Power frequency modulation control operation data acquisition system and prediction method

Abstract

The invention discloses an electric power frequency modulation control operation data acquisition system. The system comprises an environment acquisition module used for acquiring environment information, a power station acquisition module used for acquiring power generation information of a power station, a processing module used for processing the information and a storage module used for storing the information. The invention further comprises a power frequency modulation control operation data acquisition and prediction method which comprises the steps of collecting environment data acquired at an installation site, obtaining simulated environment information according to a set time frequency, and calculating to obtain simulated power generation information according to the simulated environment information and photovoltaic or wind power installation parameters. And the simulation environment information and the simulation power generation information are stored to obtain a simulation acquisition file. By collecting the data of power frequency modulation operation, the power generation frequency of solar energy and wind energy can be accurately measured, so that the power grid stability is improved, the power generation scheduling is optimized, the standby capacity is reduced, the utilization rate of renewable energy sources is improved, the system reliability is enhanced, and the operation safety of a power station is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and prediction in the new energy frequency modulation process, and particularly relates to a power frequency modulation control operation data acquisition system and a prediction method. Background Art

[0002] Vigorously developing clean energies such as solar energy, wind energy, and water storage potential energy is of great significance to the structural safety of energy and environmental protection. It can enrich the energy structure and improve the diversity of energy. In the northwest region of our country, there are abundant high-quality wind energy and solar energy resources. Moreover, under the complete power transmission conditions of the west-to-east power transmission, it has unique development conditions. Therefore, the development of solar energy and wind energy is also one of the key industries in the northwest.

[0003] Different from thermal power and hydropower, thermal power and hydropower can stably control the power generation by controlling the steam or water flow rate, while wind energy and solar energy lack such conditions; and the power generation of wind energy and solar energy fluctuates greatly with the changes of wind speed and light intensity, increasing the difficulty of frequency modulation and grid dispatching.

[0004] Currently, there are two ways to deal with solar energy. The first is to establish large-scale energy storage for local power storage. The second is to directly connect to the power grid for transmission. However, no matter which processing method, it is necessary to perform frequency modulation on wind energy and solar energy, and adjust them into stable electric energy before storage and transmission. Due to the complexity of solar energy and wind energy, the difficulty of frequency modulation is increased; When performing frequency modulation on wind energy and solar energy power generation, it is particularly important to accurately predict the fluctuations in power generation caused by the changes in the predicted wind speed and light intensity. Previous dispatching belongs to passive frequency modulation, that is, after frequency modulation through an inverter according to the real-time situation and then storage. The above processing method requires a complex frequency modulation system, and the electrical components need a large working range to complete the task. Therefore, not only a complex frequency modulation circuit needs to be designed, but also power equipment far beyond thermal power and hydropower needs to be purchased. It is often difficult to choose between the dispatching effect and cost, increasing the power generation cost of solar energy and wind energy and reducing the service life of the equipment. For this reason, we propose a power frequency modulation control operation data acquisition system and a prediction method to solve the above problems. Summary of the Invention

[0005] The present application provides a power frequency modulation control operation data acquisition system and a prediction method, which solve the problem that the operation data cannot be accurately predicted during the power generation process of solar energy and wind energy.

[0006] The present application provides a power frequency modulation control operation data acquisition system, including an environment acquisition module for obtaining environmental information, a power station acquisition module for obtaining power station power generation information, a processing module for processing information, and a storage module for storing the above information.

[0007] Preferably, the information collected by the environment acquisition module includes time parameters and environment parameters.

[0008] Preferably, the environment parameter is weather information.

[0009] Preferably, the information collected by the power station acquisition module includes power, frequency, current, voltage, and equipment resistance information.

[0010] A power frequency modulation control operation data acquisition and prediction method includes the following steps: S1. Collect the environmental data collected at the installation location, obtain the simulated environment information according to the set time frequency, calculate the simulated power generation information based on the simulated environment information and the parameters of the installed photovoltaic or wind power, and store the simulated environment information and the simulated power generation information to obtain a simulated acquisition file; S2. Collect the actual power generation information and environmental information according to the set storage frequency, and establish a current information acquisition file in the order of the current time; S3. Compare the simulated acquisition file with the current acquisition file and modify the simulated power generation information in the simulated acquisition file; S4. Associate multiple adjacent groups of the information acquisition files, establish a prediction curve and store it; S5. Associate adjacent prediction curves to establish an early warning curve; S6. Collect continuous real-time power generation information and environmental information, compare it with the prediction curve. If the continuous real-time power generation information and real-time environmental information meet the set value error of the prediction curve, call the early warning curve, and use the leading value of the early warning curve as the future prediction value of the real-time power generation.

[0011] Preferably, if the real-time power generation information and environmental information match the historical power generation information and environmental information data within a certain range, increment the historical count by one. If the real-time power generation information and environmental information are different from the historical power generation information and environmental information data, perform new storage.

[0012] Preferably, the set value error range of the environmental information is ±10 days difference in the date and time (year, month, day), ±30 minutes difference in the current day time, ±3°C in temperature, and the same weather type.

[0013] Preferably, the set value error of the power generation information is that when the set value error of the environmental information is met, the mean square error of power, frequency, current, and voltage is less than 5.

[0014] Preferably, the time frequency and the storage frequency are the same, and both are 15 - 30 minutes.

[0015] Preferably, the simulated environment data is compared with the actually measured environmental information. If the simulated environmental information and the current environmental information meet the set value error, the simulated power generation information and the current power generation information are compared. If there are significant deviations in the power generation information, the simulated power generation information is modified. If the results are consistent, the simulated power generation information is stored as the actual power generation information.

[0016] As can be seen from the above technical solutions, the present application provides a power frequency modulation control operation data acquisition system and a prediction method. When selecting sites for installing wind power generation and solar power generation, there are generally rich wind energy and solar energy resources. Especially, these resources have fixed cycles within a certain period of time. Especially for solar energy, within the current year, the light changes are highly similar to those in previous years. Therefore, the acquisition of power frequency modulation control operation data has high accuracy for predicting the power generation of solar energy and wind energy. The present application collects based on the actual acquisition volume and the simulated volume obtained from collecting previous years' data, and is used for the advanced prediction value to customize the power generation plan in advance and reduce the situation of curtailment of wind and light. Specifically, generally before building a power station, it is necessary to investigate the perennial weather conditions in the area to obtain relevant data, and then calculate the approximate power generation situation based on the operation of photovoltaic panels or wind turbines. Therefore, we extract the above-collected weather conditions at nodes of 15 - 30 minutes, store the weather conditions at this time unit by time. The weather conditions mainly include the weather state at this time and the corresponding light intensity or wind force. Then, based on the theoretical power generation of photovoltaic panels or wind turbines, data such as the theoretical power, frequency, current, and voltage at this time node are obtained and stored. Then, after the installation of photovoltaic panels or wind turbine units is completed, the actual power generation data is collected at time nodes of 15 - 30 minutes, and the actual data is compared with the originally set theoretical data. If there are significant similarities (i.e., actual calculations have errors) between the actual power generation situation in this time period and the theoretical value, the original simulated data can be modified. When the power generation information and environmental information of this area for many years are obtained, the power generation information and environmental information are established as prediction curves, and multiple adjacent prediction curves are established as warning curves. When a similar prediction curve occurs in the same time period of the next year, the prediction curve after the warning curve in this prediction curve can be used as the predicted value, so as to predict the power generation of the power station in advance for a period of time, which is convenient for timely adjusting the power generation of the power station.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. The present application can accurately predict the power generation of solar energy or wind energy in advance, which helps to adjust the power generation plan in advance, maintain the stability of the power grid, prevent equipment failures or system collapses caused by frequency fluctuations, and can help power grid operators better manage the voltage level and ensure that the power grid voltage is within a safe range; 2. Accurately predicting the power generation frequencies of solar energy and wind energy through this application helps to more precisely match the power supply and demand, reduce the demand for backup power generation capacity, optimize the scheduling of power generation resources, lower the power generation cost, and improve economic benefits; 3. Predicting the data situation during the power generation operation helps to optimize the charge and discharge strategies of the energy storage system, improve the utilization efficiency of the energy storage system, reduce the dependence on backup power supplies, and lower the construction and maintenance costs of backup power generation capacity; 4. It can better manage the grid connection of renewable energy, reduce the impact on the power grid, help grid operators better manage the power generation of renewable energy, reduce the phenomena of wind curtailment and light curtailment, and improve the utilization rate of renewable energy; 5. By predicting the situation of operation data, potential grid faults can be detected in advance, preventive measures can be taken, the reliability of the system can be enhanced, emergencies can be responded to more quickly, and the impact on the power grid can be reduced.

[0018] In summary, by collecting the data of power frequency regulation operation, this application can accurately measure the power generation frequencies of solar energy and wind energy, improve the stability of the power grid, optimize power generation scheduling, reduce backup capacity, increase the utilization rate of renewable energy, enhance the reliability of the system, and maintain the safe operation of power stations. Brief Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions of this application, the attached drawings required for the implementation cases will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic structural diagram of a power frequency regulation control operation data acquisition system proposed by the present invention; In the figure: 1 processing module, 2 storage module, 3 environment acquisition module, 4 power station acquisition module. Detailed Embodiments

[0021] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the attached drawings.

[0022] See Figure 1, a power frequency modulation control operation data acquisition system. This application is used to obtain various parameters of power station operation, to predict solar or wind power generation to a certain extent, to make preparations in advance for the frequency modulation circuit and power grid connection, and then optimize power generation scheduling and improve the stability of grid connection. Specifically, it includes an environmental acquisition module 3 for obtaining environmental information. The environmental acquisition module 3 includes multiple modules for collecting environmental information, which is used to collect the weather conditions of the day, that is, humidity, temperature, wind force, light intensity, and the weather state of the day (sunny, cloudy or overcast), and make a comprehensive measurement of the weather conditions during this period. Due to the particularity of the weather, especially in the northwestern region, the weather is relatively stable throughout the year. When the weather state is the same as that in the same period of previous years, the prediction accuracy is higher. Therefore, it is possible to predict the current year's situation based on the weather conditions of previous years; A power station acquisition module 4 for obtaining power station generation information, which collects various information during power generation. Specifically, the information collected by the power station acquisition module 4 includes power, frequency, current, voltage, and equipment resistance information. By storing the collected power station information, relatively accurate real data can be obtained, avoiding errors caused by using theoretical values for calculation, and it can also monitor the reliability of the power station operation based on abnormal changes in the values. A processing module 1 for information processing and a storage module 2 for storing the above information. The processing module 1 is used to edit the data to make it into a computer language that can be recognized and called for storage and calculation. The obtained data is sorted and stored in the storage module 2. To ensure the security of the system, the storage module 2 can choose cloud storage for storage.

[0023] In this application, the information collected by the environmental acquisition module 3 includes time parameters and environmental parameters. Among them, the time parameters are used to accurately locate the time points of each year, ensuring that the weather factors during this period are similar, which is convenient for storage and also convenient for calling. The environmental parameters are mainly weather information, which investigates and samples temperature, humidity, light conditions, wind force conditions, and cloud conditions.

[0024] A power frequency modulation control operation data acquisition and prediction method includes the following steps: S1. Collect the environmental data collected at the installation site. When setting the location for solar or wind energy, the meteorological data of recent years will first be analyzed to obtain the feasibility study data. Therefore, this environmental data is the local meteorological data of previous years. The selected time can be the data of the past five years. According to the set time frequency, the meteorological data of previous years is intercepted at time nodes of 15 - 30 minutes per minute to obtain the data at these nodes, and the data is stored to obtain the simulated environmental information. According to the simulated environmental information and the parameters of installing photovoltaic or wind power, that is, the theoretical power generation of the photovoltaic panel or wind turbine under certain meteorological conditions, the simulated power generation information is calculated. A set of theoretical power generation data is obtained before construction, and the simulated environmental information and simulated power generation information are stored to obtain the simulated acquisition file. There are differences between this file and the theoretical value, and the simulation quantity can be revised one to two years after the installation is completed; S2. Collect the actual power generation information and environmental information according to the set storage frequency. After the power generation equipment is installed, the actual power generation situation is collected at the same frequency as in S1, and the collected environmental information is used as the comparison condition. Since the weather conditions are similar in the same time period, when the environmental information is the same, it can more accurately reflect the power generation information. Therefore, the actual situation can revise the above-mentioned simulated information, and thus establish the current information acquisition file in the order of the current time; S3. Compare the simulated acquisition file with the current acquisition file and modify the simulated power generation information in the simulated acquisition file. If there are a large number of identical or regularly deviated data in the simulated acquisition file, the simulated acquisition file can be modified to a certain extent through the current acquisition file to make it closer to the real data. The establishment of the current acquisition file can use two years of data as the comparison condition. Except for the influence of abnormal weather, the error deviation at the same time is not very large; S4. To further improve the prediction accuracy, associate adjacent multiple groups of information acquisition files, establish a prediction curve and store it, and conduct the comparison through the curve formed by multiple groups of data during the comparison; S5. Associate adjacent prediction curves to establish an early warning curve. The early warning curve covers a longer time. When most curves overlap, their subsequent development situations are also similar. Therefore, the accuracy of the system can be improved through the setting of the early warning curve; S6. Collect continuous real-time power generation information and real-time environmental information. The amount of data collected is consistent with the amount of data in the prediction curve. Compare it with the prediction curve. If the continuous real-time power generation information and environmental information meet the set value error of the prediction curve, then call the warning curve, and use the leading value of the warning curve as the future prediction value of the real-time power generation. When the curve formed by the collected continuous real-time power generation information and environmental information is consistent with the prediction curve in the same period, it proves that the data after the warning curve in the prediction curve is still consistent with the upcoming power generation information. Therefore, the leading amount of the warning curve in the prediction curve is the later prediction value. Further, to improve the accuracy of the prediction, special weather conditions are also set in the system in this application. When special weather appears through forecasting in the system, the prediction of the current power generation information can be cancelled.

[0025] In this application, if the real-time power generation information and environmental information match the historical power generation information and environmental information data within a certain range, it proves that there is consistency in the historical power generation information and environmental information during this period. The number of this group of data is incremented by one. After several years of accumulation, the specific number of days and intervals of the stable operation of the entire power station can be found through the number of data, which is also of great significance for the later maintenance of the power station. If the real-time power generation information and environmental information are different from the historical power generation information and environmental information data, new storage is performed. When the power generation information in a certain interval of time is deviated for many years, the power generation situation of the power station during this time period can be monitored keyly to avoid abnormal situations affecting the operation of the power station.

[0026] In this application, due to the certain advance or lag of the weather conditions every year, the set value error range of the environmental information is ±10 days different from the date and time of year, month, and day. Except for special abnormal weather, within the ten days before and after, the weather state can return to the normal state compared with the same period of previous years. At the same time, the time difference of the day is ±30 minutes, and the temperature difference is ±3°C, which further reduces the impact of the weather lag on the system. However, the premise of prediction needs to ensure that the weather types are the same, that is, it has predictability under the same gas phase conditions.

[0027] In this application, in addition to the monitoring and comparison of environmental information, the power generation information also needs to be compared to further improve the stability of the prediction. That is, the set value error of the power generation information is that when the set value error of the environmental information is met, the mean square error of power, frequency, current, and voltage is less than 5. When both the environmental information and the power generation information are within the error range, this system will have strong stability.

[0028] In this application, the simulated environmental data is compared with the actually measured environmental information. If the simulated environmental information and the current environmental information meet the set value error, and the weather conditions are the same within the same time period of different years, then the simulated power generation information is compared with the current power generation information. If there is a large deviation in the power generation information, the simulated power generation information is modified. That is, when there is a large and uniform difference between the simulated value and the actual value, it proves that there is a certain error in the simulation result, and the simulated power generation information needs to be modified to be consistent with the actual result. If the results are consistent, that is, the simulated power generation information and the current power generation information are the same, the simulated power generation information is stored as the actual power generation information and used as a control sample.

[0029] As can be seen from the above technical solutions, for the site selection of installing wind power generation and solar power generation, there are generally rich wind energy and solar energy resources. Especially, these resources have fixed cycles within a certain period of time. Especially for solar energy, within the current year, the light changes are highly similar to those in previous years. Therefore, the collection of power frequency control operation data has high accuracy for predicting the power generation of solar energy and wind energy. This application collects data based on the actual collected data and the simulated data obtained from collecting previous years' data for advanced prediction values, which are used to customize power generation plans in advance and reduce the situation of curtailment of wind and light. Specifically, generally before building a power station, it is necessary to investigate the perennial weather conditions in the area to obtain relevant data, and then calculate the approximate power generation situation based on the operation of photovoltaic panels or wind turbines. Therefore, we extract the above-collected weather conditions at nodes of 15 - 30 minutes and store the weather conditions at this time unit by time. The weather conditions mainly include the weather state at this time and the corresponding light intensity or wind force. Then, based on the theoretical power generation of photovoltaic panels or wind turbines, data such as the theoretical power, frequency, current, and voltage at this time node are obtained and stored. Then, after the installation of photovoltaic panels or wind turbine units is completed, the actual power generation data is collected at time nodes of 15 - 30 minutes, and the actual data is compared with the originally set theoretical data. If there is a large difference (i.e., an error in actual calculation) between the actual power generation situation in this time period and the theoretical value, the original simulated data can be modified. When the power generation information and environmental information of this area for many years are obtained, the power generation information and environmental information are established as prediction curves, and multiple adjacent prediction curves are established as warning curves. When a similar prediction curve occurs in the same time period of the next year, the prediction curve after the warning curve in this prediction curve can be used as a prediction quantity, so as to make an advanced prediction of the power generation of the power station for a period of time and facilitate timely adjustment of the power generation of the power station.

[0030] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope of the present application is pointed out by the claims.

[0031] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The embodiments of the present application described above do not constitute a limitation on the protection scope of the present application.

Claims

1. A power frequency modulation control operation data acquisition system, characterized in that It includes an environment acquisition module (3) for acquiring environment information, a power station acquisition module (4) for acquiring power station power generation information, a processing module (1) for processing information, and a storage module (2) for storing the above information.

2. The power frequency modulation control operation data acquisition system according to claim 1, characterized in that, The information acquired by the environment acquisition module (3) includes time parameters and environment parameters.

3. A power frequency modulation control operation data acquisition system according to claim 2, characterized in that, The environment parameter is weather information.

4. A power frequency modulation control operation data acquisition system according to claim 1, characterized in that, The information acquired by the power station acquisition module (4) includes power, frequency, current, voltage, and equipment resistance information.

5. A method for collecting and predicting operation data of power frequency modulation control, characterized in that It includes the following steps: S1. Collect the environment data acquired at the installation site, obtain the simulated environment information according to the set time frequency, calculate the simulated power generation information based on the simulated environment information and the parameters of installing photovoltaic or wind power, and store the simulated environment information and the simulated power generation information to obtain a simulated acquisition file. S2. Collect the actual power generation information and environment information according to the set storage frequency, and establish a current information acquisition file in the order of the current time. S3. Compare the simulated acquisition file with the current acquisition file and modify the simulated power generation information in the simulated acquisition file. S4. Associate multiple adjacent groups of the information acquisition files, establish a prediction curve and store it. S5. Associate adjacent prediction curves to establish an early warning curve. S6. Collect continuous real-time power generation information and real-time environment information, compare them with the prediction curve. If the continuous real-time power generation information and environment information meet the set value error with the prediction curve, call the early warning curve, and use the leading value of the early warning curve as the future prediction value of the real-time power generation amount.

6. A method for collecting and predicting operation data of power frequency modulation control according to claim 5, characterized in that, If the real-time power generation information and environment information match the historical power generation information and environment information data within a certain range, increment the historical quantity by one. If the real-time power generation information and environment information are different from the historical power generation information and environment information data, perform new storage.

7. A method for collecting and predicting operation data of power frequency modulation control according to claim 6, characterized in that, The set value error range of the environment information is that the date and time differ by ±10 days in year, month, and day, the time of the same day differs by ±30 minutes, the temperature differs by ±3°C, and the weather types are the same.

8. A power frequency modulation control operation data acquisition and prediction method according to claim 7, characterized in that, The set value error of the power generation information is that when the set value error of the environment information is met, the mean square error of power, frequency, current, and voltage is less than 5.

9. A method for collecting and predicting operation data of power frequency modulation control according to claim 8, characterized in that, The time frequency and the storage frequency are the same, and both are 15 - 30 minutes.

10. A method for collecting and predicting operation data of power frequency modulation control according to claim 9, characterized in that, Compare the simulated environment data with the actually measured environment information. If the simulated environment information and the current environment information meet the set value error, then compare the simulated power generation information with the current power generation information. If there are large deviations in the power generation information, modify the simulated power generation information. If the results are consistent, store the simulated power generation information as the actual power generation information.

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