Photovoltaic power station power supply scheduling method based on data analysis

Abstract

The invention relates to the technical field of power supply scheduling, in particular to a photovoltaic power station power supply scheduling method based on data analysis. The power supply data of the power station is acquired and analyzed to obtain the scheduling prediction information, the power generation related data is analyzed to obtain the power generation state information, and then the scheduling prediction information and the power generation state information are comprehensively analyzed to obtain the initial scheduling strategy; power supply scheduling of the photovoltaic power station can be realized according to the initial scheduling strategy, and power supply scheduling is facilitated; according to the method, the scheduling optimization information is obtained by analyzing the response action information, then the initial scheduling strategy is optimized according to the scheduling optimization information to obtain the power supply scheduling instruction, and the response state of each device is monitored, so that power supply scheduling can be conveniently scheduled and adjusted according to the real-time response state; and faults caused by abnormal response are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply scheduling, and in particular to a photovoltaic power station power supply scheduling method based on data analysis. Background Art

[0002] Photovoltaic power stations, also known as photovoltaic power stations, are facilities that use solar cell modules to convert light energy into electrical energy; these power stations usually contain a large number of photovoltaic modules, inverters, distribution equipment and possible energy storage systems; photovoltaic power stations can be distributed or centralized, the former are usually installed at the user end, such as rooftops or open spaces, for self-sufficiency or to supply power to the grid; the latter are usually built in open areas, such as deserts or Gobi, for large-scale power generation and input into the grid.

[0003] As the proportion of renewable energy in the energy structure continues to increase, the power supply scheduling of photovoltaic power stations helps to improve their electricity consumption rate. Through effective coordination with the power grid and flexible scheduling strategies, more photovoltaic power generation can be absorbed and utilized by the power grid, reducing the phenomenon of abandoned light and promoting the sustainable development of renewable energy.

[0004] At present, when photovoltaic power stations are conducting power supply scheduling, each device module is required to respond according to the scheduling information. However, most of the device modules are independently controlled. Therefore, there will be a delay in scheduling response, which makes it impossible for photovoltaic output power to keep up with load changes in time. Frequency fluctuations may exceed the allowable range, affecting the normal operation of sensitive equipment in the power grid, such as precision instruments and electronic equipment. Summary of the invention

[0005] The present invention provides a photovoltaic power station power supply scheduling method based on data analysis, which is used to solve the above technical problems.

[0006] A first aspect of the present invention provides a photovoltaic power station power supply scheduling method based on data analysis, comprising:

[0007] Step 1: Obtain the operating data of each equipment module and the power supply data of the photovoltaic power station; collect the corresponding operating data of each equipment module of the photovoltaic power station through sensors, and the operating data includes power generation related data and response action information, among which the power generation related data includes photovoltaic module data, inverter data and energy storage system data, and the response action information includes instruction execution time, response delay and online status; the power supply data includes power generation data, load data, meteorological data and electricity price data.

[0008] Step 2: Obtain the power supply data of the power station and analyze the power supply data to obtain scheduling prediction information.

[0009] As a further improvement of the present invention, the power supply data is analyzed, and the specific analysis method is as follows:

[0010] The power supply data includes power generation data, load data, meteorological data and electricity price data; the power generation data includes power generation power and photovoltaic performance data, obtains a pre-set photovoltaic health performance range, matches the photovoltaic performance data with the photovoltaic health performance range, and generates photovoltaic health information when the photovoltaic performance data is in the photovoltaic health performance range; conversely, when the photovoltaic performance data exceeds the photovoltaic health performance range, generates photovoltaic abnormal information; based on the photovoltaic health information, the corresponding power generation power is obtained, and multiple historical power generation powers of the corresponding date of the power generation power are extracted from the database, the multiple historical power generation powers are calculated to obtain the average value, and the average value is marked as the normal reference power, the power generation power corresponding to the current date is calculated to obtain the power difference with the normal reference power, and the corresponding power adjustment information is obtained according to the power difference.

[0011] Based on the database, load data of different time periods are obtained, and the time periods include working days and weekends. When the corresponding time period is a working day, the peak power consumption corresponds to industrial and commercial activities, and the valley power consumption corresponds to residential power consumption. Conversely, when the corresponding time period is a weekend, the peak power consumption corresponds to residential power consumption and commercial activities, and the valley power consumption corresponds to industrial activities. The power consumption objects corresponding to each time period are marked to obtain the power supply object information corresponding to each time period.

[0012] Meteorological data is identified to obtain light intensity data and duration of illumination. Based on the corresponding daily light intensity data and duration of illumination, power generation forecast information can be obtained, and the power supply efficiency and energy storage capacity of the photovoltaic power station can be obtained. The power supply efficiency and energy storage capacity are comprehensively judged with the power generation forecast information to obtain the corresponding energy supply and storage information. For example, when the power generation forecast information exceeds the power supply efficiency and energy storage capacity, the corresponding electric energy will be mainly supplied to the power grid and the power generation will be reduced.

[0013] The electricity price data is identified to obtain the time-of-use electricity price, which includes peak, flat and valley electricity prices. The duration of each time-of-use electricity price is obtained, and the corresponding change in electricity consumption is obtained. The change in electricity consumption is compared with the preset change threshold. When the corresponding change threshold is exceeded, the corresponding duration is adjusted to obtain the corresponding electricity price-related adjustment. For example, in summer, due to the high demand for air-conditioning electricity, the peak electricity price period may be extended or the electricity price may increase. This can help consider supplying more electricity when the electricity price is high during scheduling, thereby improving economic benefits.

[0014] The power regulation information, power supply object information, energy storage information and electricity price related adjustments are combined to obtain the scheduling prediction information.

[0015] Step 3: Identify the operation data to obtain power generation related data and analyze the power generation related data to obtain power generation status information.

[0016] As a further improvement of the present invention, the power generation related data is analyzed, and the specific analysis method is as follows:

[0017] A1: PV module data, inverter data and energy storage system data are obtained by identifying power generation related data;

[0018] A2: Obtain the sunlight power generation and module temperature according to the photovoltaic module data; divide the sunlight power generation into multiple power generation intervals, each power generation interval corresponds to a power generation capacity value, match the sunlight power generation corresponding to each photovoltaic panel with the multiple power generation intervals to obtain the corresponding power generation capacity value, calculate the power generation capacity value corresponding to each photovoltaic panel to obtain the total power generation capacity value; obtain a pre-set module temperature threshold, compare the module temperature corresponding to each photovoltaic panel with the module temperature threshold, when the module temperature is greater than the module temperature threshold, calculate the difference between the module temperature and the module temperature threshold to obtain a temperature anomaly value, add and sum the temperature anomalies corresponding to each photovoltaic panel to obtain the total temperature anomaly value.

[0019] A3: The inverter output power and input power are obtained according to the inverter data, the output power and the input power are calculated to obtain the power loss value, the conversion efficiency is calculated according to the power loss value, and the preset conversion reference efficiency is obtained. When the conversion efficiency is less than the conversion reference efficiency, the conversion efficiency corresponding to the current inverter is calculated to obtain the conversion abnormality value by the difference between the conversion reference efficiency.

[0020] A4: Obtain energy storage margin and energy storage efficiency based on energy storage system data; obtain a preset minimum margin limit, compare the energy storage margin of each energy storage module with the minimum margin limit, and when the energy storage margin is less than the minimum margin limit, mark the corresponding energy storage module as a full storage module, count the number of full storage modules and record it as the number of full storage modules, and calculate the ratio of the number of full storage modules to the total number of energy storage modules to obtain the full storage ratio.

[0021] A5: Construct two circles with the total temperature anomaly value and the conversion anomaly value as the radius of the circle, with the centers of the two circles as the starting point and the end point, and draw a straight line perpendicular to the two circles. The length of the straight line is equal to the full storage ratio. Then construct a frustum with the two circles and the straight line, and construct a sphere with the midpoint of the straight line as the center and the total power generation capacity as the radius. Cut off the part of the frustum corresponding to the sphere and calculate the volume of the remaining part of the frustum, and mark the value of the volume as the power generation impact value.

[0022] A6: Compare the power generation impact value with a preset power generation impact threshold. When the power generation impact value is greater than the power generation impact threshold, generate corresponding power generation status information that the power generation status is abnormal.

[0023] Step 4: Comprehensively analyze the dispatch prediction information and power generation status information to obtain the initial dispatch strategy, specifically: identify the power generation status information, and when the power generation status information indicates that the power generation status is abnormal, generate the corresponding initial dispatch strategy for abnormality investigation; conversely, when the power generation status information corresponds to a normal power generation status, generate the corresponding initial dispatch strategy for maintaining the original dispatch prediction information.

[0024] Step 5: Identify the operation data to obtain response action information and analyze the response action information to obtain scheduling optimization information.

[0025] As a further improvement of the present invention, the response action information is analyzed, and the specific analysis method is as follows:

[0026] S1: Obtain the instruction execution time, response delay and online status of each device module of the photovoltaic power station by identifying the response action information;

[0027] S2: Based on the historical execution time corresponding to multiple response actions corresponding to each device module extracted from the database, multiple historical execution times are calculated to obtain an average value, and the average value is marked as the execution reference time, and the execution time corresponding to each current device module is obtained. When the execution time is greater than the execution reference time, the execution time is calculated to be different from the corresponding execution reference time to obtain the execution timeout, and the execution timeouts corresponding to each device module are added together to obtain the total execution value, which is recorded as zxy.

[0028] S3: Obtain the response delays corresponding to multiple response actions of the device module, and obtain a pre-set response delay threshold, compare each response delay with the response delay threshold, and when the response delay is greater than the response delay threshold, mark the corresponding device module as a response abnormality module, count the number of response abnormal modules, and record it as the number of abnormal response modules, and record it as ycx.

[0029] S4: Obtain a preset detection time period, identify the online status of each device module corresponding to the detection time period, when the online status corresponds to offline, the reasons for offline include but are not limited to communication failure, equipment failure, environmental impact, and human operation, count the offline duration corresponding to each device module, record the offline duration as offline, and calculate the ratio of the offline duration to the duration corresponding to the detection time period to obtain the offline impact value, which is recorded as dxy.

[0030] S5: Normalize the total execution shadow value, the number of abnormal response modules, and the offline impact value and take their values. Use the formula The response status value XY is calculated; where ycx' represents the allowed number of abnormal response modules; the value of n is a positive integer. It is represented by the sum of the offline impact values ​​corresponding to n detection time periods; p1, p2 and p3 are all preset weight factors, and their values ​​are 2.726, 1.145 and 2.367 respectively.

[0031] S6: When the response state value is greater than a preset response state threshold, a corresponding response state mark is generated as abnormal, and corresponding scheduling optimization information is generated as an instruction adjustment delay.

[0032] Step six: Optimize the initial dispatch strategy according to the dispatch optimization information to obtain the power supply dispatch instruction; identify the dispatch optimization information, and when the response state corresponding to the dispatch optimization information is abnormal, perform dispatch optimization according to the abnormality, and use the optimized dispatch instruction as the power supply dispatch instruction.

[0033] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0034] 1. The present invention obtains the power supply data of the power station and analyzes the power supply data to obtain the scheduling prediction information, and obtains the power generation related data by identifying the operation data and analyzes the power generation related data to obtain the power generation status information, and then obtains the initial scheduling strategy by comprehensively analyzing the scheduling prediction information and the power generation status information. According to the initial scheduling strategy, the power supply scheduling of the photovoltaic power station can be realized, which is convenient for power supply scheduling.

[0035] 2. The present invention obtains response action information by identifying the operating data and analyzes the response action information to obtain scheduling optimization information, then optimizes the initial scheduling strategy according to the scheduling optimization information to obtain power supply scheduling instructions, and monitors the response status of each device to facilitate power supply scheduling and adjustment according to the real-time response status to avoid failures caused by abnormal responses. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.

[0037] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In an embodiment of the present invention, an embodiment of a photovoltaic power station power supply scheduling method based on data analysis includes the following steps:

[0040] Step 1: Obtain the operating data of each equipment module and the power supply data of the photovoltaic power station; collect the corresponding operating data of each equipment module of the photovoltaic power station through sensors, and the operating data includes power generation related data and response action information, among which the power generation related data includes photovoltaic module data, inverter data and energy storage system data, and the response action information includes instruction execution time, response delay and online status; the power supply data includes power generation data, load data, meteorological data and electricity price data.

[0041] Step 2: Obtain the power supply data of the power station and analyze the power supply data to obtain scheduling prediction information; analyze the power supply data, and the specific analysis method is as follows:

[0042] The power supply data includes power generation data, load data, meteorological data and electricity price data; the power generation data includes power generation power and photovoltaic performance data, obtains a pre-set photovoltaic health performance range, matches the photovoltaic performance data with the photovoltaic health performance range, and generates photovoltaic health information when the photovoltaic performance data is in the photovoltaic health performance range; conversely, when the photovoltaic performance data exceeds the photovoltaic health performance range, generates photovoltaic abnormal information; based on the photovoltaic health information, the corresponding power generation power is obtained, and multiple historical power generation powers of the corresponding date of the power generation power are extracted from the database, the multiple historical power generation powers are calculated to obtain the average value, and the average value is marked as the normal reference power, the power generation power corresponding to the current date is calculated to obtain the power difference with the normal reference power, and the corresponding power adjustment information is obtained according to the power difference.

[0043] Based on the database, load data of different time periods are obtained, and the time periods include working days and weekends. When the corresponding time period is a working day, the peak power consumption corresponds to industrial and commercial activities, and the valley power consumption corresponds to residential power consumption. Conversely, when the corresponding time period is a weekend, the peak power consumption corresponds to residential power consumption and commercial activities, and the valley power consumption corresponds to industrial activities. The power consumption objects corresponding to each time period are marked to obtain the power supply object information corresponding to each time period.

[0044] Meteorological data is identified to obtain light intensity data and duration of illumination. Based on the corresponding daily light intensity data and duration of illumination, power generation forecast information can be obtained, and the power supply efficiency and energy storage capacity of the photovoltaic power station can be obtained. The power supply efficiency and energy storage capacity are comprehensively judged with the power generation forecast information to obtain the corresponding energy supply and storage information. For example, when the power generation forecast information exceeds the power supply efficiency and energy storage capacity, the corresponding electric energy will be mainly supplied to the power grid and the power generation will be reduced.

[0045] The electricity price data is identified to obtain the time-of-use electricity price, which includes peak, flat and valley electricity prices. The duration of each time-of-use electricity price is obtained, and the corresponding change in electricity consumption is obtained. The change in electricity consumption is compared with the preset change threshold. When the corresponding change threshold is exceeded, the corresponding duration is adjusted to obtain the corresponding electricity price-related adjustment. For example, in summer, due to the high demand for air-conditioning electricity, the peak electricity price period may be extended or the electricity price may increase. This can help consider supplying more electricity when the electricity price is high during scheduling, thereby improving economic benefits.

[0046] The power regulation information, power supply object information, energy storage information and electricity price related adjustments are combined to obtain the scheduling prediction information.

[0047] Step 3: Identify the operation data to obtain power generation related data and analyze the power generation related data to obtain power generation status information; analyze the power generation related data, and the specific analysis method is as follows:

[0048] A1: PV module data, inverter data and energy storage system data are obtained by identifying power generation related data;

[0049] A2: Obtain the sunlight power generation and module temperature according to the photovoltaic module data; divide the sunlight power generation into multiple power generation intervals, each power generation interval corresponds to a power generation capacity value, match the sunlight power generation corresponding to each photovoltaic panel with the multiple power generation intervals to obtain the corresponding power generation capacity value, calculate the power generation capacity value corresponding to each photovoltaic panel to obtain the total power generation capacity value; obtain a pre-set module temperature threshold, compare the module temperature corresponding to each photovoltaic panel with the module temperature threshold, when the module temperature is greater than the module temperature threshold, calculate the difference between the module temperature and the module temperature threshold to obtain a temperature anomaly value, add and sum the temperature anomalies corresponding to each photovoltaic panel to obtain the total temperature anomaly value.

[0050] A3: The inverter output power and input power are obtained according to the inverter data, the output power and the input power are calculated to obtain the power loss value, the conversion efficiency is calculated according to the power loss value, and the preset conversion reference efficiency is obtained. When the conversion efficiency is less than the conversion reference efficiency, the conversion efficiency corresponding to the current inverter is calculated to obtain the conversion abnormality value by the difference between the conversion reference efficiency.

[0051] A4: Obtain energy storage margin and energy storage efficiency based on energy storage system data; obtain a preset minimum margin limit, compare the energy storage margin of each energy storage module with the minimum margin limit, and when the energy storage margin is less than the minimum margin limit, mark the corresponding energy storage module as a full storage module, count the number of full storage modules and record it as the number of full storage modules, and calculate the ratio of the number of full storage modules to the total number of energy storage modules to obtain the full storage ratio.

[0052] A5: Construct two circles with the total temperature anomaly value and the conversion anomaly value as the radius of the circle, with the centers of the two circles as the starting point and the end point, and draw a straight line perpendicular to the two circles. The length of the straight line is equal to the full storage ratio. Then construct a frustum with the two circles and the straight line, and construct a sphere with the midpoint of the straight line as the center and the total power generation capacity as the radius. Cut off the part of the frustum corresponding to the sphere and calculate the volume of the remaining part of the frustum, and mark the value of the volume as the power generation impact value.

[0053] A6: Compare the power generation impact value with a preset power generation impact threshold. When the power generation impact value is greater than the power generation impact threshold, generate corresponding power generation status information that the power generation status is abnormal.

[0054] Step 4: Comprehensively analyze the dispatch prediction information and power generation status information to obtain the initial dispatch strategy, specifically: identify the power generation status information, and when the power generation status information indicates that the power generation status is abnormal, generate the corresponding initial dispatch strategy for abnormality investigation; conversely, when the power generation status information corresponds to a normal power generation status, generate the corresponding initial dispatch strategy for maintaining the original dispatch prediction information.

[0055] Step 5: Identify the operation data to obtain the response action information and analyze the response action information to obtain the scheduling optimization information; analyze the response action information, and the specific analysis method is as follows:

[0056] S1: Obtain the instruction execution time, response delay and online status of each device module of the photovoltaic power station by identifying the response action information;

[0057] S2: Based on the historical execution time corresponding to multiple response actions corresponding to each device module extracted from the database, multiple historical execution times are calculated to obtain an average value, and the average value is marked as the execution reference time, and the execution time corresponding to each current device module is obtained. When the execution time is greater than the execution reference time, the execution time is calculated to be different from the corresponding execution reference time to obtain the execution timeout, and the execution timeouts corresponding to each device module are added together to obtain the execution total shadow value.

[0058] S3: Obtain the response delays corresponding to multiple response actions of the device module, and obtain a pre-set response delay threshold, compare each response delay with the response delay threshold, and when the response delay is greater than the response delay threshold, mark the corresponding device module as a response abnormality module, count the number of response abnormality modules, and record it as the number of abnormal response modules.

[0059] S4: Obtain a preset detection time period, identify the online status of each device module corresponding to the detection time period, when the online status corresponds to offline, the reasons for offline include but are not limited to communication failure, equipment failure, environmental impact, and human operation, count the offline duration corresponding to each device module, record the offline duration as offline, and calculate the ratio of the offline duration to the duration corresponding to the detection time period to obtain the offline impact value.

[0060] S5: Normalize the total execution shadow value, the number of abnormal response modules, and the offline impact value and take their values. Use the formula The response status value XY is calculated; where zxy, ycx and dxy represent the execution total impact value, the number of abnormal response modules and the offline impact value respectively, and ycx' represents the allowed number of abnormal response modules; the value of n is a positive integer, It is represented by the sum of the offline impact values ​​corresponding to n detection time periods; p1, p2 and p3 are all preset weight factors, and their values ​​are 2.726, 1.145 and 2.367 respectively.

[0061] S6: When the response state value is greater than a preset response state threshold, a corresponding response state mark is generated as abnormal, and corresponding scheduling optimization information is generated as an instruction adjustment delay.

[0062] Step six: Optimize the initial dispatch strategy according to the dispatch optimization information to obtain the power supply dispatch instruction; identify the dispatch optimization information, and when the response state corresponding to the dispatch optimization information is abnormal, perform dispatch optimization according to the abnormality, and use the optimized dispatch instruction as the power supply dispatch instruction.

[0063] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic power station power supply scheduling method based on data analysis, characterized in that: The steps include: Step 1: Obtain the operating data of each equipment module and the power supply data of the photovoltaic power station; Step 2: Obtain the power supply data of the power station and analyze the power supply data to obtain scheduling prediction information; Step 3: Identify the operation data to obtain power generation related data and analyze the power generation related data to obtain power generation status information; Step 4: Comprehensively analyze the dispatch forecast information and power generation status information to obtain the initial dispatch strategy; Step 5: Identify the operation data to obtain response action information and analyze the response action information to obtain scheduling optimization information; Step 6: Optimize the initial dispatch strategy according to the dispatch optimization information to obtain the power supply dispatch instruction.

2. A photovoltaic power station power supply scheduling method based on data analysis according to claim 1, characterized in that: The obtaining of the operating data of each device module and the obtaining of the power supply data of the photovoltaic power station are specifically as follows: the corresponding operating data are obtained by collecting the corresponding operating data of each device module of the photovoltaic power station through sensors, and the operating data include power generation related data and response action information, wherein the power generation related data include photovoltaic module data, inverter data and energy storage system data, and the response action information includes instruction execution time, response delay and online status; the power supply data include power generation data, load data, meteorological data and electricity price data.

3. A photovoltaic power station power supply scheduling method based on data analysis according to claim 1, characterized in that: The specific analysis method of the power supply data is as follows: The power supply data includes power generation data, load data, meteorological data and electricity price data; the power generation data includes power generation power and photovoltaic performance data, obtains a pre-set photovoltaic health performance interval, matches the photovoltaic performance data with the photovoltaic health performance interval, and generates photovoltaic health information when the photovoltaic performance data is in the photovoltaic health performance interval; based on the photovoltaic health information, the corresponding power generation power is obtained, and multiple historical power generation powers of the corresponding date of the power generation power are extracted from the database, the multiple historical power generation powers are calculated to obtain an average value, and the average value is marked as the normal reference power, the power generation power corresponding to the current date is calculated to obtain the power difference with the normal reference power, and the corresponding power adjustment information is obtained according to the power difference. Based on the database, load data of different time periods are obtained. The time periods include working days and weekends. When the corresponding time period is a working day, the electricity consumption object corresponding to the peak value is industrial and commercial activities, and the electricity consumption object corresponding to the valley value is residential electricity consumption; Mark the electricity users corresponding to each time period to obtain the power supply object information corresponding to each time period; Identify meteorological data to obtain light intensity data and duration. Based on the daily light intensity data and duration, we can obtain power generation forecast information, and obtain the power supply efficiency and energy storage capacity of the photovoltaic power station. Combine the power supply efficiency and energy storage capacity with the power generation forecast information to obtain the corresponding energy supply and storage information. The electricity price data is identified to obtain the time-of-use electricity price, which includes peak, flat and valley electricity prices. The duration of each time-of-use electricity price and the corresponding change in electricity consumption are obtained, and the change in electricity consumption is compared with the preset change threshold. When the corresponding change threshold is exceeded, the corresponding duration is adjusted to obtain the corresponding electricity price-related adjustment. The power adjustment information, power supply object information, energy storage information and electricity price-related adjustment are combined to obtain the scheduling forecast information.

4. A photovoltaic power station power supply scheduling method based on data analysis according to claim 1, characterized in that: The specific analysis method of the power generation related data is as follows: A1: PV module data, inverter data and energy storage system data are obtained by identifying power generation related data; A2: Obtain the sunlight power generation and module temperature according to the photovoltaic module data; divide the sunlight power generation into multiple power generation intervals, each power generation interval corresponds to a power generation capacity value, match the sunlight power generation corresponding to each photovoltaic panel with the multiple power generation intervals to obtain the corresponding power generation capacity value, calculate the power generation capacity value corresponding to each photovoltaic panel to obtain the total power generation capacity value; obtain a pre-set module temperature threshold, compare the module temperature corresponding to each photovoltaic panel with the module temperature threshold, when the module temperature is greater than the module temperature threshold, calculate the difference between the module temperature and the module temperature threshold to obtain a temperature anomaly value, add and sum the temperature anomalies corresponding to each photovoltaic panel to obtain the total temperature anomaly value; A3: Obtain the inverter output power and input power according to the inverter data, calculate the difference between the output power and the input power to obtain the power loss value, calculate the conversion efficiency according to the power loss value, and obtain the preset conversion reference efficiency. When the conversion efficiency is less than the conversion reference efficiency, calculate the difference between the conversion efficiency corresponding to the current inverter and the conversion reference efficiency to obtain the conversion abnormality value; A4: Obtain energy storage margin and energy storage efficiency based on energy storage system data; obtain a preset minimum margin limit, compare the energy storage margin of each energy storage module with the minimum margin limit, and when the energy storage margin is less than the minimum margin limit, mark the corresponding energy storage module as a full storage module, count the number of full storage modules and record it as the number of full storage modules, and calculate the ratio of the number of full storage modules to the total number of energy storage modules to obtain the full storage ratio; A5: Use the total temperature anomaly value and the conversion anomaly value as the radius of the circle to construct two circles respectively, use the centers of the two circles as the starting point and the end point, draw a straight line perpendicular to the two circles, and the length of the straight line is equal to the full storage ratio. Then use the two circles and the straight line to construct a frustum, use the midpoint of the straight line as the center and the total power generation capacity as the radius to construct a sphere, cut off the part of the frustum corresponding to the sphere and calculate the volume of the remaining part of the frustum, and mark the value of the volume as the power generation impact value; A6: Compare the power generation impact value with a preset power generation impact threshold. When the power generation impact value is greater than the power generation impact threshold, generate corresponding power generation status information that the power generation status is abnormal.

5. A photovoltaic power station power supply scheduling method based on data analysis according to claim 1, characterized in that: The comprehensive analysis of the scheduling prediction information and the power generation status information to obtain the initial scheduling strategy is specifically: the power generation status information is identified, and when the power generation status information indicates that the power generation status is abnormal, a corresponding initial scheduling strategy is generated for abnormality troubleshooting; conversely, when the power generation status information corresponds to a normal power generation status, a corresponding initial scheduling strategy is generated for maintaining the original scheduling prediction information.

6. A photovoltaic power station power supply scheduling method based on data analysis according to claim 1, characterized in that: The specific analysis method of analyzing the response action information is as follows: S1: Obtain the instruction execution time, response delay and online status of each device module of the photovoltaic power station by identifying the response action information; S2: extracting historical execution times corresponding to multiple response actions corresponding to each device module from the database, calculating the average value of the multiple historical execution times, and marking the average value as the execution reference time, obtaining the execution time corresponding to each current device module, and when the execution time is greater than the execution reference time, calculating the difference between the execution time and the corresponding execution reference time to obtain the execution timeout, and adding up the execution timeouts corresponding to each device module to obtain the execution total shadow value; S3: Obtain response delays corresponding to multiple response actions of the device module, and obtain a preset response delay threshold, compare each response delay with the response delay threshold, and when the response delay is greater than the response delay threshold, mark the corresponding device module as a response abnormal module, count the number of response abnormal modules, and record it as the number of abnormal response modules; S4: Obtain a preset detection time period, identify the online state of each device module corresponding to the detection time period, and when the online state corresponds to offline, record the duration corresponding to offline as offline, and calculate the ratio of offline duration to duration corresponding to the detection time period to obtain the offline impact value; S5: Comprehensively calculate the total execution shadow value, the number of abnormal response modules and the offline impact value to obtain a response status value; S6: When the response state value is greater than a preset response state threshold, a corresponding response state mark is generated as abnormal, and corresponding scheduling optimization information is generated as an instruction adjustment delay.

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