New energy power generation intelligent regulation and control method

Abstract

The invention discloses a new energy power generation intelligent regulation and control method, and relates to the technical field of new energy power generation regulation and control, and the method comprises the following steps: monitoring meteorological data and comprehensive data in a power grid; predicting a future estimated power grid load; calculating a fluctuation parameter between the estimated value and the actual value; acquiring a power supply plan of the power bureau, correcting and estimating a power grid load based on fluctuation parameters, and analyzing and calculating the final estimated generating capacity of the new energy power station; allocating different generating capacity tasks to different new energy power plants based on the estimated generating capacity; the method is used for solving the problems that the energy utilization rate of the new energy power station is insufficient and the load of part of power generation equipment is relatively large due to inaccurate regulation and control of different new energy power stations in the existing new energy power generation regulation and control technology.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation control, and in particular to a renewable energy power generation intelligent control method. Background Art

[0002] New energy power generation regulation technology refers to the operation management and control technology for renewable energy (such as solar energy, wind energy, hydropower, etc.) power generation systems. It aims to solve the challenges brought about by the volatility, intermittency and uncontrollability of new energy power generation systems, and ensure the safe and stable operation of the power grid.

[0003] Existing renewable energy power generation control technology usually predicts the load of the power grid, so as to allocate power generation tasks for renewable energy power stations. Since electric energy is not easy to store, a lot of electric energy generated by renewable energy power stations will be wasted. Allocating power generation tasks according to the prediction results can greatly reduce energy waste. However, existing renewable energy power generation control technology usually only performs overall control on renewable energy electric energy, and there are many types of renewable energy electric energy, such as photovoltaic electric energy and wind power electric energy. Although the renewable energy power station is generally regulated, the electric energy generated by different renewable energy electric energy under the influence of the environment is also different, and balanced control cannot be achieved. For example, in a patent application with publication number CN114285034A, a day-ahead control optimization method and system considering power receiving and renewable energy fluctuations are disclosed. The scheme only considers the overall renewable energy electric energy, and does not consider the difference in power generation of different renewable energy electric energy under the influence of the environment, resulting in inaccurate control of renewable energy electric energy. The existing renewable energy power generation control technology also has the problem of inaccurate control of different renewable energy power stations, resulting in insufficient energy utilization of renewable energy power stations and heavy load on some power generation equipment. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by monitoring meteorological data and comprehensive data in the power grid through sensors, analyzing historical meteorological data and comprehensive data, predicting the future estimated power grid load, and then calculating the fluctuation parameters between the estimated value and the actual value based on the analysis results obtained by analyzing the historical meteorological data and comprehensive data, and then obtaining the power supply plan of the power bureau, correcting the estimated power grid load based on the fluctuation parameters, analyzing and calculating the final estimated power generation of the new energy power station, and finally allocating different power generation tasks to different new energy power plants based on the estimated power generation.

[0005] To achieve the above objectives, in a first aspect, the present application provides a method for intelligently controlling new energy power generation, comprising the following steps:

[0006] Monitor meteorological data and comprehensive data within the power grid through sensors;

[0007] Analyze historical meteorological data and comprehensive data to predict future estimated grid load;

[0008] Based on the analysis results obtained by analyzing historical meteorological data and comprehensive data, the fluctuation parameters between the estimated value and the actual value are calculated;

[0009] Obtain the power supply plan of the power bureau, estimate the grid load based on the fluctuation parameters, and analyze and calculate the final estimated power generation of the new energy power station;

[0010] Different power generation tasks are assigned to different renewable energy power plants based on the estimated power generation.

[0011] Furthermore, monitoring the meteorological data and the comprehensive data in the power grid through sensors includes the following sub-steps:

[0012] The meteorological data includes air temperature, solar radiation and wind intensity, wherein the air temperature is the average temperature of the environment on that day, the solar radiation is the average total solar radiation in a day, and the wind intensity is the average wind speed in a day;

[0013] The comprehensive data includes grid load, which is the total amount of electric power taken from the grid by residents on that day.

[0014] Furthermore, the historical meteorological data and comprehensive data are analyzed to predict the future estimated grid load, including the following sub-steps:

[0015] Establish a historical load record database and collect the temperature and grid load on different dates;

[0016] Analyze and predict future estimated grid load based on historical load record database.

[0017] Furthermore, establishing a historical load record database and collecting the temperature and grid load on different dates includes the following sub-steps:

[0018] When obtaining the temperature and grid load, whether the current date is a working day or a holiday is recorded simultaneously;

[0019] Establish a historical load record database to record the temperature and grid load of each day on weekdays or holidays in history.

[0020] Furthermore, analyzing and predicting the future estimated grid load based on the historical load record database includes the following sub-steps:

[0021] With the temperature as the X-axis and the power grid load as the Y-axis, two plane rectangular coordinate systems are established, which are named as the weekday load forecast scatter plot and the holiday load forecast scatter plot respectively;

[0022] The temperature and the corresponding grid load on weekdays in the historical load record database are entered into the load forecast scatter plot for weekdays; the temperature and the corresponding grid load on holidays in the historical load record database are entered into the load forecast scatter plot for holidays;

[0023] Polynomial regression is performed on the workday load forecast scatter plot and the holiday load forecast scatter plot to obtain the workday load forecast function and the holiday load forecast function respectively.

[0024] Furthermore, based on the analysis results obtained by analyzing the historical meteorological data and the comprehensive data, calculating the fluctuation parameter between the estimated value and the actual value includes the following sub-steps:

[0025] Based on the workday load forecast function and the holiday load forecast function, the initial working parameters and the initial holiday parameters are obtained through analysis and calculation;

[0026] Further analysis is performed on the initial working parameters and the initial holiday parameters to obtain working fluctuation parameters and holiday fluctuation parameters.

[0027] Furthermore, based on the workday load prediction function and the holiday load prediction function, analyzing and calculating the work initial parameters and the holiday initial parameters includes the following sub-steps:

[0028] Read the historical load record database, name the data in the working day category as working data, and name the data in the holiday category as holiday data;

[0029] Get the temperature in the working data, mark it as the working temperature, substitute the working temperature into the working day load prediction function, and mark the calculation result as the working prediction load; get the temperature in the holiday data, mark it as the holiday temperature, substitute the holiday temperature into the holiday load prediction function, and mark the calculation result as the holiday prediction load;

[0030] The maximum value of the grid load corresponding to the working temperature is marked as the working load, and the maximum value of the grid load corresponding to the holiday temperature is marked as the holiday load;

[0031] Calculate WL / WFL and mark the calculation result as the initial working parameter, where WL is the workload and WFL is the predicted workload; calculate HL / HFL and mark the calculation result as the initial holiday parameter, where HL is the holiday load and HFL is the predicted holiday load;

[0032] Calculate the initial working parameters and initial holiday parameters corresponding to different working temperatures and holiday temperatures.

[0033] Furthermore, further analyzing the initial working parameters and the initial holiday parameters to obtain the working fluctuation parameters and the holiday fluctuation parameters includes the following sub-steps:

[0034] Get the severe high temperature threshold and the severe low temperature threshold;

[0035] Eliminate the working initial parameters whose working temperature is greater than or equal to the severe high temperature threshold or less than or equal to the severe low temperature threshold;

[0036] Eliminate the holiday initial parameters when the holiday temperature is greater than or equal to the severe high temperature threshold or less than or equal to the severe low temperature threshold;

[0037] Find the maximum value among the remaining work initial parameters and mark it as the work fluctuation parameter; find the maximum value among the remaining holiday initial parameters and mark it as the holiday fluctuation parameter;

[0038] The work fluctuation parameters and holiday fluctuation parameters are collectively referred to as fluctuation parameters.

[0039] Furthermore, obtaining the power supply plan of the power bureau, estimating the grid load based on the fluctuation parameter correction, and analyzing and calculating the final estimated power generation of the new energy power station include the following sub-steps:

[0040] Obtaining a power supply plan from the power bureau, wherein the power supply plan includes a traditional power supply ratio and a new energy power supply ratio;

[0041] Get tomorrow's temperature and mark it as the predicted temperature; get whether tomorrow is a workday or a holiday. If it is a workday, output a work prediction signal; if it is a holiday, output a holiday prediction signal;

[0042] If the working forecast signal is output, the forecast temperature is substituted into the working day load forecast function for calculation to obtain the estimated grid load, and the estimated grid load is multiplied by the working fluctuation parameter to obtain the predicted grid load;

[0043] If a holiday prediction signal is output, the predicted temperature is substituted into the holiday load prediction function for calculation to obtain the estimated grid load, and the estimated grid load is multiplied by the holiday fluctuation parameter to obtain the predicted grid load;

[0044] Multiply the predicted grid load by the renewable energy supply ratio to get the estimated power generation.

[0045] Furthermore, allocating different power generation tasks to different renewable energy power plants based on the estimated power generation includes the following sub-steps:

[0046] Obtain tomorrow's solar radiation and wind intensity, connect to the equipment specification database of the solar power plant, and obtain the total battery area, photoelectric conversion efficiency, and total efficiency of the photovoltaic power station system;

[0047] Through the formula PS = Ios × Tba × Pce × η1, where PS is the solar power generation, Ios is the solar radiation, Tba is the total area of ​​the battery, Pce is the photoelectric conversion efficiency, and η1 is the total efficiency of the photovoltaic power station system;

[0048] Connect to the wind farm equipment specification database to obtain air density, power factor, rotor area and generator conversion efficiency;

[0049] By the formula PW = 0.5 × ρ × V 3 ×A×Cp×η2×86400, where PW is wind power generation, ρ is air density, V is wind intensity, A is rotor area, Cp is power coefficient, and η2 is generator conversion efficiency;

[0050] Calculate PS / (PS+PW) and PW / (PS+PW), and mark the results as solar power generation ratio and wind power generation ratio respectively;

[0051] Multiply the estimated power generation by the solar power generation ratio and the wind power generation ratio respectively to obtain the solar power demand and wind power demand. The power generation required by the solar power plant tomorrow is the solar power demand, and the power generation required by the wind power plant is the wind power demand.

[0052] Beneficial effects of the present invention: The present invention monitors meteorological data and comprehensive data in the power grid through sensors, and then analyzes historical meteorological data and comprehensive data to predict the estimated power grid load in the future. The advantage is that temperature is the biggest factor affecting residents' electricity consumption, and under the condition of a large base, the influence of other factors on electricity consumption is relatively small. Therefore, the total electricity consumption of residents in a certain period of time in the future can be predicted through historical temperature and power grid load. The power grid load here is not the electricity demand of residents at a certain moment, but the sum of the electricity used by residents in a day, which improves the accuracy and effectiveness of the regulation of new energy power generation;

[0053] The present invention calculates the fluctuation parameter between the estimated value and the actual value based on the analysis result obtained by analyzing the historical meteorological data and comprehensive data, obtains the power supply plan of the power bureau, corrects the estimated grid load based on the fluctuation parameter, analyzes and calculates the final estimated power generation of the new energy power station, and finally allocates different power generation tasks to different new energy power plants based on the estimated power generation. The advantage is that since the estimated grid load is a predicted value, a certain error needs to be considered, and it can be expanded based on the fluctuation parameter to avoid insufficient power generation. Finally, the power generation of the solar power plant and the wind power plant is predicted based on the meteorological data to obtain the solar power generation and the wind power generation. Finally, the power generation tasks are allocated based on their ratio, so that the task loads of the two different new energy power stations can reach a certain balance, preventing the task volume of one party from being too large and the task volume of the other party from being too small, thereby improving the accuracy and rationality of the regulation and control of new energy power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the steps of the method of the present invention;

[0055] Figure 2 It is a scatter plot of the working day load forecast of the present invention;

[0056] Figure 3 It is a scatter plot of holiday load prediction of the present invention;

[0057] Figure 4 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0058] 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.

[0059] Example 1, please refer to Figure 1 As shown, the present application provides a method for intelligently controlling new energy power generation, comprising the following steps:

[0060] Step S1, monitoring meteorological data and comprehensive data in the power grid through sensors; Step S1 includes the following sub-steps:

[0061] Step S101, meteorological data includes temperature, solar radiation and wind strength, where the temperature is the average temperature of the environment on that day, the solar radiation is the total amount of solar radiation in a day, and the wind strength is the average wind speed in a day;

[0062] Step S102, the comprehensive data includes the grid load, which is the total amount of electric power taken from the grid by residents on that day;

[0063] In specific implementation, meteorological data can be obtained through data released by the Meteorological Bureau, and the grid load is the sum of the electric power drawn from the grid by all residents in the power supply area under the responsibility of the power bureau on that day.

[0064] Step S2, analyzing historical meteorological data and comprehensive data to predict future estimated grid load; Step S2 includes the following sub-steps:

[0065] Step S201, establishing a historical load record database and collecting temperatures and grid loads on different dates;

[0066] Step S201 includes the following sub-steps:

[0067] Step S2011, when obtaining the temperature and the grid load, synchronously record whether the current date is a working day or a holiday;

[0068] Step S2012, establishing a historical load record database, and recording the temperature and grid load of each day on working days or holidays in history through the historical load record database;

[0069] In the specific implementation, some data of the historical load record database is shown in Table 1 below:

[0070] Table 1 Partial data of historical load record database

[0071]

[0072] Step S202, analyzing and predicting the future estimated grid load based on the historical load record database;

[0073] Step S202 includes the following sub-steps:

[0074] See also Figures 2 to 3 As shown, in step S2021, two plane rectangular coordinate systems are established with the temperature as the X-axis and the power grid load as the Y-axis, and are named as the weekday load forecast scatter plot and the holiday load forecast scatter plot respectively;

[0075] Step S2022, entering the temperature and corresponding grid load of the working day in the historical load record database into the working day load forecast scatter plot; entering the temperature and corresponding grid load of the holiday in the historical load record database into the holiday load forecast scatter plot;

[0076] Step S2023, performing polynomial regression on the workday load prediction scatter plot and the holiday load prediction scatter plot, respectively, to obtain a workday load prediction function and a holiday load prediction function;

[0077] In the specific implementation, the data is constructed and input to obtain the workday load forecast scatter plot as shown in the figure. Figure 2 As shown, construct and enter data to obtain the holiday load forecast scatter plot as shown Figure 3 As shown; the working day load forecast function obtained by polynomial regression is YW = 10.125 × XW 2 -330.41×XW+10290, the holiday load forecast function is YH=10.345×XH 2 -335.25×XH+11452, where YW is the grid load on weekdays, XW is the temperature on weekdays, YH is the power load on holidays, and XH is the temperature on holidays.

[0078] Step S3, based on the analysis results obtained by analyzing the historical meteorological data and the comprehensive data, calculate the fluctuation parameter between the estimated value and the actual value; Step S3 includes the following sub-steps:

[0079] Step S301, based on the workday load prediction function and the holiday load prediction function, analyze and calculate to obtain the work initial parameters and the holiday initial parameters;

[0080] Step S301 includes the following sub-steps:

[0081] Step S3011, read the historical load record database, name the data in the working day category as working data, and name the data in the holiday category as holiday data;

[0082] Step S3012, obtain the temperature in the working data, mark it as the working temperature, substitute the working temperature into the working day load prediction function, and mark the calculation result as the working predicted load; obtain the temperature in the holiday data, mark it as the holiday temperature, substitute the holiday temperature into the holiday load prediction function, and mark the calculation result as the holiday predicted load;

[0083] Step S3013, marking the maximum value of the grid load corresponding to the working temperature as the working load, and marking the maximum value of the grid load corresponding to the holiday temperature as the holiday load;

[0084] Step S3014, calculate WL / WFL, and mark the calculation result as the work initial parameter, where WL is the workload and WFL is the workload forecast load; calculate HL / HFL, and mark the calculation result as the holiday initial parameter, where HL is the holiday load and HFL is the holiday forecast load;

[0085] Step S3015, calculating the initial working parameters and initial holiday parameters corresponding to different working temperatures and holiday temperatures;

[0086] In the specific implementation, based on Table 1, the working temperature is obtained to be 34°C, and YW=10.125×XW is substituted 2 -330.41×XW+10290, the calculated work forecast load WFL is 10761kWh, the calculation result is rounded to an integer, the holiday temperature is 34℃, substitute into YH=10.345×XH 2 -335.25×XH+11452, the holiday forecast load HFL is calculated to be 12012kWh; Figure 2 In the coordinate point with X axis as 34, the maximum value of Y axis is 10837, that is, the workload WL is 10837kWh. Similarly, the holiday load HL is 12201kWh. The calculated working initial parameters are 1.007063 and the holiday initial parameters are 1.015734. The calculation results are rounded to six decimal places.

[0087] Step S302, further analyzing the work initial parameters and the holiday initial parameters to obtain the work fluctuation parameters and the holiday fluctuation parameters;

[0088] Step S302 includes the following sub-steps:

[0089] Step S3021, obtaining a severe high temperature threshold and a severe low temperature threshold;

[0090] Step S3022, removing the working initial parameters whose working temperature is greater than or equal to the severe high temperature threshold or less than or equal to the severe low temperature threshold;

[0091] Step S3023, removing the holiday initial parameters whose holiday temperature is greater than or equal to the severe high temperature threshold or less than or equal to the severe low temperature threshold;

[0092] Step S3024, finding the maximum value among the remaining work initial parameters, marking it as the work fluctuation parameter, finding the maximum value among the remaining holiday initial parameters, marking it as the holiday fluctuation parameter;

[0093] Step S3025, the work fluctuation parameter and the holiday fluctuation parameter are collectively referred to as fluctuation parameters;

[0094] In specific implementation, high temperature and low temperature weather will greatly increase residents' electricity consumption, but the probability of this happening is usually small and varies from region to region, which is not applicable to daily life. Therefore, it is necessary to eliminate their influence and obtain the severe high temperature threshold and the severe low temperature threshold of 35℃ and -10℃ respectively. Figure 2 as well as Figure 3There are no working temperatures or holiday temperatures that are greater than or equal to the severe high temperature threshold or less than or equal to the severe low temperature threshold, so all working initial parameters and holiday initial parameters are retained; the working fluctuation parameter is found to be 1.182597 and the holiday fluctuation parameter is 1.154239.

[0095] Step S4, obtaining the power supply plan of the power bureau, correcting the estimated power grid load based on the fluctuation parameters, and analyzing and calculating the final estimated power generation of the new energy power station; Step S4 includes the following sub-steps:

[0096] Step S401, obtaining a power supply plan from the power bureau, the power supply plan including a traditional power supply ratio and a new energy power supply ratio, that is, the ratio of traditional power to new energy power input into the power grid;

[0097] Step S402, obtaining tomorrow's temperature and marking it as the predicted temperature; obtaining whether tomorrow is a working day or a holiday, if it is a working day, outputting a working prediction signal; if it is a holiday, outputting a holiday prediction signal;

[0098] Step S403: if a working prediction signal is output, the predicted temperature is substituted into the working day load prediction function for calculation to obtain the estimated grid load, and the estimated grid load is multiplied by the working fluctuation parameter to obtain the predicted grid load;

[0099] Step S404, if a holiday prediction signal is output, the predicted temperature is substituted into the holiday load prediction function for calculation to obtain an estimated grid load, and the estimated grid load is multiplied by the holiday fluctuation parameter to obtain a predicted grid load;

[0100] Step S405, multiplying the predicted grid load by the renewable energy power supply ratio to obtain an estimated power generation;

[0101] In the specific implementation, the traditional power supply ratio is 60%, the new energy power supply ratio is 40%, the temperature tomorrow is 33℃, and tomorrow is a working day, the work prediction signal is output, and XW=33 is substituted into YW=10.125×XW 2 -330.41×XW+10290, the estimated grid load is calculated to be 10413kWh. The calculation result is rounded to an integer, and the predicted grid load is further calculated to be 12314kWh. The predicted grid load is then multiplied by the renewable energy power supply ratio to obtain the estimated power generation, and the estimated power generation is calculated to be 4926kWh.

[0102] Step S5, allocating different power generation tasks to different renewable energy power plants based on the estimated power generation; Step S5 includes the following sub-steps:

[0103] Step S501, obtaining tomorrow's solar radiation and wind intensity, connecting to the equipment specification database of the solar power plant, obtaining the total battery area, photoelectric conversion efficiency and total efficiency of the photovoltaic power station system;

[0104] Step S502, by the formula PS=Ios×Tba×Pce×η1, where PS is the solar power generation, Ios is the solar radiation, Tba is the total area of ​​the battery, Pce is the photoelectric conversion efficiency, and η1 is the total efficiency of the photovoltaic power station system;

[0105] In the specific implementation, the solar radiation is the average value of the solar radiation energy received per square meter of surface area in a day. The solar radiation is 15.22 J / ㎡, the wind strength is 4 m / s, the total battery area is 5000 ㎡, the photoelectric conversion efficiency is 17.5%, and the total efficiency of the photovoltaic power station system is 0.28. The calculated solar power generation PS is 3729 kWh;

[0106] Step S503, connecting to the equipment specification database of the wind power plant to obtain air density, power coefficient, wind rotor area and conversion efficiency of the generator;

[0107] Step S504: PW = 0.5 × ρ × V 3 ×A×Cp×η2×86400, where PW is wind power generation, ρ is air density, V is wind intensity, A is rotor area, Cp is power coefficient, and η2 is generator conversion efficiency;

[0108] In the specific implementation, the air density is obtained to be 1.128kg / m 3 , the power coefficient is 0.3, the rotor area is 50㎡, the wind strength is 4m / s, the conversion efficiency is 0.22, the constant 86400 is the number of seconds in a day, and the wind power generation PW is calculated to be 10291692Wh, which is converted to kWh as 10291.692kWh, and the integer is retained as 10292kWh;

[0109] Step S505, calculating PS / (PS+PW) and PW / (PS+PW), and marking the calculation results as solar power generation ratio and wind power generation ratio respectively;

[0110] Step S506, multiplying the estimated power generation by the solar power generation ratio and the wind power generation ratio respectively to obtain the solar power generation requirement and the wind power generation requirement. The power generation required by the solar power plant tomorrow is the solar power generation requirement, and the power generation required by the wind power plant is the wind power generation requirement.

[0111] In the specific implementation, the calculated solar power generation ratio and wind power generation ratio are 27% and 73% respectively; the estimated power generation is 4926kWh, and the calculated solar power generation demand and wind power generation demand are 1330kWh and 3596kWh respectively. The power generation required by the solar power plant the next day is 1330kWh, and the power generation required by the wind power plant is 3596kWh. Since the amount of electricity that can be generated by the solar power plant and the wind power plant is greater than the amount of electricity that needs to be produced, it is possible to choose to reduce the load of equipment in the new energy power plant, or to perform maintenance and repairs on some equipment, which will not affect daily use and can provide electricity to surrounding cities.

[0112] Example 2, please refer to Figure 4 As shown, Figure 4 The structural schematic diagram of an electronic device is illustrated, and the electronic device may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a new energy power generation intelligent control method are executed to achieve the following functions: monitoring meteorological data and comprehensive data in the power grid; predicting the future estimated power grid load; calculating the fluctuation parameters between the estimated value and the actual value; obtaining the power supply plan of the power bureau, correcting the estimated power grid load based on the fluctuation parameters, analyzing and calculating the final estimated power generation of the new energy power station; and allocating different power generation tasks to different new energy power plants based on the estimated power generation.

[0113] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0114] Embodiment 3, the present application also provides a computer program product, the computer program product includes a computer program stored on a computer-readable storage medium, the computer program includes program instructions, when the program instructions are executed by the computer, the computer can execute a new energy power generation intelligent control method provided by the above methods, the method includes: monitoring meteorological data and comprehensive data in the power grid; predicting future estimated power grid load; calculating the fluctuation parameters between the estimated value and the actual value; obtaining the power supply plan of the power bureau, correcting the estimated power grid load based on the fluctuation parameters, analyzing and calculating the final estimated power generation of the new energy power station; based on the estimated power generation, assigning different power generation tasks to different new energy power plants.

[0115] Embodiment 4, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned intelligent control method for new energy power generation are executed to achieve the following functions: monitor meteorological data and comprehensive data in the power grid; predict future estimated power grid load; calculate fluctuation parameters between the estimated value and the actual value; obtain the power supply plan of the power bureau, correct the estimated power grid load based on the fluctuation parameters, analyze and calculate the final estimated power generation of the new energy power station; assign different power generation tasks to different new energy power plants based on the estimated power generation.

[0116] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0117] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 application.

Claims

1. A method for intelligent control of renewable energy power generation, characterized in that: The steps include: Monitor meteorological data and comprehensive data within the power grid through sensors; Analyze historical meteorological data and comprehensive data to predict future estimated grid load; Based on the analysis results obtained by analyzing historical meteorological data and comprehensive data, the fluctuation parameters between the estimated value and the actual value are calculated; Obtain the power supply plan of the power bureau, estimate the grid load based on the fluctuation parameters, and analyze and calculate the final estimated power generation of the new energy power station; Different power generation tasks are assigned to different renewable energy power plants based on the estimated power generation.

2. The intelligent control method for renewable energy power generation according to claim 1, characterized in that: Monitoring meteorological data and comprehensive data in the power grid through sensors includes the following sub-steps: The meteorological data includes air temperature, solar radiation and wind intensity, wherein the air temperature is the average temperature of the environment on that day, the solar radiation is the average total solar radiation in a day, and the wind intensity is the average wind speed in a day; The comprehensive data includes grid load, which is the total amount of electric power taken from the grid by residents on that day.

3. The intelligent control method for renewable energy power generation according to claim 2 is characterized in that: Analyzing historical meteorological data and comprehensive data to predict future estimated grid load includes the following sub-steps: Establish a historical load record database and collect the temperature and grid load on different dates; Analyze and predict future estimated grid load based on historical load record database.

4. The intelligent control method for renewable energy power generation according to claim 3 is characterized in that: Establishing a historical load record database and collecting the temperature and grid load on different dates includes the following sub-steps: When obtaining the temperature and grid load, whether the current date is a working day or a holiday is recorded simultaneously; Establish a historical load record database to record the temperature and grid load of each day on weekdays or holidays in history.

5. The intelligent control method for renewable energy power generation according to claim 4, characterized in that: Analyzing and predicting the future estimated grid load based on the historical load record database includes the following sub-steps: With the temperature as the X-axis and the power grid load as the Y-axis, two plane rectangular coordinate systems are established, which are named as the weekday load forecast scatter plot and the holiday load forecast scatter plot respectively; The temperature and the corresponding grid load on weekdays in the historical load record database are entered into the load forecast scatter plot for weekdays; the temperature and the corresponding grid load on holidays in the historical load record database are entered into the load forecast scatter plot for holidays; Polynomial regression is performed on the workday load forecast scatter plot and the holiday load forecast scatter plot to obtain the workday load forecast function and the holiday load forecast function respectively.

6. A new energy power generation intelligent control method according to claim 5, characterized in that: Based on the analysis results obtained by analyzing historical meteorological data and comprehensive data, the calculation of the fluctuation parameters between the estimated value and the actual value includes the following sub-steps: Based on the workday load forecast function and the holiday load forecast function, the initial working parameters and the initial holiday parameters are obtained through analysis and calculation; Further analysis is performed on the initial working parameters and the initial holiday parameters to obtain working fluctuation parameters and holiday fluctuation parameters.

7. A new energy power generation intelligent control method according to claim 6, characterized in that: Based on the workday load forecast function and the holiday load forecast function, analyzing and calculating the work initial parameters and the holiday initial parameters includes the following sub-steps: Read the historical load record database, name the data in the working day category as working data, and name the data in the holiday category as holiday data; Get the temperature in the working data, mark it as the working temperature, substitute the working temperature into the working day load prediction function, and mark the calculation result as the working prediction load; get the temperature in the holiday data, mark it as the holiday temperature, substitute the holiday temperature into the holiday load prediction function, and mark the calculation result as the holiday prediction load; The maximum value of the grid load corresponding to the working temperature is marked as the working load, and the maximum value of the grid load corresponding to the holiday temperature is marked as the holiday load; Calculate WL / WFL and mark the calculation result as the initial working parameter, where WL is the workload and WFL is the predicted workload; calculate HL / HFL and mark the calculation result as the initial holiday parameter, where HL is the holiday load and HFL is the predicted holiday load; Calculate the initial working parameters and initial holiday parameters corresponding to different working temperatures and holiday temperatures.

8. The intelligent control method for renewable energy power generation according to claim 7, characterized in that: Further analyzing the initial working parameters and the initial holiday parameters to obtain the working fluctuation parameters and the holiday fluctuation parameters includes the following sub-steps: Get the severe high temperature threshold and the severe low temperature threshold; Eliminate the working initial parameters whose working temperature is greater than or equal to the severe high temperature threshold or less than or equal to the severe low temperature threshold; Eliminate the holiday initial parameters when the holiday temperature is greater than or equal to the severe high temperature threshold or less than or equal to the severe low temperature threshold; Find the maximum value among the remaining work initial parameters and mark it as the work fluctuation parameter; find the maximum value among the remaining holiday initial parameters and mark it as the holiday fluctuation parameter; The work fluctuation parameters and holiday fluctuation parameters are collectively referred to as fluctuation parameters.

9. The intelligent control method for renewable energy power generation according to claim 8, characterized in that: Obtaining the power supply plan of the power bureau, correcting the estimated grid load based on the fluctuation parameters, and analyzing and calculating the final estimated power generation of the new energy power station include the following sub-steps: Obtaining a power supply plan from the power bureau, wherein the power supply plan includes a traditional power supply ratio and a new energy power supply ratio; Get tomorrow's temperature and mark it as the predicted temperature; get whether tomorrow is a workday or a holiday. If it is a workday, output a work prediction signal; if it is a holiday, output a holiday prediction signal; If the working forecast signal is output, the forecast temperature is substituted into the working day load forecast function for calculation to obtain the estimated grid load, and the estimated grid load is multiplied by the working fluctuation parameter to obtain the predicted grid load; If a holiday prediction signal is output, the predicted temperature is substituted into the holiday load prediction function for calculation to obtain the estimated grid load, and the estimated grid load is multiplied by the holiday fluctuation parameter to obtain the predicted grid load; Multiply the predicted grid load by the renewable energy supply ratio to get the estimated power generation.

10. A new energy power generation intelligent control method according to claim 9, characterized in that: Allocating different power generation tasks to different renewable energy power plants based on the estimated power generation includes the following sub-steps: Obtain tomorrow's solar radiation and wind intensity, connect to the equipment specification database of the solar power plant, and obtain the total battery area, photoelectric conversion efficiency, and total efficiency of the photovoltaic power station system; Through the formula PS = Ios × Tba × Pce × η1, where PS is the solar power generation, Ios is the solar radiation, Tba is the total area of ​​the battery, Pce is the photoelectric conversion efficiency, and η1 is the total efficiency of the photovoltaic power station system; Connect to the wind farm equipment specification database to obtain air density, power factor, rotor area and generator conversion efficiency; By the formula PW = 0.5 × ρ × V 3 ×A×Cp×η2×86400, where PW is wind power generation, ρ is air density, V is wind intensity, A is rotor area, Cp is power coefficient, and η2 is generator conversion efficiency; Calculate PS / (PS+PW) and PW / (PS+PW), and mark the results as solar power generation ratio and wind power generation ratio respectively; Multiply the estimated power generation by the solar power generation ratio and the wind power generation ratio respectively to obtain the solar power demand and wind power demand. The power generation required by the solar power plant tomorrow is the solar power demand, and the power generation required by the wind power plant is the wind power demand.

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