Multi-source power grid peak regulation and frequency modulation system

Abstract

The invention discloses a multi-source power grid peak and frequency regulation system, and the system comprises a construction module which is used for constructing a multi-source power grid structure reference diagram; the screening module is used for screening out a reference data group corresponding to the power supply main node based on the association degree of the data acquisition node and the power supply main node; the determination module is used for generating an environment state grade according to the environment related data in the reference data set, generating an operation state grade according to the power operation data in the reference data set, and determining a preferred data set corresponding to the power supply main node according to the environment state grade and the operation state grade; and the formulating module is used for constructing an optimal data prediction model of the corresponding power supply area based on the optimal data groups of the plurality of power supply main nodes in the same power supply area, formulating a peak regulation and frequency modulation strategy of the corresponding power supply area based on the optimal data prediction model, and improving the operation stability and the electric energy quality of the multi-source power grid on the premise of ensuring that the power demand is met.

Description

Technical Field

[0001] The present application relates to the technical field of power system dispatching, and in particular to a multi-source power grid peak and frequency regulation system. Background Art

[0002] With the rapid development of economy and the improvement of people's living standards, the demand for electricity is also increasing, and the fluctuation of power load is getting bigger and bigger. Therefore, it is particularly important to ensure the safe and stable operation of power system. Grid peak and frequency regulation refers to balancing the input or output power of the grid with the load change by adjusting the operation mode, load or compensation, so as to achieve the purpose of ensuring the energy security and economic operation of the system.

[0003] In the existing technology, peak-shaving and frequency regulation mostly rely on manual scheduling modes. With the addition of various new energy sources, manual scheduling modes and traditional automatic scheduling can no longer fully meet the power demand of the power grid 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 reasonably arrange power supply strategies according to the actual needs of multi-source power grids. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a multi-source power grid peak-shaving and frequency-regulating system, constructs a multi-source power grid structure reference map, and screens out a reference data group corresponding to the power supply main node according to the degree of association between the data acquisition node and the power supply main node in the multi-source power grid structure reference map. The preferred data group of the power supply main node is screened out according to the environmental status level and operating status level of the reference data group, and a preferred data prediction model for the corresponding power supply area is constructed, so as to obtain the peak-shaving and frequency-regulating strategy for each power supply area, thereby improving the operating stability and power quality of the multi-source power grid while ensuring that the power demand is met.

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

[0006] A 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 map based on the geographic location information, wherein the multi-source power grid structure reference map includes a plurality of data acquisition nodes and a plurality of power supply areas, wherein the power supply area includes a plurality of power supply main nodes;

[0007] A screening module, used to screen out a reference data group corresponding to the power supply main node based on the degree of association between the data acquisition node and the power supply main node;

[0008] A determination module, used to generate an environmental status level according to the environment-related data in the reference data group, generate an operation status level according to the power operation data in the reference data group, and determine a preferred data group corresponding to the power supply main node according to the environmental status level and the operation status level;

[0009] A formulation module is used to construct a preferred data prediction model for the corresponding power supply area based on the preferred data groups of multiple power supply main nodes in the same power supply area, and formulate a peak and frequency regulation strategy for the corresponding power supply area based on the preferred data prediction model.

[0010] In some embodiments of the present application, constructing a multi-source power grid structure reference diagram includes:

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

[0012] Acquire multiple regional feature information in the multi-source power grid basic map, set multiple pending areas according to the regional feature information, and obtain the power supply demand of the pending areas. If the power supply demand is less than a preset power supply demand threshold, set the corresponding pending area as the power supply area;

[0013] If the power demand is greater than the preset power demand threshold, the corresponding undetermined area is divided twice;

[0014] Obtain historical power data at the branch nodes of the pending area to be divided twice, analyze the historical power data, and obtain the historical power supply at the corresponding branch nodes;

[0015] The undetermined area to be divided twice is intercepted. If the number of branch nodes in the intercepted area is greater than the preset node number threshold or the sum of historical power supply is greater than the preset power supply threshold, the undetermined area in the intercepted area is set as the power supply area;

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

[0017] A branch node whose influence degree is greater than a preset influence degree is set as a main power supply node in the corresponding power supply area.

[0018] In some embodiments of the present application, based on the degree of association between the data acquisition node and the power supply main node, a reference data group corresponding to the power supply main node is screened out, including:

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

[0020] Generate a scanning area corresponding to the supply main node according to the origin and the scanning radius, obtain a data acquisition node in the scanning area, and generate a first correlation coefficient between the corresponding data acquisition node and the power supply main node according to the positional relationship between the data acquisition node and the origin;

[0021] Acquire the change characteristics of the power operation data of the data acquisition node in the scanning area, compare the change characteristics with the reference change characteristics at the power supply main node, and generate a second correlation coefficient between the corresponding data acquisition node and the power supply main node according to the comparison result;

[0022] generating a correlation degree according to the first correlation coefficient and the second correlation coefficient;

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

[0024] Among them, M is the correlation degree between the corresponding data acquisition node and the power supply main 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] The data collection node whose correlation degree is greater than a preset correlation degree threshold is set as a characteristic data collection node corresponding to the power supply main node;

[0026] The environment-related data and power operation data of the characteristic data collection node at the same time node are set as the reference data group corresponding to the power supply main node.

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

[0028] Presetting a first preset importance, a second preset importance, and a third preset importance;

[0029] When the importance of the main power supply 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 and the second preset importance, 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 and the third preset importance, the scanning radius is set to the third preset scanning radius;

[0032] When the importance of the main power supply 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 the present application, obtaining the change characteristics of the power operation data of the data collection nodes in the scanning area includes:

[0034] The power operation data of the selected data collection node within a preset period of time, wherein the power operation data includes power generation data, power consumption data and energy data;

[0035] Constructing a power generation data change curve, a power consumption data change curve and an energy data change curve according to the power generation data, power consumption data and energy data of the same data collection node within a preset time period;

[0036] Analyze the power generation data change curve, the power consumption data change curve, and the energy data change curve to obtain a first change time node, a second change time node, and a third time change node of the power generation data change curve, the power consumption data change curve, and the energy data change curve;

[0037] If the change time deviations among the first change time node, the second change time node and the third change time node are not all within the preset deviation range, then the corresponding first change time node, the second change time node and the third change time node are removed;

[0038] If 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, then obtaining the first change value, the second change value and the third change value at the first change time node, the second change time node and the third change time node;

[0039] According to the relationship between the first change value, the second change value, the third change value and the preset change value interval, respectively set the first change coefficient, the second change coefficient and the third change coefficient;

[0040] Generate a first change feature according to the first change time node and the first change coefficient, generate a second change feature according to the second change time node and the second change coefficient, and generate a third change feature according to the third change time node and the third change coefficient;

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

[0042] Generate a plurality of first change feature difference values ​​according to the first change feature and the corresponding first reference change feature, generate a plurality of second change feature difference values ​​according to the second change feature and the corresponding second reference change feature, and generate a plurality of third change feature difference values ​​according to the third change feature and the corresponding third reference change feature;

[0043] generating a second correlation coefficient according to the first change characteristic difference value, the second change characteristic difference value, and the third change difference value;

[0044]

[0045] Among them, n is the number of time nodes whose 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, △p1i is the i-th first change feature difference value, p01i is the first standard change feature difference value corresponding to the i-th first change feature, △p2i is the i-th second change feature difference value, p02i is the second standard change feature difference value corresponding to the i-th second change feature, △p3i is the i-th third change feature difference value, p03i is the third standard change feature difference value 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 the present application, determining a preferred data group corresponding to a power supply master node according to an environmental status level and an operating status level includes:

[0047] Obtaining environment-related data in a reference data group of the power supply main node, calculating an influence coefficient of the environment-related data on the power operation data of the power supply main node, and setting the environment-related data whose influence coefficient is greater than a preset influence coefficient threshold as the environment influence data;

[0048] Compare the environmental impact data in each reference data group with the preset standard data interval to obtain environmental impact data deviation values ​​of multiple environmental impact data, and perform weighted average processing on the environmental impact data deviation values ​​of the same reference data group to obtain the environmental status level of the corresponding reference data group;

[0049] Classifying the reference data groups of the power supply main node according to the environmental status level, and obtaining reference data groups of the same power supply main node at different environmental status levels;

[0050] Compare the power operation data in the reference data group of the same environmental state level with the corresponding preset standard power operation data interval to determine the operation state level corresponding to the environmental state level;

[0051] Setting the power operation data whose operation state level is greater than the preset operation state level threshold value at the same environmental state level as the preferred power operation data;

[0052] The environmental impact data corresponding to the multiple environmental status levels and the corresponding preferred power operation data are combined into a preferred data group corresponding to the power supply main node.

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

[0054] Based on the constraints of the stable operation state of the power grid and cost optimization control, the preset standard power operation data interval of different environmental state levels is 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-area;

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

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

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

[0058] The environmental impact data of each environmental status level and the demand factor data are used as input items, and the supply factor data is used as output items, and neural network learning and training are carried out to obtain the optimal data prediction model.

[0059] In some embodiments of the present application, based on the optimal data prediction model, a peak load and frequency regulation strategy for the corresponding power supply area is formulated, 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 important proportion coefficient of each power supply main node in the current power supply area, and distribute them to the corresponding power supply main nodes in the current power supply area;

[0061] Input the real-time environmental impact data and the corresponding comprehensive demand factor data at each power supply main node into the optimal data prediction model to obtain the predicted supply factor data at the corresponding power supply main node;

[0062] Comprehensively analyze the predicted supply factor data at multiple power supply main nodes in the same power supply area to determine the peak and frequency regulation strategies for the corresponding power supply area.

[0063] Compared with the prior art, the multi-source power grid peak-shaving and frequency-regulating system of the embodiment of the present application has the following beneficial effects:

[0064] A multi-source power grid structure reference graph is constructed. According to the degree of association between the data acquisition nodes and the main power supply nodes in the multi-source power grid structure reference graph, the reference data group corresponding to the main power supply node is screened out. According to the environmental status level and operating status level of the reference data group, the preferred data group of the main power supply node is screened out, and a preferred data prediction model for the corresponding power supply area is constructed, so as to obtain the peak-shaving and frequency-regulating strategies for each power supply area, thereby improving the operating stability and power quality of the multi-source power grid while ensuring that the power demand is met. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of a multi-source power grid peak and frequency regulation system in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0066] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0067] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

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

[0069] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

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

[0071] A 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 map based on the geographic location information, wherein the multi-source power grid structure reference map includes a plurality of data acquisition nodes and a plurality of power supply areas, wherein the power supply area includes a plurality of power supply main nodes;

[0072] A screening module, used to screen out a reference data group corresponding to the power supply main node based on the degree of association between the data acquisition node and the power supply main node;

[0073] A determination module, used to generate an environmental status level according to the environment-related data in the reference data group, generate an operation status level according to the power operation data in the reference data group, and determine a preferred data group corresponding to the power supply main node according to the environmental status level and the operation status level;

[0074] A formulation module is used to construct a preferred data prediction model for the corresponding power supply area based on the preferred data groups of multiple power supply main nodes in the same power supply area, and formulate a peak and frequency regulation strategy for the corresponding power supply area based on the preferred data prediction model.

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

[0076] In this embodiment, the reference data group refers to the environment-related data and power operation data at the data collection node whose correlation degree with the power supply main node is greater than the preset correlation degree threshold, and the preferred data group refers to the preferred power operation data and corresponding environmental image data whose operating status level under different environmental status levels at each power supply main node is greater than the preset operating status level threshold.

[0077] In this embodiment, a multi-source power grid structure reference graph is constructed, and according to the degree of association between the data acquisition node and the power supply main node in the multi-source power grid structure reference graph, a reference data group corresponding to the power supply main node is screened out, and a preferred data group of the power supply main node is screened out according to the environmental status level and the operating status level of the reference data group, and a preferred data prediction model for the corresponding power supply area is constructed, so as to obtain the peak-shaving and frequency-regulating strategies for each power supply area, thereby improving the operating stability and power quality of the multi-source power grid while ensuring that the power demand is met.

[0078] In some embodiments of the present application, constructing a multi-source power grid structure reference diagram includes:

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

[0080] Acquire multiple regional feature information in the multi-source power grid basic map, set multiple pending areas according to the regional feature information, and obtain the power supply demand of the pending areas. If the power supply demand is less than a preset power supply demand threshold, set the corresponding pending area as the power supply area;

[0081] If the power demand is greater than the preset power demand threshold, the corresponding undetermined area is divided twice;

[0082] Obtain historical power data at the branch nodes of the pending area to be divided twice, analyze the historical power data, and obtain the historical power supply at the corresponding branch nodes;

[0083] The undetermined area to be divided twice is intercepted. If the number of branch nodes in the intercepted area is greater than the preset node number threshold or the sum of historical power supply is greater than the preset power supply threshold, the undetermined area in the intercepted area is set as the power supply area;

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

[0085] A branch node whose influence degree is greater than a preset influence degree is set as a main power supply node in the corresponding power supply area.

[0086] In this embodiment, the regional characteristic information refers to the geographical characteristics, meteorological data, etc. of multiple regions. The multi-source power grid basic map is divided into multiple pending areas based on the regional characteristic information, laying the foundation for the subsequent formulation of reasonable peak-shaving and frequency-regulating 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 pending area. When the power supply demand is greater than the preset power supply demand threshold, it means that the power demand of the current pending area is large and complex, and the corresponding pending area should be divided twice to improve the power supply stability of each power supply area.

[0088] In this embodiment, the pending areas to be divided twice are scanned and analyzed to obtain the branch node locations of the lines in each pending area and the corresponding historical power data. The historical power data includes power supply data, power demand data, etc. The pending areas are divided twice according to the number of branch nodes and the sum of the power supply, so that the power adjustment of the corresponding areas is simplified.

[0089] In this embodiment, the utilization coefficient refers to the participation ratio of the corresponding branch node in the corresponding power supply area and the degree of data utilization. The data utilization degree includes complete data utilization and random data utilization. If the participation ratio is large and the data utilization degree is large, the utilization coefficient is large, otherwise the utilization coefficient is small. 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 the present application, based on the degree of association between the data acquisition node and the power supply main node, a reference data group corresponding to the power supply main node is screened out, including:

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

[0092] Generate a scanning area corresponding to the supply main node according to the origin and the scanning radius, obtain a data acquisition node in the scanning area, and generate a first correlation coefficient between the corresponding data acquisition node and the power supply main node according to the positional relationship between the data acquisition node and the origin;

[0093] Acquire the change characteristics of the power operation data of the data acquisition node in the scanning area, compare the change characteristics with the reference change characteristics at the power supply main node, and generate a second correlation coefficient between the corresponding data acquisition node and the power supply main node according to the comparison result;

[0094] generating a correlation degree according to the first correlation coefficient and the second correlation coefficient;

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

[0096] Among them, M is the correlation degree between the corresponding data acquisition node and the power supply main 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] The data collection node whose correlation degree is greater than a preset correlation degree threshold is set as a characteristic data collection node corresponding to the power supply main node;

[0098] The environment-related data and power operation data of the characteristic data collection node at the same time node are set as the reference data group corresponding to the power supply main node.

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

[0100] Presetting a first preset importance, a second preset importance, and a third preset importance;

[0101] When the importance of the main power supply 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 and the second preset importance, 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 and the third preset importance, the scanning radius is set to the third preset scanning radius;

[0104] When the importance of the main power supply 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 second preset scanning radius<the third preset scanning radius<the fourth preset scanning radius.

[0106] In some embodiments of the present application, obtaining the change characteristics of the power operation data of the data collection nodes in the scanning area includes:

[0107] The power operation data of the selected data collection node within a preset period of time, wherein the power operation data includes power generation data, power consumption data and energy data;

[0108] Constructing a power generation data change curve, a power consumption data change curve and an energy data change curve according to the power generation data, power consumption data and energy data of the same data collection node within a preset time period;

[0109] Analyze the power generation data change curve, the power consumption data change curve, and the energy data change curve to obtain a first change time node, a second change time node, and a third time change node of the power generation data change curve, the power consumption data change curve, and the energy data change curve;

[0110] If the change time deviations among the first change time node, the second change time node and the third change time node are not all within the preset deviation range, then the corresponding first change time node, the second change time node and the third change time node are removed;

[0111] If 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, then obtaining the first change value, the second change value and the third change value at the first change time node, the second change time node and the third change time node;

[0112] According to the relationship between the first change value, the second change value, the third change value and the preset change value interval, respectively set the first change coefficient, the second change coefficient and the third change coefficient;

[0113] Generate a first change feature according to the first change time node and the first change coefficient, generate a second change feature according to the second change time node and the second change coefficient, and generate a third change feature according to the third change time node and the third change coefficient;

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

[0115] Generate a plurality of first change feature difference values ​​according to the first change feature and the corresponding first reference change feature, generate a plurality of second change feature difference values ​​according to the second change feature and the corresponding second reference change feature, and generate a plurality of third change feature difference values ​​according to the third change feature and the corresponding third reference change feature;

[0116] generating a second correlation coefficient according to the first change characteristic difference value, the second change characteristic difference value, and the third change difference value;

[0117]

[0118] Among them, n is the number of time nodes whose 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, △p1i is the i-th first change feature difference value, p01i is the first standard change feature difference value corresponding to the i-th first change feature, △p2i is the i-th second change feature difference value, p02i is the second standard change feature difference value corresponding to the i-th second change feature, △p3i is the i-th third change feature difference value, p03i is the third standard change feature difference value 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, the power generation data involves the power generation, power generation capacity and power generation efficiency at the corresponding data collection node, the power consumption data involves the power consumption, power load, power peak and valley values, etc. of the corresponding data collection node, and the energy data involves the energy consumption, energy conversion efficiency, etc. of the corresponding data collection node.

[0120] In this embodiment, the change time node refers to the time node at which 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 node is the synchronous change node of the power operation data. By comparing the change characteristics of the synchronous change node with the supply main node, the second correlation coefficient of the corresponding data acquisition node and the supply main node is accurately obtained, laying a data foundation for the subsequent determination of the preferred data group of the corresponding supply main node, ensuring the accuracy of the predicted power operation data of the supply main node, thereby ensuring the effectiveness of the peak-shaving and frequency regulation strategy of the multi-source power grid, and improving the operating stability and efficiency of the multi-source power grid.

[0121] In some embodiments of the present application, determining a preferred data group corresponding to a power supply master node according to an environmental status level and an operating status level includes:

[0122] Obtaining environment-related data in a reference data group of the power supply main node, calculating an influence coefficient of the environment-related data on the power operation data of the power supply main node, and setting the environment-related data whose influence coefficient is greater than a preset influence coefficient threshold as the environment influence data;

[0123] Compare the environmental impact data in each reference data group with the preset standard data interval to obtain environmental impact data deviation values ​​of multiple environmental impact data, and perform weighted average processing on the environmental impact data deviation values ​​of the same reference data group to obtain the environmental status level of the corresponding reference data group;

[0124] Classifying the reference data groups of the power supply main node according to the environmental status level, and obtaining reference data groups of the same power supply main node at different environmental status levels;

[0125] Compare the power operation data in the reference data group of the same environmental state level with the corresponding preset standard power operation data interval to determine the operation state level corresponding to the environmental state level;

[0126] Setting the power operation data whose operation state level is greater than the preset operation state level threshold value at the same environmental state level as the preferred power operation data;

[0127] The environmental impact data corresponding to the multiple environmental status levels and the corresponding preferred power operation data are combined into a preferred data group corresponding to the power supply main node.

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

[0129] In this embodiment, the environmental impact data deviation values ​​of the same reference data group are weighted averaged. The smaller the weighted average, the greater the environmental status level, and vice versa. The larger the environmental status level, the more beneficial the corresponding environmental impact data is to the power supply of the multi-source power grid. The larger the operating status level of the power operation data, the more beneficial the power supply of the corresponding power supply main node is to the demand and the operation is stable.

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

[0131] Based on the constraints of the stable operation state of the power grid and cost optimization control, the preset standard power operation data interval of different environmental state levels is 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-area;

[0132] The power operation data of the same environmental status level is compared with the corresponding preset standard power operation data interval to obtain the preset standard power operation data sub-interval in which 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 benefits based on the annual investment cost of the multi-source power grid, fuel cost, operation and maintenance cost, environmental cost, and interaction cost with other power grids.

[0134] In this embodiment, based on the grid operation stability and cost control optimization as constraint conditions, the preset standard power operation data interval 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 the present application, based on the preferred data groups of multiple power supply master nodes in the same power supply area, a preferred data prediction model for the corresponding power supply area is constructed, including:

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

[0137] The environmental impact data of each environmental status level and the demand factor data are used as input items, and the supply factor data is used as output items, and neural network learning and training are carried out to obtain the optimal data prediction model.

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

[0139] In this embodiment, the demand factor data and supply factor data of multiple power supply main nodes in each power supply area at different environmental status levels are used to construct a preferred data prediction model, so as to improve the accuracy of the predicted power operation data, and determine the peak and frequency regulation strategy of the corresponding power supply area according to the predicted power operation data at each power supply main node at different environmental status levels to ensure the power supply stability and efficiency of the corresponding power supply area, thereby improving the operating stability and power quality of the multi-source power grid.

[0140] In some embodiments of the present application, based on the optimal data prediction model, a peak load and frequency regulation strategy for the corresponding power supply area is formulated, 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 important proportion coefficient of each power supply main node in the current power supply area, and distribute them to the corresponding power supply main nodes in the current power supply area;

[0142] Input the real-time environmental impact data and the corresponding comprehensive demand factor data at each power supply main node into the optimal data prediction model to obtain the predicted supply factor data at the corresponding power supply main node;

[0143] Comprehensively analyze the predicted supply factor data at multiple power supply main nodes in the same power supply area to determine the peak and frequency regulation strategies for the corresponding power supply area.

[0144] In this embodiment, the important proportional coefficient is set according to the historical supply volume and historical power supply stability of the main power supply node. The comprehensive demand factor data is divided according to the important proportional coefficient to obtain sub-comprehensive demand factor data of each main power supply node. The sub-comprehensive demand factor data and the real-time environmental image data at the corresponding main power supply node are input into the preferred data prediction model to obtain predicted supply factor data. The predicted supply factor data of multiple main power supply nodes in the same power supply area are comprehensively analyzed to formulate reasonable peak-shaving and frequency regulation strategies, so as to achieve the optimization of the power system regulation plan under the premise of ensuring the power demand of the multi-source power grid, thereby improving the operating stability and power quality of the multi-source power grid.

[0145] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A multi-source power grid peak load and frequency modulation system, characterized in that: include: A 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 map based on the geographic location information, wherein the multi-source power grid structure reference map includes a plurality of data acquisition nodes and a plurality of power supply areas, wherein the power supply area includes a plurality of power supply main nodes; A screening module, used to screen out a reference data group corresponding to the power supply main node based on the degree of association between the data acquisition node and the power supply main node; A determination module, used to generate an environmental status level according to the environment-related data in the reference data group, generate an operation status level according to the power operation data in the reference data group, and determine a preferred data group corresponding to the power supply main node according to the environmental status level and the operation status level; A formulation module is used to construct a preferred data prediction model for the corresponding power supply area based on the preferred data groups of multiple power supply main nodes in the same power supply area, and formulate a peak and frequency regulation strategy for the corresponding power supply area based on the preferred data prediction model.

2. The multi-source power grid peak load and frequency modulation system according to claim 1, characterized in that: Construct a multi-source grid structure reference diagram, including: Obtain the composition structure and connection relationship of the multi-source power grid, and generate a basic map of the multi-source power grid based on the map location information; Acquire multiple regional feature information in the multi-source power grid basic map, set multiple pending areas according to the regional feature information, and obtain the power supply demand of the pending areas. If the power supply demand is less than a preset power supply demand threshold, set the corresponding pending area as the power supply area; If the power demand is greater than the preset power demand threshold, the corresponding undetermined area is divided twice; Obtain historical power data at the branch nodes of the pending area to be divided twice, analyze the historical power data, and obtain the historical power supply at the corresponding branch nodes; The undetermined area to be divided twice is intercepted. If the number of branch nodes in the intercepted area is greater than the preset node number threshold or the sum of historical power supply is greater than the preset power supply threshold, the undetermined area in the intercepted area is set as the power supply area; Obtain historical power data at the branch nodes in each power supply area, calculate the utilization coefficient and proportion coefficient of the historical power data at each branch node in the corresponding power supply area, and calculate the influence of the corresponding branch node on the corresponding power supply area; A branch node whose influence degree is greater than a preset influence degree is set as a main power supply node in the corresponding power supply area.

3. The multi-source power grid peak load and frequency modulation system according to claim 2, characterized in that: Based on the degree of association between the data collection node and the main power supply node, the reference data group corresponding to the main power supply node is screened out, including: The location information of each power supply main node is set as the origin, and the scanning radius is set according to the importance of the power supply main node in the corresponding power supply area; Generate a scanning area corresponding to the supply main node according to the origin and the scanning radius, obtain a data acquisition node in the scanning area, and generate a first correlation coefficient between the corresponding data acquisition node and the power supply main node according to the positional relationship between the data acquisition node and the origin; Acquire the change characteristics of the power operation data of the data acquisition node in the scanning area, compare the change characteristics with the reference change characteristics at the power supply main node, and generate a second correlation coefficient between the corresponding data acquisition node and the power supply main node according to the comparison result; generating a correlation degree according to the first correlation coefficient and the second correlation coefficient; M = m1*e1+m2*e2; Among them, M is the correlation degree between the corresponding data acquisition node and the power supply main 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. The data collection node whose correlation degree is greater than a preset correlation degree threshold is set as a characteristic data collection node corresponding to the power supply main node; The environment-related data and power operation data of the characteristic data collection node at the same time node are set as the reference data group corresponding to the power supply main node.

4. The multi-source power grid peak load and frequency modulation system according to claim 3, characterized in that: The scanning radius is set according to the importance of the main power supply node in the corresponding power supply area, including: Presetting a first preset importance, a second preset importance, and a third preset importance; When the importance of the main power supply 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 and the second preset importance, 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 and the third preset importance, the scanning radius is set to the third preset scanning radius; When the importance of the main power supply 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.

5. The multi-source power grid peak load and frequency modulation system according to claim 4, characterized in that: Obtain the change characteristics of the power operation data of the data collection nodes in the scan area, including: The power operation data of the selected data collection node within a preset period of time, wherein the power operation data includes power generation data, power consumption data and energy data; Constructing a power generation data change curve, a power consumption data change curve and an energy data change curve according to the power generation data, power consumption data and energy data of the same data collection node within a preset time period; Analyze the power generation data change curve, the power consumption data change curve, and the energy data change curve to obtain a first change time node, a second change time node, and a third time change node of the power generation data change curve, the power consumption data change curve, and the energy data change curve; If the change time deviations among the first change time node, the second change time node and the third change time node are not all within the preset deviation range, then the corresponding first change time node, the second change time node and the third change time node are removed; If 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, then obtaining the first change value, the second change value and the third change value at the first change time node, the second change time node and the third change time node; According to the relationship between the first change value, the second change value, the third change value and the preset change value interval, respectively set the first change coefficient, the second change coefficient and the third change coefficient; Generate a first change feature according to the first change time node and the first change coefficient, generate a second change feature according to the second change time node and the second change coefficient, and generate a third change feature according to the third change time node and the third change coefficient; Acquire power operation data of the power supply main node in a preset period of time, and generate a first reference change feature, a second reference change feature, and a third reference change feature; Generate a plurality of first change feature difference values ​​according to the first change feature and the corresponding first reference change feature, generate a plurality of second change feature difference values ​​according to the second change feature and the corresponding second reference change feature, and generate a plurality of third change feature difference values ​​according to the third change feature and the corresponding third reference change feature; generating a second correlation coefficient according to the first change characteristic difference value, the second change characteristic difference value, and the third change difference value; Among them, n is the number of time nodes whose 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, △p1i is the i-th first change feature difference value, p01i is the first standard change feature difference value corresponding to the i-th first change feature, △p2i is the i-th second change feature difference value, p02i is the second standard change feature difference value corresponding to the i-th second change feature, △p3i is the i-th third change feature difference value, p03i is the third standard change feature difference value 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.

6. The multi-source power grid peak load and frequency modulation system according to claim 5, characterized in that: The preferred data group corresponding to the power supply main node is determined according to the environmental status level and the operating status level, including: Obtaining environment-related data in a reference data group of the power supply main node, calculating an influence coefficient of the environment-related data on the power operation data of the power supply main node, and setting the environment-related data whose influence coefficient is greater than a preset influence coefficient threshold as the environment influence data; Compare the environmental impact data in each reference data group with the preset standard data interval to obtain environmental impact data deviation values ​​of multiple environmental impact data, and perform weighted average processing on the environmental impact data deviation values ​​of the same reference data group to obtain the environmental status level of the corresponding reference data group; Classifying the reference data groups of the power supply main node according to the environmental status level, and obtaining reference data groups of the same power supply main node at different environmental status levels; Compare the power operation data in the reference data group of the same environmental state level with the corresponding preset standard power operation data interval to determine the operation state level under the corresponding environmental state level; Setting the power operation data whose operation state level is greater than the preset operation state level threshold value at the same environmental state level as the preferred power operation data; The environmental impact data corresponding to the multiple environmental status levels and the corresponding preferred power operation data are combined into a preferred data group corresponding to the power supply main node.

7. The multi-source power grid peak load and frequency modulation system according to claim 6, characterized in that: Determine the operating status level under the corresponding environmental status level, including: Based on the constraints of the stable operation state of the power grid and cost optimization control, the preset standard power operation data interval under different environmental state levels is 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-area; The power operation data under the same environmental status level is compared with the corresponding preset standard power operation data interval to obtain the preset standard power operation data sub-interval in which the power operation data is located, and the operation status level of the corresponding power operation data is set.

8. The multi-source power grid peak load and frequency modulation system according to claim 7, characterized in that: Based on the preferred data groups of multiple power supply main nodes in the same power supply area, a preferred data prediction model for the corresponding power supply area is constructed, including: Analyze the preferred power operation data in the preferred data group to obtain demand factor data and supply factor data under corresponding environmental status levels; The environmental impact data of each environmental status level and the demand factor data are used as input items, and the supply factor data is used as output items, and neural network learning and training are carried out to obtain the optimal data prediction model.

9. The multi-source power grid peak load and frequency modulation system according to claim 8, characterized in that: Based on the optimal data prediction model, formulate peak load and frequency regulation strategies for the corresponding power supply area, 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 important proportion coefficient of each power supply main node in the current power supply area, and distribute them to the corresponding power supply main nodes in the current power supply area; Input the real-time environmental impact data and the corresponding comprehensive demand factor data at each power supply main node into the optimal data prediction model to obtain the predicted supply factor data at the corresponding power supply main node; Comprehensively analyze the predicted supply factor data at multiple power supply main nodes in the same power supply area to determine the peak and frequency regulation strategies for the corresponding power supply area.

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