A multi-source power grid peak shaving and frequency regulation system

Abstract

This application discloses a multi-source power grid peak-shaving and frequency regulation system, comprising: a construction module for constructing a multi-source power grid structure reference diagram; a screening module for screening reference data groups for corresponding power supply master nodes based on the correlation between data acquisition nodes and power supply master nodes; a determination module for generating environmental status levels based on environmental-related data in the reference data groups, generating operating status levels based on power operation data in the reference data groups, and determining preferred data groups for corresponding power supply master nodes based on environmental status levels and operating status levels; and a formulation module for constructing a preferred data prediction model for a corresponding power supply area based on the preferred data groups of multiple power supply master nodes in the same power supply area, and formulating peak-shaving and frequency regulation strategies for the corresponding power supply area based on the preferred data prediction model, thereby improving the operational stability and power quality of the multi-source power grid while ensuring that power demand is met.

Description

Technical Field

[0001] This application relates to the field of power system dispatching technology, and in particular to a multi-source power grid peak shaving and frequency regulation system. Background Technology

[0002] With rapid economic development and improved living standards, electricity demand is constantly increasing, and power load fluctuations are becoming increasingly significant. Therefore, ensuring the safe and stable operation of the power system is of paramount importance. Power grid peak shaving and frequency regulation refer to adjusting operating modes, loads, or compensation to maintain a balance between the power grid's input and output power and load changes, thereby ensuring system energy security and economical operation.

[0003] In existing technologies, peak shaving and frequency regulation mostly rely on manual dispatching. With the addition of various new energy sources, manual dispatching and traditional automated dispatching can no longer fully meet the power grid's electricity demand and cannot guarantee the stability and power quality of the power system. Therefore, there is an urgent need for a peak shaving and frequency regulation system that can rationally arrange power supply strategies according to the actual needs of multi-source power grids. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a multi-source power grid peak-shaving and frequency regulation system. It constructs a multi-source power grid structure reference diagram, selects reference data groups for corresponding power supply master nodes based on the correlation between data acquisition nodes and power supply master nodes within the reference diagram, selects preferred data groups for power supply master nodes based on the environmental and operational status levels of the reference data groups, and constructs a preferred data prediction model for the corresponding power supply area. This yields peak-shaving and frequency regulation strategies for each power supply area, improving the operational stability and power quality of the multi-source power grid while ensuring that power demand is met.

[0005] In some embodiments of this application, a multi-source power grid peak-shaving and frequency regulation system is provided, comprising:

[0006] The construction module is used to obtain the composition architecture and connection relationship of the multi-source power grid, and construct a multi-source power grid structure reference diagram based on geographical location information. The multi-source power grid structure reference diagram includes several data acquisition nodes and several power supply areas, and the power supply areas include multiple power supply master nodes.

[0007] The filtering module is used to filter out the reference data group of the corresponding power supply master node based on the degree of correlation between the data acquisition node and the power supply master node;

[0008] The determination module is used to generate an environmental status level based on environmental-related data in the reference data group, generate an operating status level based on power operation data in the reference data group, and determine the preferred data group for the corresponding power supply master node based on the environmental status level and the operating status level.

[0009] The module is used to construct a preferred data prediction model for a corresponding power supply area based on preferred data sets from multiple power supply master nodes in the same power supply area, and to formulate peak shaving and frequency regulation strategies for the corresponding power supply area based on the preferred data prediction model.

[0010] In some embodiments of this application, a multi-source power grid structure reference diagram is constructed, including:

[0011] Obtain the composition and connection relationships of the multi-source power grid, and generate a basic map of the multi-source power grid based on map location information;

[0012] Obtain feature information of multiple regions in the multi-source power grid base map, set multiple undetermined regions based on the feature information, and obtain the power supply demand of the undetermined regions. If the power supply demand is less than the preset power supply demand threshold, then set the corresponding undetermined region as the power supply region.

[0013] If the power demand exceeds the preset power demand threshold, the corresponding undetermined area will be divided into secondary areas.

[0014] Obtain historical power data at the branch nodes of the undetermined region that needs to be further divided, analyze the historical power data, and obtain the historical power supply at the corresponding branch nodes;

[0015] If the number of branch nodes within the cut-off area is greater than the preset node number threshold or the sum of historical power supply is greater than the preset power supply threshold, then the cut-off area is set as the power supply area.

[0016] Obtain historical power data at branch nodes within each power supply area, calculate the utilization coefficient and proportion coefficient of historical power data at each branch node within the corresponding power supply area, and calculate the degree of influence of the corresponding branch node on the corresponding power supply area.

[0017] Branch nodes whose impact exceeds the preset impact level are designated as the main power supply nodes within the corresponding power supply area.

[0018] In some embodiments of this application, reference data groups corresponding to the power supply master node are selected based on the degree of association between the data acquisition node and the power supply master node, including:

[0019] The location information of each power supply master node is set as the origin, and the scanning radius is set according to the importance of the power supply master node in the corresponding power supply area.

[0020] The scanning area of ​​the corresponding power supply master node is generated based on the origin and scanning radius. Data acquisition nodes within the scanning area are obtained, and the first correlation coefficient between the corresponding data acquisition node and the power supply master node is generated based on the positional relationship between the data acquisition node and the origin.

[0021] The change characteristics of power operation data of data acquisition nodes within the scanning area are obtained, and the change characteristics are compared with the reference change characteristics at the power supply master node. Based on the comparison results, a second correlation coefficient between the corresponding data acquisition node and the power supply master node is generated.

[0022] The degree of association is generated based on the first and second association coefficients;

[0023] M = m1*e1 + m2*e2;

[0024] Where M represents the degree of correlation between the corresponding data acquisition node and the power supply master node, m1 is the first correlation coefficient, e1 is the first weight coefficient, e2 is the second weight coefficient, and m2 is the second correlation coefficient.

[0025] Data acquisition nodes with a correlation degree greater than a preset correlation degree threshold are set as feature data acquisition nodes of the corresponding power supply master node;

[0026] The environmental data and power operation data of the feature data acquisition node at the same time point are set as the reference data group of the corresponding power supply master node.

[0027] In some embodiments of this application, the scanning radius is set according to the importance of the power supply master node within the corresponding power supply area, including:

[0028] Pre-set a first preset importance level, a second preset importance level, and a third preset importance level;

[0029] When the importance of the power supply master node in the corresponding power supply area is less than the first preset importance, the scanning radius is set to the first preset scanning radius.

[0030] When the importance of the power supply master node in the corresponding power supply area is between the first preset importance level and the second preset importance level, the scanning radius is set to the second preset scanning radius;

[0031] When the importance of the power supply master node in the corresponding power supply area is between the second preset importance level and the third preset importance level, the scanning radius is set to the third preset scanning radius;

[0032] When the importance of the power supply master node in the corresponding power supply area is greater than the third preset importance, the scanning radius is set to the fourth preset scanning radius.

[0033] In some embodiments of this application, acquiring the changing characteristics of power operation data of data acquisition nodes within the scanning area includes:

[0034] The selected data acquisition node collects power operation data within a preset time period. The power operation data includes power generation data, power consumption data, and energy data.

[0035] Based on the power generation data, power consumption data, and energy data of the same data acquisition node within a preset time period, construct power generation data change curves, power consumption data change curves, and energy data change curves;

[0036] By analyzing the power generation data change curves, electricity consumption data change curves, and energy data change curves, the first change time node, the second change time node, and the third change time node of the power generation data change curves, electricity consumption data change curves, and energy data change curves are obtained.

[0037] If the time deviations between the first, second, and third change time nodes are not all within the preset deviation range, then the corresponding first, second, and third change time nodes are removed.

[0038] If the time deviations between the first, second, and third change time nodes are all within the preset deviation range, then the first change value, the second change value, and the third change value at the first, second, and third change time nodes are obtained.

[0039] Based on the relationship between the first change value, the second change value, and the third change value and the preset change value range, the first change coefficient, the second change coefficient, and the third change coefficient are set respectively;

[0040] A first change feature is generated based on a first change time point and a first change coefficient; a second change feature is generated based on a second change time point and a second change coefficient; and a third change feature is generated based on a third change time point and a third change coefficient.

[0041] Acquire power operation data of the power supply master node during a preset time period, and generate a first reference change feature, a second reference change feature, and a third reference change feature;

[0042] Multiple first change feature difference values ​​are generated based on the first change feature and the corresponding first reference change feature; multiple second change feature difference values ​​are generated based on the second change feature and the corresponding second reference change feature; and multiple third change feature difference values ​​are generated based on the third change feature and the corresponding third reference change feature.

[0043] A second correlation coefficient is generated based on the difference values ​​of the first change feature, the second change feature, and the third change feature.

[0044]

[0045] Where n is the number of time nodes where the time deviations between the first, second, and third change time nodes are all within the preset deviation range; △p1i is the difference value of the i-th first change feature; p01i is the difference value of the first standard change feature corresponding to the i-th first change feature; △p2i is the difference value of the i-th second change feature; p02i is the difference value of the second standard change feature corresponding to the i-th second change feature; △p3i is the difference value of the i-th third change feature; p03i is the difference value of the third standard change feature corresponding to the i-th third change feature; a1i is the weight coefficient corresponding to the i-th first change feature; a2i is the weight coefficient corresponding to the i-th second change feature; and a3i is the weight coefficient corresponding to the i-th third change feature.

[0046] In some embodiments of this application, determining the preferred data set for the corresponding power supply master node based on the environmental state level and the operational state level includes:

[0047] Obtain environmental-related data from the reference data group of the power supply master node, calculate the influence coefficient of the environmental-related data on the power operation data of the power supply master node, and set the environmental-related data with an influence coefficient greater than the preset influence coefficient threshold as environmental impact data;

[0048] The environmental impact data in each reference data group is compared with the preset standard data range to obtain the environmental impact data deviation value of multiple environmental impact data. The environmental impact data deviation value of the same reference data group is weighted and averaged to obtain the environmental status level of the corresponding reference data group.

[0049] The reference data sets of the power supply master node are classified according to the environmental condition level to obtain the reference data sets of the same power supply master node at different environmental condition levels.

[0050] The power operation data in the reference data group of the same environmental condition level is compared with the corresponding preset standard power operation data range to determine the operation status level of the corresponding environmental condition level.

[0051] Power operation data whose operating status level is greater than the preset operating status level threshold under the same environmental condition level are set as preferred power operation data;

[0052] The environmental impact data corresponding to multiple environmental status levels, along with the corresponding preferred power operation data, are combined to form the preferred data group for the corresponding power supply master node.

[0053] In some embodiments of this application, determining the operating state level corresponding to the environmental state level includes:

[0054] Based on the constraints of stable power grid operation and cost optimization control, the preset standard power operation data intervals of different environmental state levels are divided into multiple preset standard power operation data sub-intervals, and a corresponding operation state level is set for each preset standard power operation data sub-interval.

[0055] The power operation data of the same environmental condition level is compared with the corresponding preset standard power operation data range to obtain the preset standard power operation data sub-range where the power operation data is located, and the operation status level of the corresponding power operation data is set.

[0056] In some embodiments of this application, a preferred data prediction model for a corresponding power supply area is constructed based on preferred data sets of multiple power supply master nodes in the same power supply area, including:

[0057] Analyze the preferred power operation data in the preferred data set to obtain the demand factor data and supply factor data corresponding to the environmental state level;

[0058] Using environmental impact data and demand factor data for each environmental state level as inputs and supply factor data as outputs, a neural network is trained to obtain an optimal data prediction model.

[0059] In some embodiments of this application, a peak-shaving and frequency regulation strategy for the corresponding power supply area is formulated based on a preferred data prediction model, including:

[0060] Obtain comprehensive demand factor data for each power supply area, divide the comprehensive demand factor data into multiple sub-comprehensive demand factor data according to the importance ratio coefficient of each power supply master node in the current power supply area, and allocate them to the corresponding power supply master node in the current power supply area;

[0061] The real-time environmental impact data and the corresponding comprehensive demand factor data at each power supply master node are input into the optimal data prediction model to obtain the predicted supply factor data at the corresponding power supply master node.

[0062] By comprehensively analyzing the predicted supply factor data at multiple power supply master nodes in the same power supply area, the peak-shaving and frequency regulation strategies for the corresponding power supply area can be determined.

[0063] The multi-source power grid peak-shaving and frequency regulation system of this application embodiment has the following advantages compared with the prior art:

[0064] A reference diagram of a multi-source power grid structure is constructed. Based on the correlation between data acquisition nodes and power supply master nodes in the reference diagram, reference data groups for corresponding power supply master nodes are selected. Based on the environmental status level and operating status level of the reference data groups, the preferred data groups for power supply master nodes are selected. A prediction model for the preferred data for the corresponding power supply area is constructed, thereby obtaining the peak-shaving and frequency regulation strategy for each power supply area. Under the premise of ensuring that power demand is met, the operational stability and power quality of the multi-source power grid are improved. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of a multi-source power grid peak-shaving and frequency regulation system in a preferred embodiment of this application. Detailed Implementation

[0066] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0067] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0068] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0069] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0070] like Figure 1 As shown in the preferred embodiment of this application, a multi-source power grid peak-shaving and frequency regulation system includes:

[0071] The construction module is used to obtain the composition architecture and connection relationship of the multi-source power grid, and construct a multi-source power grid structure reference diagram based on geographical location information. The multi-source power grid structure reference diagram includes several data acquisition nodes and several power supply areas, and the power supply areas include multiple power supply master nodes.

[0072] The filtering module is used to filter out the reference data group of the corresponding power supply master node based on the degree of correlation between the data acquisition node and the power supply master node;

[0073] The determination module is used to generate an environmental status level based on environmental-related data in the reference data group, generate an operating status level based on power operation data in the reference data group, and determine the preferred data group for the corresponding power supply master node based on the environmental status level and the operating status level.

[0074] The module is used to construct a preferred data prediction model for a corresponding power supply area based on preferred data sets from multiple power supply master nodes in the same power supply area, and to formulate peak shaving and frequency regulation strategies for the corresponding power supply area based on the preferred data prediction model.

[0075] In this embodiment, the architecture includes distributed generation, energy storage system, energy management system and static conversion system. The connection relationship refers to the electrical wiring network structure inside the multi-source power grid, the location of the power grid node where the distributed power source is located, the power supply system, etc.

[0076] In this embodiment, the reference data group refers to the environmental and power operation data of the data acquisition node whose correlation with the power supply master node is greater than a preset correlation threshold, and the preferred data group refers to the preferred power operation data and corresponding environmental image data of each power supply master node whose operation status level is greater than a preset operation status level threshold under different environmental status levels.

[0077] In this embodiment, a multi-source power grid structure reference diagram is constructed. Based on the correlation between data acquisition nodes and power supply master nodes in the multi-source power grid structure reference diagram, reference data groups for corresponding power supply master nodes are selected. Based on the environmental state level and operating state level of the reference data groups, preferred data groups for power supply master nodes are selected, and a preferred data prediction model for the corresponding power supply area is constructed. This yields peak-shaving and frequency regulation strategies for each power supply area, thereby improving the operational stability and power quality of the multi-source power grid while ensuring that power demand is met.

[0078] In some embodiments of this application, a multi-source power grid structure reference diagram is constructed, including:

[0079] Obtain the composition and connection relationships of the multi-source power grid, and generate a basic map of the multi-source power grid based on map location information;

[0080] Obtain feature information of multiple regions in the multi-source power grid base map, set multiple undetermined regions based on the feature information, and obtain the power supply demand of the undetermined regions. If the power supply demand is less than the preset power supply demand threshold, then set the corresponding undetermined region as the power supply region.

[0081] If the power demand exceeds the preset power demand threshold, the corresponding undetermined area will be divided into secondary areas.

[0082] Obtain historical power data at the branch nodes of the undetermined region that needs to be further divided, analyze the historical power data, and obtain the historical power supply at the corresponding branch nodes;

[0083] If the number of branch nodes within the cut-off area is greater than the preset node number threshold or the sum of historical power supply is greater than the preset power supply threshold, then the cut-off area is set as the power supply area.

[0084] Obtain historical power data at branch nodes within each power supply area, calculate the utilization coefficient and proportion coefficient of historical power data at each branch node within the corresponding power supply area, and calculate the degree of influence of the corresponding branch node on the corresponding power supply area.

[0085] Branch nodes whose impact exceeds the preset impact level are designated as the main power supply nodes within the corresponding power supply area.

[0086] In this embodiment, regional feature information refers to the geographical features and meteorological data of multiple regions. Based on the regional feature information, the multi-source power grid base map is divided into multiple undetermined regions, laying the foundation for subsequent formulation of reasonable peak-shaving and frequency regulation strategies and improving the management efficiency of the multi-source power grid.

[0087] In this embodiment, the power supply demand is obtained by processing the average of multiple power supply demands of the undetermined area. When the power supply demand is greater than the preset power supply demand threshold, it indicates that the power demand of the current undetermined area is large and complex. The corresponding undetermined area should be divided a second time to improve the power supply stability of each power supply area.

[0088] In this embodiment, the undetermined area that needs to be subdivided is scanned and analyzed to obtain the branch node location of the line in each undetermined area and the corresponding historical power data. The historical power data includes power supply data, power demand data, etc. The undetermined area is subdivided according to the number of branch nodes and the sum of power supply, which simplifies the power adjustment of the corresponding area.

[0089] In this embodiment, the utilization coefficient refers to the participation ratio and data utilization degree of the corresponding branch node in the corresponding power supply area. The data utilization degree includes full data utilization and random data utilization. If the participation ratio is large and the data utilization degree is large, the utilization coefficient is large, and vice versa. The proportion coefficient refers to the ratio of the power supply of the corresponding branch node to the power demand of the corresponding power supply area. The larger the ratio, the larger the proportion coefficient. The degree of influence = utilization coefficient + proportion coefficient.

[0090] In some embodiments of this application, reference data groups corresponding to the power supply master node are selected based on the degree of association between the data acquisition node and the power supply master node, including:

[0091] The location information of each power supply master node is set as the origin, and the scanning radius is set according to the importance of the power supply master node in the corresponding power supply area.

[0092] The scanning area of ​​the corresponding power supply master node is generated based on the origin and scanning radius. Data acquisition nodes within the scanning area are obtained, and the first correlation coefficient between the corresponding data acquisition node and the power supply master node is generated based on the positional relationship between the data acquisition node and the origin.

[0093] The change characteristics of power operation data of data acquisition nodes within the scanning area are obtained, and the change characteristics are compared with the reference change characteristics at the power supply master node. Based on the comparison results, a second correlation coefficient between the corresponding data acquisition node and the power supply master node is generated.

[0094] The degree of association is generated based on the first and second association coefficients;

[0095] M = m1*e1 + m2*e2;

[0096] Where M represents the degree of correlation between the corresponding data acquisition node and the power supply master node, m1 is the first correlation coefficient, e1 is the first weight coefficient, e2 is the second weight coefficient, and m2 is the second correlation coefficient.

[0097] Data acquisition nodes with a correlation degree greater than a preset correlation degree threshold are set as feature data acquisition nodes of the corresponding power supply master node;

[0098] The environmental data and power operation data of the feature data acquisition node at the same time point are set as the reference data group of the corresponding power supply master node.

[0099] In some embodiments of this application, the scanning radius is set according to the importance of the power supply master node within the corresponding power supply area, including:

[0100] Pre-set a first preset importance level, a second preset importance level, and a third preset importance level;

[0101] When the importance of the power supply master node in the corresponding power supply area is less than the first preset importance, the scanning radius is set to the first preset scanning radius.

[0102] When the importance of the power supply master node in the corresponding power supply area is between the first preset importance level and the second preset importance level, the scanning radius is set to the second preset scanning radius;

[0103] When the importance of the power supply master node in the corresponding power supply area is between the second preset importance level and the third preset importance level, the scanning radius is set to the third preset scanning radius;

[0104] When the importance of the power supply master node in the corresponding power supply area is greater than the third preset importance, the scanning radius is set to the fourth preset scanning radius.

[0105] In this embodiment, the first preset scanning radius < the second preset scanning radius < the third preset scanning radius < the fourth preset scanning radius.

[0106] In some embodiments of this application, acquiring the changing characteristics of power operation data of data acquisition nodes within the scanning area includes:

[0107] The selected data acquisition node collects power operation data within a preset time period. The power operation data includes power generation data, power consumption data, and energy data.

[0108] Based on the power generation data, power consumption data, and energy data of the same data acquisition node within a preset time period, construct power generation data change curves, power consumption data change curves, and energy data change curves;

[0109] By analyzing the power generation data change curves, electricity consumption data change curves, and energy data change curves, the first change time node, the second change time node, and the third change time node of the power generation data change curves, electricity consumption data change curves, and energy data change curves are obtained.

[0110] If the time deviations between the first, second, and third change time nodes are not all within the preset deviation range, then the corresponding first, second, and third change time nodes are removed.

[0111] If the time deviations between the first, second, and third change time nodes are all within the preset deviation range, then the first change value, the second change value, and the third change value at the first, second, and third change time nodes are obtained.

[0112] Based on the relationship between the first change value, the second change value, and the third change value and the preset change value range, the first change coefficient, the second change coefficient, and the third change coefficient are set respectively;

[0113] A first change feature is generated based on a first change time point and a first change coefficient; a second change feature is generated based on a second change time point and a second change coefficient; and a third change feature is generated based on a third change time point and a third change coefficient.

[0114] Acquire power operation data of the power supply master node during a preset time period, and generate a first reference change feature, a second reference change feature, and a third reference change feature;

[0115] Multiple first change feature difference values ​​are generated based on the first change feature and the corresponding first reference change feature; multiple second change feature difference values ​​are generated based on the second change feature and the corresponding second reference change feature; and multiple third change feature difference values ​​are generated based on the third change feature and the corresponding third reference change feature.

[0116] A second correlation coefficient is generated based on the difference values ​​of the first change feature, the second change feature, and the third change feature.

[0117]

[0118] Where n is the number of time nodes where the time deviations between the first, second, and third change time nodes are all within the preset deviation range; △p1i is the difference value of the i-th first change feature; p01i is the difference value of the first standard change feature corresponding to the i-th first change feature; △p2i is the difference value of the i-th second change feature; p02i is the difference value of the second standard change feature corresponding to the i-th second change feature; △p3i is the difference value of the i-th third change feature; p03i is the difference value of the third standard change feature corresponding to the i-th third change feature; a1i is the weight coefficient corresponding to the i-th first change feature; a2i is the weight coefficient corresponding to the i-th second change feature; and a3i is the weight coefficient corresponding to the i-th third change feature.

[0119] In this embodiment, power generation data includes the power generation, power generation capacity, and power generation efficiency at the corresponding data acquisition node; power consumption data includes the power consumption, power load, and peak-valley values ​​at the corresponding data acquisition node; and energy data includes the energy consumption and energy conversion efficiency at the corresponding data acquisition node.

[0120] In this embodiment, the change time node refers to the time node where the change value is greater than the preset minimum change value. The change time deviations between the first change time node, the second change time node, and the third change time node are all within the preset deviation range, indicating that the corresponding time nodes are synchronous change nodes of power operation data. By comparing the change characteristics of the synchronous change node with those of the supply master node, the second correlation coefficient between the corresponding data acquisition node and the supply master node is accurately obtained. This lays the data foundation for subsequently determining the preferred data set of the corresponding supply master node, ensuring the accuracy of the predicted power operation data of the supply master node, thereby ensuring the effectiveness of the peak-shaving and frequency regulation strategy of the multi-source power grid and improving the stability and efficiency of the multi-source power grid operation.

[0121] In some embodiments of this application, determining the preferred data set for the corresponding power supply master node based on the environmental state level and the operational state level includes:

[0122] Obtain environmental-related data from the reference data group of the power supply master node, calculate the influence coefficient of the environmental-related data on the power operation data of the power supply master node, and set the environmental-related data with an influence coefficient greater than the preset influence coefficient threshold as environmental impact data;

[0123] The environmental impact data in each reference data group is compared with the preset standard data range to obtain the environmental impact data deviation value of multiple environmental impact data. The environmental impact data deviation value of the same reference data group is weighted and averaged to obtain the environmental status level of the corresponding reference data group.

[0124] The reference data sets of the power supply master node are classified according to the environmental condition level to obtain the reference data sets of the same power supply master node at different environmental condition levels.

[0125] The power operation data in the reference data group of the same environmental condition level is compared with the corresponding preset standard power operation data range to determine the operation status level of the corresponding environmental condition level.

[0126] Power operation data whose operating status level is greater than the preset operating status level threshold under the same environmental condition level are set as preferred power operation data;

[0127] The environmental impact data corresponding to multiple environmental status levels, along with the corresponding preferred power operation data, are combined to form the preferred data group for the corresponding power supply master node.

[0128] In this embodiment, environmental data includes ambient temperature, humidity, wind force, and light intensity. The influence coefficient refers to the degree of influence of each environmental data on the power operation data. Environmental impact data refers to environmental data that can have a significant impact on the power operation data of a multi-source power grid. For example, wind force affects the wind power generation system in a multi-source power grid, and light intensity affects the photovoltaic power generation system in a multi-source power grid.

[0129] In this embodiment, the environmental impact data deviation values ​​of the same reference data group are processed by weighted average. The smaller the weighted average, the higher the environmental status level, and vice versa. The higher the environmental status level, the better the corresponding environmental impact data is for the power supply of the multi-source power grid. The higher the operating status level of the power operation data, the better the power supply of the corresponding main power supply node is and the stable operation.

[0130] In some embodiments of this application, determining the operating state level under the corresponding environmental state level includes:

[0131] Based on the constraints of stable power grid operation and cost optimization control, the preset standard power operation data intervals of different environmental state levels are divided into multiple preset standard power operation data sub-intervals, and a corresponding operation state level is set for each preset standard power operation data sub-interval.

[0132] The power operation data of the same environmental condition level is compared with the corresponding preset standard power operation data range to obtain the preset standard power operation data sub-range where the power operation data is located, and the operation status level of the corresponding power operation data is set.

[0133] In this embodiment, cost optimization control refers to maximizing the benefits based on the annual investment costs, fuel costs, operation and maintenance costs, environmental costs, and interaction costs with other power grids of a multi-source power grid.

[0134] In this embodiment, based on the constraints of grid operation stability and cost control optimization, the preset standard power operation data range is divided and the operation status level is set. When the grid operation is more stable and the cost is the lowest, the operation status level is higher, and vice versa.

[0135] In some embodiments of this application, a preferred data prediction model for a corresponding power supply area is constructed based on preferred data sets of multiple power supply master nodes in the same power supply area, including:

[0136] Analyze the preferred power operation data in the preferred data set to obtain the demand factor data and supply factor data corresponding to the environmental state level;

[0137] Using environmental impact data and demand factor data for each environmental state level as inputs and supply factor data as outputs, a neural network is trained to obtain an optimal data prediction model.

[0138] In this embodiment, demand factor data refers to data in power operation data that is related to power demand, such as electricity demand and electricity price level. Supply factor data refers to data in power operation data that needs to be regulated to meet power demand, such as grid frequency and grid load.

[0139] In this embodiment, an optimal data prediction model is constructed by using demand factor data and supply factor data of multiple power supply master nodes in each power supply area at different environmental state levels. This improves the accuracy of predicting power operation data. Based on the predicted power operation data at each power supply master node at different environmental state levels, the peak-shaving and frequency regulation strategy for the corresponding power supply area is determined to ensure the power supply stability and efficiency of the corresponding power supply area, thereby improving the operational stability and power quality of the multi-source power grid.

[0140] In some embodiments of this application, a peak-shaving and frequency regulation strategy for the corresponding power supply area is formulated based on a preferred data prediction model, including:

[0141] Obtain comprehensive demand factor data for each power supply area, divide the comprehensive demand factor data into multiple sub-comprehensive demand factor data according to the importance ratio coefficient of each power supply master node in the current power supply area, and allocate them to the corresponding power supply master node in the current power supply area;

[0142] The real-time environmental impact data and the corresponding comprehensive demand factor data at each power supply master node are input into the optimal data prediction model to obtain the predicted supply factor data at the corresponding power supply master node.

[0143] By comprehensively analyzing the predicted supply factor data at multiple power supply master nodes in the same power supply area, the peak-shaving and frequency regulation strategies for the corresponding power supply area can be determined.

[0144] In this embodiment, the important proportional coefficient is set based on the historical supply volume and historical power supply stability of the power supply master node. The comprehensive demand factor data is divided according to the important proportional coefficient to obtain the sub-comprehensive demand factor data of each power supply master node. The sub-comprehensive demand factor data and the real-time environmental image data of the corresponding power supply master node are input into the optimal data prediction model to obtain the predicted supply factor data. The predicted supply factor data of multiple power supply master nodes in the same power supply area are comprehensively analyzed to formulate a reasonable peak-shaving and frequency regulation strategy. Under the premise of ensuring the power demand of the multi-source power grid, the regulation scheme of the power system is optimized, which improves the operation stability and power quality of the multi-source power grid.

[0145] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A multi-source power grid peak-shaving and frequency regulation system, characterized in that, include: The construction module is used to obtain the composition architecture and connection relationship of the multi-source power grid, and construct a multi-source power grid structure reference diagram based on geographical location information. The multi-source power grid structure reference diagram includes several data acquisition nodes and several power supply areas, and the power supply areas include multiple power supply master nodes. The filtering module is used to filter out the reference data group of the corresponding power supply master node based on the degree of correlation between the data acquisition node and the power supply master node; The determination module is used to generate an environmental status level based on environmental-related data in the reference data group, generate an operating status level based on power operation data in the reference data group, and determine the preferred data group for the corresponding power supply master node based on the environmental status level and the operating status level. The module is used to construct a preferred data prediction model for a corresponding power supply area based on preferred data sets from multiple power supply master nodes in the same power supply area, and to formulate peak shaving and frequency regulation strategies for the corresponding power supply area based on the preferred data prediction model. Construct a reference diagram for a multi-source power grid structure, including: Obtain the composition and connection relationships of the multi-source power grid, and generate a basic map of the multi-source power grid based on map location information; Obtain feature information of multiple regions in the multi-source power grid base map, set multiple undetermined regions based on the feature information, and obtain the power supply demand of the undetermined regions. If the power supply demand is less than the preset power supply demand threshold, then set the corresponding undetermined region as the power supply region. If the power demand exceeds the preset power demand threshold, the corresponding undetermined area will be divided into secondary areas. Obtain historical power data at the branch nodes of the undetermined region that needs to be subdivided, analyze the historical power data, and obtain the historical power supply at the corresponding branch nodes; If the number of branch nodes within the cut-off area is greater than the preset node number threshold or the sum of historical power supply is greater than the preset power supply threshold, then the cut-off area is set as the power supply area. Obtain historical power data at branch nodes within each power supply area, calculate the utilization coefficient and proportion coefficient of historical power data at each branch node within the corresponding power supply area, and calculate the degree of influence of the corresponding branch node on the corresponding power supply area. Branch nodes whose impact exceeds the preset impact level are designated as the main power supply nodes within the corresponding power supply area.

2. The multi-source power grid peak-shaving and frequency regulation system as described in claim 1, characterized in that, Based on the correlation between data acquisition nodes and power supply master nodes, reference data groups corresponding to the power supply master nodes are selected, including: The location information of each power supply master node is set as the origin, and the scanning radius is set according to the importance of the power supply master node in the corresponding power supply area. The scanning area of ​​the corresponding power supply master node is generated based on the origin and scanning radius. Data acquisition nodes within the scanning area are obtained, and the first correlation coefficient between the corresponding data acquisition node and the power supply master node is generated based on the positional relationship between the data acquisition node and the origin. The change characteristics of power operation data of data acquisition nodes within the scanning area are obtained, and the change characteristics are compared with the reference change characteristics at the power supply master node. Based on the comparison results, a second correlation coefficient between the corresponding data acquisition node and the power supply master node is generated. The degree of association is generated based on the first and second association coefficients; ； Where M represents the degree of correlation between the corresponding data acquisition node and the power supply master node, m1 is the first correlation coefficient, e1 is the first weight coefficient, e2 is the second weight coefficient, and m2 is the second correlation coefficient. Data acquisition nodes with a correlation degree greater than a preset correlation degree threshold are set as feature data acquisition nodes of the corresponding power supply master node; The environmental data and power operation data of the feature data acquisition node at the same time point are set as the reference data group of the corresponding power supply master node.

3. The multi-source power grid peak-shaving and frequency regulation system as described in claim 2, characterized in that, The scanning radius is set according to the importance of the power supply master node in the corresponding power supply area, including: Pre-set a first preset importance level, a second preset importance level, and a third preset importance level; When the importance of the power supply master node in the corresponding power supply area is less than the first preset importance, the scanning radius is set to the first preset scanning radius. When the importance of the power supply master node in the corresponding power supply area is between the first preset importance level and the second preset importance level, the scanning radius is set to the second preset scanning radius; When the importance of the power supply master node in the corresponding power supply area is between the second preset importance level and the third preset importance level, the scanning radius is set to the third preset scanning radius; When the importance of the power supply master node in the corresponding power supply area is greater than the third preset importance, the scanning radius is set to the fourth preset scanning radius.

4. The multi-source power grid peak-shaving and frequency regulation system as described in claim 3, characterized in that, Acquire the changing characteristics of power operation data from data acquisition nodes within the scanned area, including: Select data acquisition nodes collect power operation data within a preset time period. The power operation data includes power generation data, power consumption data, and energy data. Based on the power generation data, power consumption data, and energy data of the same data acquisition node within a preset time period, construct power generation data change curves, power consumption data change curves, and energy data change curves; By analyzing the power generation data change curves, electricity consumption data change curves, and energy data change curves, the first change time node, the second change time node, and the third change time node of the power generation data change curves, electricity consumption data change curves, and energy data change curves are obtained. If the time deviations between the first, second, and third change time nodes are not all within the preset deviation range, then the corresponding first, second, and third change time nodes are removed. If the time deviations between the first, second, and third change time nodes are all within the preset deviation range, then the first change value, the second change value, and the third change value at the first, second, and third change time nodes are obtained. Based on the relationship between the first change value, the second change value, and the third change value and the preset change value range, the first change coefficient, the second change coefficient, and the third change coefficient are set respectively; A first change feature is generated based on a first change time point and a first change coefficient; a second change feature is generated based on a second change time point and a second change coefficient; and a third change feature is generated based on a third change time point and a third change coefficient. Acquire power operation data of the power supply master node during a preset time period, and generate a first reference change feature, a second reference change feature, and a third reference change feature; Multiple first change feature difference values ​​are generated based on the first change feature and the corresponding first reference change feature; multiple second change feature difference values ​​are generated based on the second change feature and the corresponding second reference change feature; and multiple third change feature difference values ​​are generated based on the third change feature and the corresponding third reference change feature. A second correlation coefficient is generated based on the difference values ​​of the first change feature, the second change feature, and the third change feature. Where n is the number of time points where the time deviations between the first, second, and third change time points are all within a preset deviation range. For the i-th first change feature difference value, The difference value of the first standard change feature corresponding to the i-th first change feature. For the i-th second change feature difference value, The difference value of the second standard change feature corresponding to the i-th second change feature. For the i-th third change feature difference value, Let a1i be the difference value of the third standard change feature corresponding to the i-th third change feature, a2i be the weight coefficient corresponding to the i-th first change feature, a3i be the weight coefficient corresponding to the i-th second change feature, and a3i be the weight coefficient corresponding to the i-th third change feature.

5. The multi-source power grid peak-shaving and frequency regulation system as described in claim 4, characterized in that, The preferred data set for the corresponding power supply master node is determined based on the environmental condition level and the operational condition level, including: Obtain environmental-related data from the reference data group of the power supply master node, calculate the influence coefficient of the environmental-related data on the power operation data of the power supply master node, and set the environmental-related data with an influence coefficient greater than the preset influence coefficient threshold as environmental impact data; The environmental impact data in each reference data group is compared with the preset standard data range to obtain the environmental impact data deviation value of multiple environmental impact data. The environmental impact data deviation value of the same reference data group is weighted and averaged to obtain the environmental status level of the corresponding reference data group. The reference data sets of the power supply master node are classified according to the environmental condition level to obtain the reference data sets of the same power supply master node at different environmental condition levels. The power operation data in the reference data group of the same environmental condition level is compared with the corresponding preset standard power operation data range to determine the operation status level under the corresponding environmental condition level. Power operation data whose operating status level is greater than the preset operating status level threshold under the same environmental condition level are set as preferred power operation data; The environmental impact data corresponding to multiple environmental status levels, along with the corresponding preferred power operation data, are combined to form the preferred data group for the corresponding power supply master node.

6. The multi-source power grid peak-shaving and frequency regulation system as described in claim 5, characterized in that, Determine the operational status level under the corresponding environmental status level, including: Based on the constraints of stable power grid operation and cost optimization control, the preset standard power operation data intervals under different environmental state levels are divided into multiple preset standard power operation data sub-intervals, and a corresponding operation state level is set for each preset standard power operation data sub-interval. The power operation data under the same environmental condition level is compared with the corresponding preset standard power operation data range to obtain the preset standard power operation data sub-range where the power operation data is located, and the operation status level of the corresponding power operation data is set.

7. The multi-source power grid peak-shaving and frequency regulation system as described in claim 6, characterized in that, Based on the optimal data sets of multiple power supply master nodes in the same power supply area, a corresponding optimal data prediction model for the power supply area is constructed, including: Analyze the preferred power operation data in the preferred data group to obtain the demand factor data and supply factor data under the corresponding environmental state level; Using environmental impact data and demand factor data for each environmental state level as inputs and supply factor data as outputs, a neural network is trained to obtain an optimal data prediction model.

8. The multi-source power grid peak-shaving and frequency regulation system as described in claim 7, characterized in that, Based on the optimized data prediction model, peak shaving and frequency regulation strategies are formulated for the corresponding power supply areas, including: Obtain comprehensive demand factor data for each power supply area, divide the comprehensive demand factor data into multiple sub-comprehensive demand factor data according to the importance ratio coefficient of each power supply master node in the current power supply area, and allocate them to the corresponding power supply master node in the current power supply area; The real-time environmental impact data and the corresponding comprehensive demand factor data at each power supply master node are input into the optimal data prediction model to obtain the predicted supply factor data at the corresponding power supply master node. By comprehensively analyzing the predicted supply factor data at multiple power supply master nodes in the same power supply area, the peak-shaving and frequency regulation strategies for the corresponding power supply area can be determined.

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