target load as the target output power data of the target grid-connected node, and processes the predicted power data, voltage data and current data of the first category of power supply through a pre-built full-category power supply access model to determine the power parameter data of the second category of power supply, so as to obtain the number of power supply nodes of the second category of power supply, and to complete the scheduling and control of the power supply nodes of the second category of power supply, thereby solving the problem of poor scheduling and control of category power supplies at the power access end caused by the uncertainty of the output of new energy power sources, so that while ensuring that the output power data of the target grid-connected node meets the power demand of the target load, it ensures that the first category of power supply and the second category of power supply are also in a balanced state, avoiding the problem of grid connection abnormality caused by voltage and current imbalance at the power access end of the grid-connected node, reducing the risk of energy flow imbalance, and improving the safety and reliability of new energy grid connection.

[0091] On the basis of the above embodiments, optionally, the first predicted power data determination module 310 is specifically used to obtain the real-time output power data of the target grid-connected node and the real-time input power data of the target load; when the real-time output power data is not equal to the real-time input power data, determine the power change of each power node in the first category of power supply within the first preset time period; or, when the real-time output power data is equal to the real-time input power data, determine the power change of each power node in the first category of power supply within the second preset time period, wherein the first preset time period is less than the second preset time period.

[0092] Optionally, the first predicted power data determination module 310 is specifically used to obtain environmental variable information of each power node in the first category of power supply within a first preset time period or a second preset time period, and determine the initial power change of each power node within the first preset time period or the second preset time period based on the environmental variable information; obtain energy conversion rate data corresponding to each power node in the first category of power supply, and determine the power change of each power node in the first category of power supply within the first preset time period or the second preset time period based on the energy conversion rate data of each power node and the initial power change of the corresponding power node within the first preset time period or the second preset time period.

[0093] Optionally, the first predicted power data determination module 310 is also specifically used to adjust the initial power data of the corresponding power supply nodes based on the power change of each power supply node to obtain the power adjustment results of each power supply node; and determine the first predicted power data of the first category of power supply based on the sum of the power adjustment results of each power supply node.

[0094] Optionally, the power parameter data determination module 320 includes a full-category power access model construction submodule, which is specifically used to obtain historical voltage data and historical current data of at least one power node in the first category of power supplies of the target grid-connected node within a preset time period, and historical voltage data and historical current data of at least one power node in the second category of power supplies of the target grid-connected node within a preset time period; fit the historical voltage data of each power node in the first category of power supplies with the historical voltage data of each power node in the second category of power supplies to obtain a voltage balance submodel of the target grid-connected node, and fit the historical current data of each power node in the first category of power supplies with the historical voltage data of each power node in the second category of power supplies to obtain a voltage balance submodel of the target grid-connected node. The historical current data of each power supply node in the source are fitted to obtain the current balance submodel of the target grid-connected node; the pre-configured power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply are obtained, and the power parameters of the first category power supply and the second category power supply are weightedly summed based on the power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply, and the weighted summation result is equal to the output power data of the target grid-connected node, and the obtained equation is set as the power balance submodel of the target grid-connected node; a full-category power supply access model is formed based on the voltage balance submodel, the current balance submodel and the power balance submodel.

[0095] Optionally, the power parameter data determination module 320 is specifically used to input the first predicted power data and the target output power data into the power balance sub-model to obtain the second predicted power data of the second category of power supply corresponding to the target grid-connected node; the voltage data and current data of the power supply node of the first category of power supply are respectively input into the voltage balance sub-model and the current balance sub-model in the full-category power supply access model to obtain the target voltage data and target current data of the power supply node of the second category of power supply.

[0096] Optionally, the power supply parameter data includes second predicted power data, target voltage data and target current data of power supply nodes in the second category of power supply; the second category of power supply adjustment module 330 is specifically used to determine the power data of a single power supply node based on the target current data and target voltage data of the power supply nodes in the power supply parameter data; determine the ratio of the second predicted power data in the power supply parameter data to the power data of the single power supply node, and determine the number of power supply nodes of the second category of power supply based on the ratio.

[0097] The energy flow balance control device based on the full-category power supply access model provided in the embodiment of the present invention can execute the energy flow balance control method based on the full-category power supply access model provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0098] Embodiment 4

[0099] Figure 4 1 is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0100] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11 in communication, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0101] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0102] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the energy flow balance control method based on the full-category power access model.

[0103] In some embodiments, the energy flow balancing control method based on the full-category power access model can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the energy flow balancing control method based on the full-category power access model described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the energy flow balancing control method based on the full-category power access model in any other appropriate manner (for example, by means of firmware).

[0104] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] The computer program for implementing the energy flow balance control method based on the full-category power access model of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or entirely on a remote machine or server.

[0106] Embodiment 5

[0107] Embodiment 5 of the present invention further provides a computer-readable storage medium, the computer-readable storage medium storing computer instructions, the computer instructions being used to enable a processor to execute an energy flow balance control method based on a full-category power supply access model, the method comprising:

[0108] Obtaining initial power data of each power supply node in the first category of power supplies corresponding to the target grid-connected node, and determining a power change of each power supply node in the first category of power supplies; determining first predicted power data of the first category of power supplies based on the power change of each power supply node in the first category of power supplies and the initial power data;

[0109] Obtain target power data of the target load and voltage data and current data of the power supply node of the first category power supply, use the target power data of the target load as the target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power supply node of the first category power supply based on a pre-built full-category power supply access model, and obtain power supply parameter data of the second category power supply;

[0110] The number of power supply nodes in the second category power supply is adjusted based on the power supply parameter data, and the number of network-connected power supply nodes of the second category power supply is controlled based on the number of power supply nodes.

[0111] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0114] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0115] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0116] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for energy flow balance control based on a full-category power supply access model, characterized in that: include: Obtaining initial power data of each power supply node in the first category of power supplies corresponding to the target grid-connected node, and determining a power change of each power supply node in the first category of power supplies; determining first predicted power data of the first category of power supplies based on the power change of each power supply node in the first category of power supplies and the initial power data; Obtain target power data of a target load and voltage data and current data of a power supply node of the first category of power supply, use the target power data of the target load as target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power supply node of the first category of power supply based on a pre-built full-category power supply access model, and obtain power supply parameter data of a second category of power supply; Adjust the number of power supply nodes in the second category of power supply based on the power supply parameter data, and control the number of network-connected power supply nodes of the second category of power supply based on the number of power supply nodes; The construction of the full-category power supply access model includes: Acquire historical voltage data and historical current data of at least one power supply node of the first category of power supplies of the target grid-connected node within a preset time period, and historical voltage data and historical current data of at least one power supply node of the second category of power supplies of the target grid-connected node within the preset time period; Fitting the historical voltage data of each power node in the first category of power supply with the historical voltage data of each power node in the second category of power supply to obtain a voltage balance submodel of the target grid-connected node, and fitting the historical current data of each power node in the first category of power supply with the historical current data of each power node in the second category of power supply to obtain a current balance submodel of the target grid-connected node; Obtain pre-configured power dynamic adjustment parameters of the first category power supply and the second category power supply, perform weighted summation on the power parameters of the first category power supply and the second category power supply based on the power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply, set the obtained weighted summation result equal to the output power data of the target grid-connected node, and set the obtained equation as the power balance sub-model of the target grid-connected node; Forming the full-category power supply access model based on the voltage balance sub-model, the current balance sub-model and the power balance sub-model; The method performs energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power supply nodes of the first category power supply based on the pre-built full-category power supply access model to obtain power supply parameter data of the second category power supply, including: Inputting the first predicted power data and the target output power data into the power balance sub-model to obtain second predicted power data of the second category power supply corresponding to the target grid-connected node; The voltage data and current data of the power supply nodes of the first category power supply are respectively input into the voltage balance submodel and the current balance submodel in the full category power supply access model to obtain the target voltage data and target current data of the power supply nodes of the second category power supply.

2. The method according to claim 1, characterized in that The determining of the power change of each power supply node in the first category of power supply includes: Acquire real-time output power data of the target grid-connected node and real-time input power data of the target load; In a case where the real-time output power data is not equal to the real-time input power data, determining a power change amount of each power supply node in the first category of power supply within a first preset time period; or, When the real-time output power data is equal to the real-time input power data, determine the power change of each power supply node in the first category of power supply within a second preset time period, wherein the first preset time period is smaller than the second preset time period.

3. The method according to claim 2, characterized in that Determining a power change amount of each power supply node in the first category of power supply within a first preset time period or a second preset time period includes: Obtaining environmental variable information of each power supply node in the first category of power supply within the first preset time period or the second preset time period, and determining an initial power change amount of each power supply node within the first preset time period or the second preset time period based on the environmental variable information; Obtain energy conversion rate data corresponding to each power supply node in the first category of power supply, and determine the power change of each power supply node in the first category of power supply within the first preset time period or the second preset time period based on the energy conversion rate data of each power supply node and the initial power change of the corresponding power node within the first preset time period or the second preset time period.

4. The method according to claim 1, characterized in that: The determining the first predicted power data of the first category of power supply based on the power change amount of each of the power supply nodes in the first category of power supply and the initial power data includes: Based on the power variation of each power node, the initial power data of the corresponding power node are adjusted respectively to obtain the power adjustment result of each power node; The first predicted power data of the first category of power supply is determined based on the sum of the power adjustment results of each of the power supply nodes.

5. The method according to claim 1, characterized in that The power supply parameter data includes second predicted power data, target voltage data and target current data of power supply nodes in the second category of power supply; The adjusting the number of power supply nodes in the second category of power supply based on the power supply parameter data includes: determining power data of a single power node based on target current data and target voltage data of the power node in the power parameter data; Determine a ratio of the second predicted power data in the power parameter data to the power data of the single power node, and determine the number of power nodes of the second category of power based on the ratio.

6. An energy flow balance control device based on a full-category power supply access model, characterized in that: include: A first predicted power data determination module is used to obtain initial power data of each power supply node in the first category of power supply corresponding to the target grid-connected node, and determine the power change of each power supply node in the first category of power supply; determine the first predicted power data of the first category of power supply based on the power change of each power supply node in the first category of power supply and the initial power data; a power parameter data determination module, used to obtain target power data of a target load and voltage data and current data of a power node of the first category of power supply, use the target power data of the target load as target output power data of the target grid-connected node, perform energy flow balancing processing on the target output power data, the first predicted power data, and the voltage data and current data of the power node of the first category of power supply based on a pre-built full-category power supply access model, and obtain power parameter data of a second category of power supply; A second category power supply adjustment module, used for adjusting the number of power supply nodes in the second category power supply based on the power supply parameter data, and controlling the number of network-connected power supply nodes of the second category power supply based on the number of power supply nodes; The power parameter data determination module is specifically used to obtain historical voltage data and historical current data of at least one power node in the first category of power sources of the target grid-connected node within a preset time period, and historical voltage data and historical current data of at least one power node in the second category of power sources of the target grid-connected node within the preset time period; fit the historical voltage data of each power node in the first category of power sources with the historical voltage data of each power node in the second category of power sources to obtain a voltage balance submodel of the target grid-connected node, and fit the historical current data of each power node in the first category of power sources with the historical current data of each power node in the second category of power sources to obtain a voltage balance submodel of the target grid-connected node. According to the fitting processing, the current balance submodel of the target grid-connected node is obtained; the pre-configured power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply are obtained, and the power parameters of the first category power supply and the power parameters of the second category power supply are weighted summed based on the power dynamic adjustment parameters of the first category power supply and the power dynamic adjustment parameters of the second category power supply, and the obtained weighted summation result is equal to the output power data of the target grid-connected node, and the obtained equation is set as the power balance submodel of the target grid-connected node; the full-category power supply access model is formed based on the voltage balance submodel, the current balance submodel and the power balance submodel; The power supply parameter data determination module is also specifically used to input the first predicted power data and the target output power data into the power balance sub-model to obtain the second predicted power data of the second category of power supply corresponding to the target grid-connected node; and input the voltage data and current data of the power supply node of the first category of power supply into the voltage balance sub-model and the current balance sub-model in the full-category power supply access model respectively to obtain the target voltage data and target current data of the power supply node of the second category of power supply.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy flow balance control method based on the full-category power supply access model described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy flow balance control method based on a full-category power supply access model as described in any one of claims 1 to 5 when executed.

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