New energy power generation intelligent optimization control system and method

Abstract

The invention discloses an intelligent optimization control system and method for new energy power generation, and relates to the technical field of new energy, and according to the method, real-time dynamic adjustment is carried out based on environmental data, so that a power generation system can better adapt to changing environmental conditions. For example, through real-time data transmission of the environment data acquisition and preprocessing module, the system can monitor illumination intensity, wind speed, temperature, humidity and energy storage state at any time, and accurate wind and light power generation efficiency calculation is carried out based on the data. And when the cooperative power generation efficiency Ncomb is lower than a set threshold value N, the system can automatically perform adjustment, such as triggering an energy storage adjustment mode or performing optimization adjustment on a breeze power generation inclination angle. Through the flexible adjustment mechanism, the system not only avoids the situation that the load cannot meet the requirement due to low power generation efficiency, but also effectively reduces the fault and shutdown risk of the wind-solar power generation system due to the change of environmental conditions.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to an intelligent optimization control system and method for new energy power generation. Background Art

[0002] Renewable energy power generation refers to the technology of using natural resources to generate electricity. It is a key component of the current global energy transformation and sustainable development. With the increasing requirements for environmental protection and energy efficiency, renewable energy power generation has become one of the mainstream technologies that are gradually replacing traditional energy. Among the many ways of renewable energy power generation, wind-solar complementary power generation systems, especially small power generation devices that combine breeze power generation with perovskite solar power generation, have gradually become an important solution for household and small commercial electricity use due to their strong adaptability to the environment and the ability to generate electricity around the clock. This power generation system combines the complementary characteristics of wind and solar energy. Through intelligent control methods, it can optimize power generation efficiency under different weather conditions, thereby maximizing energy utilization.

[0003] In the Chinese invention application with application number 201711317562.9, a bidirectional power generation system combining wind power generation and solar power generation system is disclosed, including a wind wheel and a solar panel installed on the top of a building, and also including a turbine generator, a control unit, a battery, a charge and discharge control module, a wireless communication module, a power detection module, an inverter, a photosensitive sensor, a wind sensor, a second electronic switch and a first electronic switch. The present invention combines wind power generation and solar power generation technology and applies them to household electricity, making full use of new energy technology, saving city electricity, and at the same time reducing environmental pollution to the greatest extent, solving the problem of discontinuous power supply when using solar power or wind power alone. The present invention has a simple structure, is easy to manufacture, and has a very low manufacturing cost.

[0004] In combination with the prior art, the above application still has the following deficiencies: At present, although the wind-solar complementary power generation system has many advantages, there are some common problems in the existing system. These problems are often caused by the lack of sufficient adaptive ability of the system when facing extreme weather conditions or unstable light and wind speed. For example, under low wind speed and weak light conditions, the output power of the wind-solar power generation system may not be able to meet the power load. If the inclination angle of the wind turbine or the energy storage mode is not adjusted in time at this time, the system may be short of power. In the prior art, although there are some intelligent control methods, it is often impossible to adjust the operating status of the equipment in real time in a dynamic environment, resulting in the failure of power generation efficiency to reach the optimal value. In particular, when the wind-solar power generation synergy efficiency is lower than the predetermined threshold, the system often fails to make timely adjustments, such as optimizing the angle of the wind turbine or the timely discharge of the energy storage system, resulting in the inability to meet the load demand. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a new energy power generation intelligent optimization control system and method, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented by the following technical scheme: comprising the following steps: S1. Install the DCM data acquisition module to collect the surrounding environmental data of the small power generation device combining breeze power generation and perovskite power generation in real time, and build a power generation control system to transmit the collected environmental data to the power generation control system through a wireless network; S2. In the power generation control system, the environmental data received in real time are preprocessed to obtain a dimensionless data set, and a cloud database is constructed to transmit the obtained dimensionless data set to the cloud database; S3. Construct a wind-solar algorithm model, extract dimensionless data sets and input them into the wind-solar collaborative algorithm model, calculate and output the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind respectively, and output the wind-solar collaborative efficiency Ncomb based on the output results of the wind-solar algorithm model. Set the efficiency threshold N and make a preliminary comparison with the obtained wind-solar collaborative efficiency Ncomb to evaluate and analyze the current collaborative power generation efficiency; S4. If the preliminary comparison and evaluation shows that the synergistic power generation efficiency is abnormal, the obtained wind-solar synergistic efficiency Ncomb, perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind are summarized to calculate the total output power Ptotal, and the total output power threshold Pth is set; S5. Perform secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, analyze the power output situation, and trigger the execution of energy storage adjustment and breeze power generation angle adjustment based on the secondary comparative evaluation result.

[0007] Preferably, said S1 includes S11 and S12; S11. Install a DCM data acquisition module around the small power generation device that combines breeze power generation and perovskite power generation, and set it to collect environmental data around the small power generation device that combines breeze power generation and perovskite power generation in real time; The DCM data acquisition module includes a photosensor, anemometer, temperature sensor and humidity sensor; The environmental data include light intensity L, wind speed V, ambient temperature Ta and ambient humidity Hu; S12. A power generation control system is constructed based on a small power generation device that combines breeze power generation and perovskite power generation. The power generation control system integrates a remote control module of the power generation device, an electric energy storage system, a cloud database, a data processing module and a visualization module. The communication module is built into the DCM data acquisition module and the communication module is integrated with the power generation control system using the LoRa wireless communication network to transmit the collected environmental data to the power generation control system.

[0008] Preferably, S2 includes S21 and S22; S21, receiving environmental data in real time in the power generation control system, and forwarding the environmental data to the data processing module; Extracting the battery charging ratio Sb in the power storage system in the data processing module; At the same time, the wind shear coefficient Cw is calculated and output based on the wind speed V in the environmental data. The specific algorithm formula is: ; Wherein, ln represents the natural logarithm, V1 represents the first wind speed, V2 represents the second wind speed, h1 represents the first height, h2 represents the second height, the first height h1 corresponds to the first wind speed V1, the second height h2 corresponds to the second wind speed V2, And in the power generation control system, the environmental data, the battery charging ratio Sb and the wind shear coefficient Cw are summarized and preprocessed to obtain a dimensionless data set, wherein the preprocessing includes data cleaning and dimensionless processing; The dimensionless data set includes light intensity L, wind speed V, ambient temperature Ta, ambient humidity Hu, battery charge ratio Sb and wind shear coefficient Cw; S22. Based on the integrated cloud database, a write port and a write port are set, and the dimensionless data set is stored in the cloud database through the write port, so as to perform cloud storage on the dimensionless data set.

[0009] Preferably, said S3 includes S31, S32, S33 and S34; S31, constructing a wind-solar algorithm model, wherein the wind-solar algorithm model includes a wind power generation algorithm model and a perovskite power generation algorithm model; The relevant parameters in the dimensionless data set are extracted through the write port and input into the perovskite power generation algorithm model to calculate and output the perovskite solar power generation efficiency Psolar; The perovskite solar power generation efficiency Psolar is achieved through the following perovskite power generation algorithm model; ; In the formula, Ns represents the photoelectric conversion efficiency of the perovskite component, As represents the light receiving area of ​​the perovskite component, represents the temperature influence coefficient, represents the humidity adjustment coefficient, Tref represents the standard temperature, and Smax represents the upper limit of the charging ratio of the battery energy storage system; S32, extracting relevant parameters from the dimensionless data set through the write port and inputting them into the wind power generation algorithm model to calculate and output the breeze power generation efficiency Pwind; The micro-wind power generation efficiency Pwind is calculated and output by the following wind power generation algorithm model; ; Where Nw represents the efficiency of the wind turbine, Aw represents the swept area of ​​the wind turbine, represents standard air density, ln represents natural logarithm, Hu represents the height of the device, Represents the wind speed distribution coefficient.

[0010] Preferably, S33, based on the obtained perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind, the energy storage state is introduced to perform collaborative calculation, the wind-solar collaborative efficiency Ncomb is output, and the collaborative working efficiency of breeze power generation and perovskite power generation is analyzed; The wind-solar synergy efficiency Ncomb is calculated and output by the following synergy algorithm formula: ; Where Ncomb(t) represents the wind-solar synergy efficiency at time t, Psolar(t) represents the perovskite solar power generation efficiency at time t, Pwind(t) represents the breeze power generation efficiency at time t, Sb(t) represents the battery charging ratio at time t, and Pmax represents the upper limit of power generation. Represents the influence coefficient of energy storage status on synergy efficiency.

[0011] Preferably, S34, based on the light intensity L and wind speed V obtained in real time from the dimensionless data set, the efficiency threshold N is dynamically set, and then the obtained wind-solar synergy efficiency Ncomb is preliminarily compared and evaluated with the efficiency threshold N, and the synergistic power generation efficiency of the small power generation device combining breeze power generation and perovskite power generation is analyzed, and the specific evaluation content is as follows; When the wind-solar synergy efficiency Ncomb(t) ≥ the efficiency threshold N at time t, it means that the synergy efficiency of the small power generation device combining the current micro-wind power generation and the perovskite power generation is normal, the energy storage state is stable, and no adjustment is required; When the wind-solar synergy efficiency Ncomb(t) at time t is less than the efficiency threshold N, it means that the synergy efficiency of the small power generation device combining the current micro-wind power generation and the perovskite power generation is abnormal.

[0012] Preferably, said S4 includes S41 and S42; S41. After preliminary comparison and evaluation of abnormal synergistic power generation efficiency, the light synergistic efficiency Ncomb, the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind are summarized and calculated to output the total output power Ptotal, and the output of electricity is analyzed; The total output power Ptotal is calculated and outputted by the following algorithm formula; ; In the formula, k loss represents the loss coefficient, represents the power loss, represents the collaborative optimization adjustment coefficient; S42: Setting a total output power threshold value Pth according to the upper limit value of the current power load.

[0013] Preferably, said S5 includes S51 and S52; S51, performing a secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, analyzing the current power output power and power load, and the specific evaluation contents are as follows; When the total output power Ptotal ≥ the total output power threshold Pth, it means that the current total power output power meets the current power load demand, and the power storage system is switched to charging mode. When the total output power Ptotal is less than the total output power threshold Pth, it indicates that the current total power output power does not meet the power load demand, and the energy storage adjustment and the wind power generation angle adjustment are triggered.

[0014] Preferably, S52, the energy storage adjustment assists the power load demand by converting the power energy storage system into a discharge mode; The micro-wind power generation tilt angle adjustment is performed by calculating and outputting the turbine tilt angle adjustment coefficient Wl based on the current light intensity L and wind speed V, and sending it to the remote control module of the power generation device to adjust the turbine angle of the micro-wind power generation device to optimize the wind energy capture efficiency; The turbine inclination angle adjustment coefficient W1 is calculated and output by the following algorithm formula: ; In the formula, represents the basic inclination angle, k represents the adjustment coefficient, Represents a mathematical tuning parameter.

[0015] An intelligent optimization control system for renewable energy power generation, comprising a power generation sensing module, a data integration module, a wind-solar collaborative analysis module, a total power analysis module and a dynamic adjustment module; The power generation sensing module collects the surrounding environment data of the small power generation device combining breeze power generation and perovskite power generation in real time by installing the DCM data acquisition module, and at the same time builds a power generation control system to transmit the collected environmental data to the power generation control system through a wireless network; The data integration module obtains dimensionless data sets by preprocessing the environmental data received in real time in the power generation control system, and constructs a cloud database to transmit the obtained dimensionless data sets to the cloud database; The wind-solar synergy analysis module constructs a wind-solar algorithm model, extracts dimensionless data sets and inputs them into the wind-solar synergy algorithm model, respectively calculates and outputs the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind, and outputs the wind-solar synergy efficiency Ncomb based on the wind-solar algorithm model output results. At the same time, the efficiency threshold N is set, and the current synergy power generation efficiency is preliminarily compared and evaluated with the obtained wind-solar synergy efficiency Ncomb. The total power analysis module calculates the total output power Ptotal and sets the total output power threshold Pth by summarizing the obtained wind-solar synergy efficiency Ncomb, perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind after preliminary comparison and evaluation of abnormal synergy power generation efficiency. The dynamic adjustment module analyzes the power output situation by performing a secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, and triggers the execution of energy storage adjustment and breeze power generation angle adjustment based on the secondary comparative evaluation result.

[0016] The present invention provides a new energy power generation intelligent optimization control system and method. It has the following beneficial effects: (1) This method can significantly improve the overall power generation efficiency by acquiring environmental data in real time during wind-solar hybrid power generation and adjusting the system operating status according to the data. Specifically, this method accurately calculates the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind, and combines the collaborative calculation of the energy storage system status to output the wind-solar collaborative efficiency Ncomb, thereby ensuring the efficient collaborative operation of the two power generation sources, wind power and solar power. Through the dynamically set efficiency threshold N and real-time evaluation, timely adjustments can be made under changing environmental conditions, such as adjusting the breeze power generation angle, to avoid energy waste or failure to meet load requirements due to inefficiency. Ultimately, it can maintain an efficient and stable state to maximize the comprehensive benefits of wind and solar power generation.

[0017] (2) This method enables the power generation system to better adapt to changing environmental conditions by making real-time dynamic adjustments based on environmental data. For example, the real-time data transmission of the environmental data acquisition and preprocessing modules enables the system to monitor the light intensity L, wind speed V, ambient temperature Ta, ambient humidity Hu, and energy storage status at any time, and perform accurate calculations of wind and solar power generation efficiency based on these data. When the collaborative power generation efficiency Ncomb is lower than the set threshold N, the system will automatically make adjustments, such as triggering the energy storage adjustment mode or optimizing the wind power generation angle. Through this flexible adjustment mechanism, the system not only avoids the inability to meet the load due to low power generation efficiency, but also effectively reduces the risk of failure and shutdown of the wind and solar power generation system due to changes in environmental conditions.

[0018] (3) This method is the key to improving overall benefits by rationally allocating wind and solar power generation resources. Through the intelligent optimization control method, the wind-solar synergy efficiency Ncomb can be accurately calculated based on the dimensionless data set obtained in real time, and evaluated and adjusted based on the preset efficiency threshold N. This mechanism can automatically adjust the operating state of the power generation device according to the current environmental conditions, such as adjusting the inclination angle of the breeze generator, optimizing the wind energy capture efficiency, and storing excess electricity when the energy storage is sufficient. When the environmental conditions are poor, it will automatically adjust to reduce unnecessary energy consumption and ensure that each unit of power generation resources is used most effectively. This not only reduces energy waste, but also improves the comprehensive utilization efficiency of resources and promotes the intelligence of energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the steps of an intelligent optimization control method for renewable energy power generation according to the present invention; Figure 2 The present invention is a schematic diagram of the process flow of an intelligent optimization control system for renewable energy power generation. DETAILED DESCRIPTION

[0020] 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.

[0021] Example 1 See also Figure 1 The present invention provides a new energy power generation intelligent optimization control method. To achieve the above purpose, the present invention is implemented through the following technical scheme: comprising the following steps: S1. Install the DCM data acquisition module to collect the surrounding environmental data of the small power generation device combining breeze power generation and perovskite power generation in real time, and build a power generation control system to transmit the collected environmental data to the power generation control system through a wireless network; S2. In the power generation control system, the environmental data received in real time are preprocessed to obtain a dimensionless data set, and a cloud database is constructed to transmit the obtained dimensionless data set to the cloud database; S3. Construct a wind-solar algorithm model, extract dimensionless data sets and input them into the wind-solar collaborative algorithm model, calculate and output the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind respectively, and output the wind-solar collaborative efficiency Ncomb based on the output results of the wind-solar algorithm model. Set the efficiency threshold N and make a preliminary comparison with the obtained wind-solar collaborative efficiency Ncomb to evaluate and analyze the current collaborative power generation efficiency; S4. If the preliminary comparison and evaluation shows that the synergistic power generation efficiency is abnormal, the obtained wind-solar synergistic efficiency Ncomb, perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind are summarized to calculate the total output power Ptotal, and the total output power threshold Pth is set; S5. Perform secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, analyze the power output situation, and trigger the execution of energy storage adjustment and breeze power generation angle adjustment based on the secondary comparative evaluation result.

[0022] In this embodiment, the method uses a series of systematic steps, combines the complementary characteristics of breeze power generation and perovskite power generation, optimizes power generation efficiency by using real-time environmental data and intelligent algorithms, and realizes dynamic regulation and optimization control of wind and solar power generation systems. In the specific implementation, the environmental data is first monitored in real time by the DCM data acquisition module, and the data is transmitted to the power generation control system through a wireless network for processing. Next, the data preprocessing converts the environmental data into a dimensionless data set and stores it in a cloud database to ensure timely update and efficient storage of the data. On this basis, the system constructs a wind and light algorithm model, and calculates the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind respectively according to the dimensionless data set input model, and further derives the wind and light synergy efficiency Ncomb, and performs a preliminary comparative evaluation with the set efficiency threshold N. When the power generation efficiency is detected to be abnormal, the system automatically calculates the total output power Ptotal, and sets the threshold Pth for secondary comparative evaluation, triggering the adjustment of the energy storage system and the optimization of the breeze power generation inclination angle, thereby ensuring stable power supply. Through the implementation of this method, it can be ensured that under different environmental conditions, adaptive adjustment can be made to achieve efficient and stable power generation. This not only effectively avoids the problem of power shortage caused by excessive fluctuations in wind and solar power generation efficiency, but also greatly improves energy utilization efficiency. The specific effects are reflected in the ability to respond to environmental changes such as light and wind speed in a timely manner; optimizing energy storage management and energy distribution, and maintaining system load balance by adjusting energy storage and wind turbine angles in the case of inefficient power generation; maximizing energy utilization, and through intelligent optimization and adjustment, enabling the system to operate in the best way under different conditions, improving the overall efficiency of power generation.

[0023] Example 2 Specifically: S1 includes S11 and S12; S11. Install a DCM data acquisition module around the small power generation device that combines breeze power generation and perovskite power generation, and set it to collect environmental data around the small power generation device that combines breeze power generation and perovskite power generation in real time; The DCM data acquisition module includes a photosensor, anemometer, temperature sensor, and humidity sensor; Environmental data include light intensity L, wind speed V, ambient temperature Ta and ambient humidity Hu; S12. A power generation control system is constructed based on a small power generation device that combines breeze power generation and perovskite power generation. The power generation control system integrates the power generation device remote control module, power storage system, cloud database, data processing module and visualization module. Through the built-in communication module in the DCM data acquisition module, the communication module is integrated with the power generation control system using the LoRa wireless communication network, and the collected environmental data is transmitted to the power generation control system.

[0024] In this embodiment, in the implementation of this method, first, by installing the DCM data acquisition module, the environmental data around the small power generation device combining breeze power generation and perovskite power generation are collected in real time, including light intensity L, wind speed V, ambient temperature Ta and humidity Hu. The collection of these environmental data provides real-time basic information for subsequent optimization control, ensuring that the system can respond to changes in the external environment in real time. The DCM data acquisition module can accurately measure and transmit environmental parameters to the power generation control system. Secondly, the power generation control system integrates the remote control module of the power generation device, the power storage system, the cloud database, the data processing module and the visualization module to ensure that the system can adjust and optimize autonomously under different conditions. Through the built-in LoRa wireless communication network, the DCM data acquisition module is seamlessly connected to the power generation control system to realize real-time transmission and processing of data. This information flow mechanism enables the system to receive environmental data instantly and store and analyze it in the cloud, providing accurate data support for subsequent decision-making. Through this implementation method, comprehensive real-time monitoring and remote control of environmental data can be achieved, which improves the adaptability and flexibility of wind and solar power generation. Specifically, the method ensures that the operating status can be dynamically adjusted according to changes in light and wind speed, optimizing power generation efficiency, and adjusting the energy storage system based on real-time data to avoid energy waste.

[0025] Example 3 Specifically: S2 includes S21 and S22; S21, receiving environmental data in real time in the power generation control system, and forwarding the environmental data to the data processing module; Extracting the battery charging ratio Sb in the power storage system in the data processing module; At the same time, the wind shear coefficient Cw is calculated and output based on the wind speed V in the environmental data. The specific algorithm formula is: ; Wherein, ln represents the natural logarithm, V1 represents the first wind speed, V2 represents the second wind speed, h1 represents the first height, h2 represents the second height, the first height h1 corresponds to the first wind speed V1, the second height h2 corresponds to the second wind speed V2, In the power generation control system, the environmental data, battery charging ratio Sb and wind shear coefficient Cw are summarized and preprocessed to obtain a dimensionless data set. The preprocessing includes data cleaning and dimensionless processing. The dimensionless data set includes light intensity L, wind speed V, ambient temperature Ta, ambient humidity Hu, battery charge ratio Sb, and wind shear coefficient Cw; S22. Based on the integrated cloud database, a write port and a write port are set, and the dimensionless data set is stored in the cloud database through the write port, so as to perform cloud storage on the dimensionless data set.

[0026] In this embodiment, the method ensures that the system can make intelligent decisions according to environmental changes by effectively processing the environmental data collected in real time. First, the power generation control system receives the environmental data provided by the DCM data acquisition module in real time and forwards it to the data processing module for further processing. The data processing module not only extracts the battery charging ratio Sb in the power storage system, but also calculates and outputs the wind shear coefficient Cw through the wind speed V in the environmental data. The coefficient is used to describe the characteristics of wind speed changing with height, thereby providing an accurate adjustment basis for wind power generation efficiency. Based on these real-time calculation results, the battery charging ratio Sb and the wind shear coefficient Cw are summarized with other environmental data to generate a dimensionless data set. The data set ensures the standardization of all data through preprocessing steps and provides accurate input for subsequent wind and light collaborative calculations. On this basis, data storage and management are performed through an integrated cloud database. The dimensionless data set is stored in the cloud through a write port to ensure that the system's operating data can be remotely accessed and processed in real time. Cloud storage not only ensures the long-term preservation and sharing of data, but also supports system optimization and adjustment through real-time data transmission. Through this implementation method, the system can accurately collect and process environmental data, providing solid data support for intelligent optimization control.

[0027] Example 4 Specifically: S3 includes S31, S32, S33 and S34; S31. Construct a wind-solar algorithm model, which includes a wind power generation algorithm model and a perovskite power generation algorithm model; The relevant parameters in the dimensionless data set are extracted through the write port and input into the perovskite power generation algorithm model to calculate and output the perovskite solar power generation efficiency Psolar; Perovskite solar power generation efficiency Psolar is achieved through the following perovskite power generation algorithm model; ; In the formula, Ns represents the photoelectric conversion efficiency of the perovskite component, As represents the light receiving area of ​​the perovskite component, Indicates the temperature influence coefficient, reflecting the relationship between temperature and power generation efficiency. represents the humidity adjustment coefficient, Tref represents the standard temperature considering the impact of humidity on power generation, and Smax represents the upper limit of the charging ratio of the battery energy storage system, all of which are dimensionless parameters; Used to describe the effects of light intensity and temperature on the power generation efficiency of perovskite; Used to analyze the dynamic regulation of energy storage status on power generation efficiency. When energy storage is high, the power generation intensity may be increased to support future demand; S32, extracting relevant parameters from the dimensionless data set through the write port and inputting them into the wind power generation algorithm model to calculate and output the breeze power generation efficiency Pwind; The breeze power generation efficiency Pwind is calculated and output by the following wind power generation algorithm model; ; Where Nw represents the efficiency of the wind turbine, Aw represents the swept area of ​​the wind turbine, represents standard air density, ln represents natural logarithm, Hu represents the height of the device, represents the wind speed distribution coefficient, 0.0065 represents the standard decrease rate of atmospheric temperature, which means that with the increase of the device height Hu, the atmospheric temperature will decrease by 0.0065 for every increase of 1 meter, and 273.15 is a constant used to convert Celsius temperature into absolute temperature. All of them are dimensionless parameters.

[0028] S33, based on the obtained perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind, the energy storage state is introduced for collaborative calculation, the wind-solar collaborative efficiency Ncomb is output, and the collaborative working efficiency of breeze power generation and perovskite power generation is analyzed; The wind-solar synergy efficiency Ncomb is calculated and output by the following synergy algorithm formula; ; Where Ncomb(t) represents the wind-solar synergy efficiency at time t, Psolar(t) represents the perovskite solar power generation efficiency at time t, Pwind(t) represents the breeze power generation efficiency at time t, Sb(t) represents the battery charging ratio at time t, and Pmax represents the upper limit of power generation. Represents the influence coefficient of energy storage status on synergy efficiency.

[0029] S34. Based on the light intensity L and wind speed V obtained in real time from the dimensionless data set, the efficiency threshold N is dynamically set, and then the obtained wind-solar synergy efficiency Ncomb is preliminarily compared and evaluated with the efficiency threshold N to analyze the synergistic power generation efficiency of the small power generation device combining micro-wind power generation and perovskite power generation. The specific evaluation contents are as follows; When the wind-solar synergy efficiency Ncomb(t) ≥ the efficiency threshold N at time t, it means that the synergy efficiency of the small power generation device combining the current micro-wind power generation and the perovskite power generation is normal, the energy storage state is stable, and no adjustment is required; When the wind-solar synergy efficiency Ncomb(t) at time t is less than the efficiency threshold N, it means that the synergy efficiency of the small power generation device combining the current micro-wind power generation and the perovskite power generation is abnormal; The efficiency threshold N is set by the following algorithm formula: , where min represents the lower limit function, Lmax represents the upper limit of light intensity, Vmax represents the lower limit of wind speed, 0.9 represents the upper limit of the efficiency threshold N, that is, the efficiency threshold N cannot exceed 90%, 0.75 represents the basic value of the efficiency threshold N, that is, when the environmental conditions are poor and the wind speed and light intensity are close to 0, the lowest level of the efficiency threshold N, this value can be regarded as the minimum acceptable performance target, and 0.15 represents the sensitivity factor used to dynamically adjust the efficiency threshold N, which determines the influence of the environmental conditions of light L and wind speed V on the change of the efficiency threshold N.

[0030] In this embodiment, the method collects environmental data in real time by installing a DCM data acquisition module, including light intensity, wind speed, temperature and humidity, and transmits the data to the power generation control system. At this time, by transmitting the data to the cloud database, an efficient storage and access method is provided for subsequent data analysis. Next, the environmental data is preprocessed, the dimensionless data set is extracted, and the battery charging ratio of the energy storage system and the wind shear coefficient are extracted to ensure that the impact of wind speed changes on power generation efficiency is accurately captured. At the same time, a wind-light algorithm model is constructed, and the independent algorithms of perovskite solar energy and breeze power generation are combined to input the relevant parameters in the dimensionless data set, and the solar power generation efficiency Psolar and breeze power generation efficiency Pwind are calculated respectively. At the same time, the wind-light collaborative efficiency Ncomb is obtained based on the energy storage state. This efficiency value is a key performance indicator of the power generation system, which can reflect the collaborative working efficiency of the wind-light system in real time. In addition, the efficiency threshold N is dynamically set based on the real-time acquired environmental data, and the calculation result is preliminarily compared with the efficiency threshold to evaluate whether the system is running normally.

[0031] Example 5 Specifically: S4 includes S41 and S42; S41. After preliminary comparison and evaluation of abnormal synergistic power generation efficiency, the light synergistic efficiency Ncomb, the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind are summarized and calculated to output the total output power Ptotal, and the output of electricity is analyzed; The total output power Ptotal is calculated by the following algorithm formula; ; In the formula, k loss represents the loss coefficient, represents the power loss, It represents the collaborative optimization adjustment coefficient, which is used to correct the negative capability when the collaborative efficiency is low; S42: Setting a total output power threshold value Pth according to the upper limit value of the current power load.

[0032] In this embodiment, when the wind-solar synergy efficiency Ncomb is found to be abnormal in the preliminary evaluation, the system will summarize the perovskite solar power generation efficiency Psolar, the breeze power generation efficiency Pwind and the wind-solar synergy efficiency Ncomb to calculate the total output power Ptotal. In the calculation process, the system loss coefficient k is taken into account. loss And the collaborative optimization adjustment coefficient is used to make negative corrections when the collaborative efficiency is low. Through this process, the system can accurately evaluate the current power output and provide data basis for subsequent adjustments. Next, based on the power upper limit of the current power load, the system will set the total output power threshold Pth as a comparison standard to ensure that the system can understand in real time whether the preset power output requirements are met or exceeded. The setting of this threshold is of great significance to system scheduling, and can guide the system to reduce energy waste when the load is low, or start the energy storage system when the load is high to provide more power support.

[0033] Example 6 Specifically: S5 includes S51 and S52; S51, performing secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, analyzing the current power output power and power load, the specific evaluation contents are as follows; When the total output power Ptotal ≥ the total output power threshold Pth, it means that the current total power output power meets the current power load demand, and the power storage system is switched to charging mode. When the total output power Ptotal is less than the total output power threshold Pth, it indicates that the current total power output power does not meet the power load demand, and the energy storage adjustment and the wind power generation angle adjustment are triggered.

[0034] S52, energy storage adjustment assists power load demand by converting the power energy storage system into a discharge mode; The wind power generation tilt angle adjustment is based on the current light intensity L and wind speed V. The turbine tilt angle adjustment coefficient Wl is calculated and output, and sent to the remote control module of the power generation device to adjust the turbine angle of the wind power generation device to optimize the wind energy capture efficiency. The turbine inclination angle adjustment coefficient Wl is calculated and output by the following algorithm formula: ; In the formula, represents the basic inclination angle, k represents the adjustment coefficient, which determines the sensitivity of the inclination angle to light intensity. Represents a mathematical adjustment parameter used to avoid zero value problems in the denominator or logarithmic function or numerical anomalies in extreme cases. Used to avoid excessive adjustments.

[0035] In this embodiment, the method analyzes the current power output and the actual load by performing a secondary comparative evaluation based on the total output power Ptotal and the total output power threshold Pth calculated in real time. When Ptotal ≥ Pth, it means that the current power output of the system meets the load demand, and the system will automatically switch the power storage system to charging mode to store the remaining electric energy; conversely, when Ptotal < Pth, it means that the current power generation is insufficient to meet the load demand. At this time, the system will trigger the optimization adjustment of the energy storage adjustment and the wind power generation inclination to ensure that the power demand is met. At the same time, the energy storage adjustment provides more power support by switching the power storage system to the discharge mode to ensure that the system can still provide enough power for the load when the power generation is insufficient. In addition, the inclination adjustment of the wind power generation is calculated by calculating the turbine inclination adjustment coefficient Wl according to the real-time light intensity L and wind speed V, and adjusting the inclination of the wind turbine through the remote control module to optimize the wind energy capture efficiency. This adjustment can greatly improve the efficiency of the wind power generation device at low wind speeds or insufficient light, and further supplement the power output of the system.

[0036] Example 7 See also Figure 1 and Figure 2 , an intelligent optimization control system for new energy power generation, including a power generation perception module, a data integration module, a wind-solar collaborative analysis module, a total power analysis module and a dynamic adjustment module; The power generation sensing module collects the surrounding environmental data of the small power generation device combining breeze power generation and perovskite power generation in real time by installing the DCM data acquisition module, and at the same time builds a power generation control system to transmit the collected environmental data to the power generation control system through a wireless network; The data integration module obtains dimensionless data sets by preprocessing the environmental data received in real time in the power generation control system, and at the same time constructs a cloud database to transmit the obtained dimensionless data sets to the cloud database; The wind-solar synergy analysis module constructs a wind-solar algorithm model and extracts dimensionless data sets to input into the wind-solar synergy algorithm model. It calculates and outputs the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind respectively. At the same time, based on the output results of the wind-solar algorithm model, it comprehensively outputs the wind-solar synergy efficiency Ncomb, sets the efficiency threshold N, and makes a preliminary comparison and evaluation of the current synergy power generation efficiency with the obtained wind-solar synergy efficiency Ncomb. The total power analysis module calculates the total output power Ptotal after initially comparing and evaluating the abnormality of the synergistic power generation efficiency, and summarizes the obtained wind-solar synergistic efficiency Ncomb, perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind, and sets the total output power threshold Pth; The dynamic adjustment module analyzes the power output situation by performing a secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, and triggers the execution of energy storage adjustment and breeze power generation angle adjustment based on the secondary comparative evaluation result.

[0037] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A new energy power generation intelligent optimization control method, characterized in that: The following steps are involved: S1. Install the DCM data acquisition module to collect the surrounding environmental data of the small power generation device combining breeze power generation and perovskite power generation in real time, and build a power generation control system to transmit the collected environmental data to the power generation control system through a wireless network; S2. In the power generation control system, the environmental data received in real time are preprocessed to obtain a dimensionless data set, and a cloud database is constructed to transmit the obtained dimensionless data set to the cloud database; S3. Construct a wind-solar algorithm model, extract dimensionless data sets and input them into the wind-solar collaborative algorithm model, calculate and output the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind respectively, and output the wind-solar collaborative efficiency Ncomb based on the output results of the wind-solar algorithm model. Set the efficiency threshold N and make a preliminary comparison with the obtained wind-solar collaborative efficiency Ncomb to evaluate and analyze the current collaborative power generation efficiency; S4. If the preliminary comparison and evaluation shows that the synergistic power generation efficiency is abnormal, the obtained wind-solar synergistic efficiency Ncomb, perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind are summarized to calculate the total output power Ptotal, and the total output power threshold Pth is set; S5. Perform secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, analyze the power output situation, and trigger the execution of energy storage adjustment and breeze power generation angle adjustment based on the secondary comparative evaluation result.

2. The intelligent optimization control method for renewable energy power generation according to claim 1, characterized in that: Said S1 includes S11 and S12; S11. Install a DCM data acquisition module around the small power generation device that combines breeze power generation and perovskite power generation, and set it to collect environmental data around the small power generation device that combines breeze power generation and perovskite power generation in real time; The DCM data acquisition module includes a photosensor, anemometer, temperature sensor and humidity sensor; The environmental data include light intensity L, wind speed V, ambient temperature Ta and ambient humidity Hu; S12. A power generation control system is constructed based on a small power generation device combining breeze power generation and perovskite power generation. The power generation control system integrates a remote control module of the power generation device, an electric energy storage system, a cloud database, a data processing module and a visualization module. Through the communication module built into the DCM data acquisition module, the communication module is integrated with the power generation control system using the LoRa wireless communication network to transmit the collected environmental data to the power generation control system.

3. The intelligent optimization control method for renewable energy power generation according to claim 2, characterized in that: The S2 includes S21 and S22; S21, receiving environmental data in real time in the power generation control system, and forwarding the environmental data to the data processing module; Extracting the battery charging ratio Sb in the power storage system in the data processing module; At the same time, the wind shear coefficient Cw is calculated and output based on the wind speed V in the environmental data. The specific algorithm formula is: ; Wherein, ln represents the natural logarithm, V1 represents the first wind speed, V2 represents the second wind speed, h1 represents the first height, h2 represents the second height, the first height h1 corresponds to the first wind speed V1, and the second height h2 corresponds to the second wind speed V2, And in the power generation control system, the environmental data, the battery charging ratio Sb and the wind shear coefficient Cw are summarized and preprocessed to obtain a dimensionless data set, wherein the preprocessing includes data cleaning and dimensionless processing; The dimensionless data set includes light intensity L, wind speed V, ambient temperature Ta, ambient humidity Hu, battery charge ratio Sb and wind shear coefficient Cw; S22. Based on the integrated cloud database, a write port and a write port are set, and the dimensionless data set is stored in the cloud database through the write port, so as to perform cloud storage on the dimensionless data set.

4. The intelligent optimization control method for renewable energy power generation according to claim 3 is characterized in that: The S3 includes S31, S32, S33 and S34; S31, constructing a wind-solar algorithm model, wherein the wind-solar algorithm model includes a wind power generation algorithm model and a perovskite power generation algorithm model; The relevant parameters in the dimensionless data set are extracted through the write port and input into the perovskite power generation algorithm model to calculate and output the perovskite solar power generation efficiency Psolar; The perovskite solar power generation efficiency Psolar is achieved through the following perovskite power generation algorithm model; ； In the formula, Ns represents the photoelectric conversion efficiency of the perovskite component, As represents the light receiving area of ​​the perovskite component, represents the temperature influence coefficient, represents the humidity adjustment coefficient, Tref represents the standard temperature, and Smax represents the upper limit of the charging ratio of the battery energy storage system; S32, extracting relevant parameters from the dimensionless data set through the write port and inputting them into the wind power generation algorithm model to calculate and output the breeze power generation efficiency Pwind; The micro-wind power generation efficiency Pwind is calculated and output by the following wind power generation algorithm model; ； Where Nw represents the efficiency of the wind turbine, Aw represents the swept area of ​​the wind turbine, represents standard air density, ln represents natural logarithm, Hu represents the height of the device, Represents the wind speed distribution coefficient.

5. The intelligent optimization control method for renewable energy power generation according to claim 4 is characterized in that: S33, based on the obtained perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind, the energy storage state is introduced for collaborative calculation, the wind-solar collaborative efficiency Ncomb is output, and the collaborative working efficiency of breeze power generation and perovskite power generation is analyzed; The wind-solar synergy efficiency Ncomb is calculated and output by the following synergy algorithm formula: ； Where Ncomb(t) represents the wind-solar synergy efficiency at time t, Psolar(t) represents the perovskite solar power generation efficiency at time t, Pwind(t) represents the breeze power generation efficiency at time t, Sb(t) represents the battery charging ratio at time t, and Pmax represents the upper limit of power generation. Represents the influence coefficient of energy storage status on synergy efficiency.

6. The intelligent optimization control method for renewable energy power generation according to claim 1, characterized in that: S34. Based on the light intensity L and wind speed V obtained in real time from the dimensionless data set, the efficiency threshold N is dynamically set, and then the obtained wind-solar synergy efficiency Ncomb is preliminarily compared and evaluated with the efficiency threshold N to analyze the synergistic power generation efficiency of the small power generation device combining breeze power generation and perovskite power generation. The specific evaluation contents are as follows; When the wind-solar synergy efficiency Ncomb(t) ≥ the efficiency threshold N at time t, it means that the synergy efficiency of the small power generation device combining the current micro-wind power generation and the perovskite power generation is normal, the energy storage state is stable, and no adjustment is required; When the wind-solar synergy efficiency Ncomb(t) at time t is less than the efficiency threshold N, it means that the synergy efficiency of the small power generation device combining the current micro-wind power generation and the perovskite power generation is abnormal.

7. The intelligent optimization control method for renewable energy power generation according to claim 6, characterized in that: The S4 includes S41 and S42; S41. After preliminary comparison and evaluation of abnormal synergistic power generation efficiency, the light synergistic efficiency Ncomb, the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind are summarized and calculated to output the total output power Ptotal, and the output of electricity is analyzed; The total output power Ptotal is calculated and outputted by the following algorithm formula; ； In the formula, k loss represents the loss coefficient, represents the power loss, represents the collaborative optimization adjustment coefficient; S42: Setting a total output power threshold value Pth according to the upper limit value of the current power load.

8. The intelligent optimization control method for renewable energy power generation according to claim 7, characterized in that: The S5 includes S51 and S52; S51, performing a secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, analyzing the current power output power and power load, and the specific evaluation contents are as follows; When the total output power Ptotal ≥ the total output power threshold Pth, it means that the current total power output power meets the current power load demand, and the power storage system is switched to charging mode. When the total output power Ptotal is less than the total output power threshold Pth, it indicates that the current total power output power does not meet the power load demand, and the energy storage adjustment and the wind power generation angle adjustment are triggered.

9. The intelligent optimization control method for renewable energy power generation according to claim 8, characterized in that: S52, the energy storage adjustment assists the power load demand by converting the power energy storage system into a discharge mode; The micro-wind power generation tilt angle adjustment is performed by calculating and outputting the turbine tilt angle adjustment coefficient Wl based on the current light intensity L and wind speed V, and sending it to the remote control module of the power generation device to adjust the turbine angle of the micro-wind power generation device to optimize the wind energy capture efficiency; The turbine inclination angle adjustment coefficient W1 is calculated and output by the following algorithm formula: ； In the formula, represents the basic inclination angle, k represents the adjustment coefficient, Represents a mathematical tuning parameter.

10. A new energy power generation intelligent optimization control system, applied to a new energy power generation intelligent optimization control method according to any one of claims 1 to 9, characterized in that: It includes power generation perception module, data integration module, wind-solar collaborative analysis module, total power analysis module and dynamic adjustment module; The power generation sensing module collects the surrounding environment data of the small power generation device combining breeze power generation and perovskite power generation in real time by installing the DCM data acquisition module, and at the same time builds a power generation control system to transmit the collected environmental data to the power generation control system through a wireless network; The data integration module obtains dimensionless data sets by preprocessing the environmental data received in real time in the power generation control system, and constructs a cloud database to transmit the obtained dimensionless data sets to the cloud database; The wind-solar synergy analysis module constructs a wind-solar algorithm model, extracts dimensionless data sets and inputs them into the wind-solar synergy algorithm model, respectively calculates and outputs the perovskite solar power generation efficiency Psolar and the breeze power generation efficiency Pwind, and outputs the wind-solar synergy efficiency Ncomb based on the wind-solar algorithm model output results. At the same time, the efficiency threshold N is set, and the current synergy power generation efficiency is preliminarily compared and evaluated with the obtained wind-solar synergy efficiency Ncomb. The total power analysis module calculates the total output power Ptotal and sets the total output power threshold Pth by summarizing the obtained wind-solar synergy efficiency Ncomb, perovskite solar power generation efficiency Psolar and breeze power generation efficiency Pwind after preliminary comparison and evaluation of abnormal synergy power generation efficiency. The dynamic adjustment module analyzes the power output situation by performing a secondary comparative evaluation based on the acquired total output power Ptotal and the total output power threshold Pth, and triggers the execution of energy storage adjustment and breeze power generation angle adjustment based on the secondary comparative evaluation result.

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