A method for generating a complementary operation strategy of a sandstorm and gobi desert wind scene of a typical meteorological field

Abstract

This invention discloses a method for generating a wind-solar-storage complementary operation strategy for typical meteorological scenarios in desert areas. It relates to the field of complementary strategy generation technology and addresses the problem of accurately identifying the inverse relationship between wind and solar power output. By collecting the concentration of inhalable particulate matter in the wind-solar-storage area under test and analyzing the particulate shading trend, when particulate shading exists, solar irradiance data and DC input power data of the photovoltaic inverter are collected. When the wind-solar-storage area is identified as being at the edge of a sandstorm, photovoltaic output current and wind turbine pitch angle data are collected to classify the wind-solar inverse relationship levels. Wind turbine nacelle wind speed data is retrieved to calculate the wind field trigger coefficient, and the wind-solar inverse relationship levels are corrected. Based on the correction results, either energy storage freezing or energy storage scheduling is selected. This method enables the identification of the wind-solar complementary relationship under sandstorm edge meteorological conditions and automatically generates corresponding energy storage operation strategies, reducing unnecessary charging and discharging behavior of the energy storage system.

Description

Technical Field

[0001] This invention relates to the field of complementary strategy generation technology, and more specifically, to a method for generating a complementary operation strategy for wind, solar and energy storage in a typical meteorological scenario in a desert area. Background Technology

[0002] In the large-scale construction of integrated wind, solar, and energy storage new energy bases in Northwest my country, the desert and Gobi areas, with their vast land resources, abundant solar energy resources, and favorable wind energy conditions, have gradually become important regions for centralized new energy development. However, these areas are generally characterized by frequent sandstorms and large fluctuations in the concentration of suspended particulate matter in the air. During sandstorms, the suspension of a large amount of inhalable particulate matter in the atmosphere alters the propagation path of solar radiation, causing a significant decrease or short-term fluctuation in the effective irradiance received by photovoltaic modules, resulting in a rapid decline in photovoltaic output.

[0003] The existing technology has the following shortcomings: Currently, existing technologies mainly rely on meteorological forecast information or single power monitoring data to schedule the operation of wind-solar-storage systems. It is difficult to identify the trend of photovoltaic output change caused by air particulate matter shading under the meteorological conditions of the edge of sandstorms in a timely manner. There is a lack of a comprehensive analysis mechanism for the correlation characteristics between solar irradiance and photovoltaic operation status, which makes it difficult to accurately identify the inverse relationship between wind and solar output. The energy storage system is prone to unnecessary charging and discharging adjustment behavior. Therefore, a method for generating a wind-solar-storage complementary operation strategy for typical meteorological scenarios in sandy deserts is proposed. Summary of the Invention

[0004] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide a method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in desert and Gobi areas. This method utilizes a collaborative analysis mechanism that identifies particulate matter shading trends, assesses dust shading indices, determines the level of wind-solar inverse changes, and corrects wind field triggering coefficients to address the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert-golam area, comprising the following steps: Step S1: Set the particle monitoring time, collect the concentration of inhalable particulate matter in the wind and solar storage area to be tested during the particle monitoring time and analyze the particle shading trend. Based on the particle shading trend, determine whether to collect solar irradiance data and DC input power data of photovoltaic inverter. Step S2: Evaluate the shading response characteristics by combining solar irradiance data and DC input power data, generate a dust shading index by combining particle shading trends, and evaluate the dust edge status of the current wind and solar storage area based on the dust shading index. Step S3: Determine whether the wind-solar hybrid identification stage is triggered based on the state of the sandstorm edge. When the wind-solar hybrid identification stage is triggered, detect the photovoltaic output current and wind turbine pitch angle of the wind-solar storage area to be tested, and analyze the wind-solar reverse change level using the detection results. Step S4: Access the meteorological monitoring database to retrieve wind speed data of the wind turbine nacelle, calculate the wind field triggering coefficient based on the wind turbine nacelle wind speed data and correct the wind-solar inverse change level, and select energy storage freezing processing or energy storage scheduling processing based on the corrected wind-solar inverse change level.

[0006] In a preferred embodiment, in step S1, the particle monitoring time is preset and divided into multiple monitoring times. The concentration of inhalable particulate matter in the wind and solar storage area to be tested is collected by a laser scattering particulate matter online monitoring instrument. The concentrations of each inhalable particulate matter are combined into a particulate matter concentration sequence according to the time sequence. In the particulate matter concentration sequence, the difference between adjacent inhalable particulate matter concentrations is used to obtain the particulate matter concentration change. The number of positive particulate matter concentration changes is taken as the increase count, and the ratio of the increase count to the total number of particulate matter concentration changes is taken as the increase percentage of concentration change. The average particle concentration is obtained by averaging the concentrations of each inhalable particulate matter. The particle shading trend was calculated based on the average particle concentration and the percentage increase in concentration. If the particle shading trend is less than the preset particle shading threshold, it is determined that the wind and solar storage area under test is not in a particle shading state. Conversely, if the wind and solar storage area to be tested is determined to be in a state of particle shading.

[0007] In a preferred embodiment, in step S1, when the wind and solar storage area to be tested is in a state of particle shading, solar irradiance data and DC input power data of the photovoltaic inverter are further collected. Specifically, within the preset particle monitoring time, the solar irradiance data of the wind and solar storage area to be tested is retrieved through the irradiance monitoring unit, and the solar irradiance data is sorted and combined into a solar irradiance sequence according to the time order. The DC input power data corresponding to each monitoring moment is obtained through the operation monitoring interface of the photovoltaic inverter. The DC input power data is sorted and combined into a DC input power sequence according to the time order.

[0008] In a preferred embodiment, in step S2, the solar irradiance sequence and the DC input power sequence are time-aligned using each monitoring time as a time reference. The solar irradiance data at adjacent monitoring times are calculated by difference in chronological order to obtain a sequence of solar irradiance changes. At the same time, the DC input power data at adjacent monitoring times are calculated by difference to obtain a sequence of DC input power changes. Statistical calculations were performed on the solar irradiance variation sequence and the DC input power variation sequence to obtain the average solar irradiance variation and the average DC input power variation. When both the average change in DC input power and the average change in solar irradiance are negative, the ratio of the average change in DC input power to the average change in solar irradiance is used as the shading response characteristic.

[0009] In a preferred embodiment, in step S2, a preset particle shading threshold and particle shading trend are retrieved, and a shading disturbance factor is calculated based on the preset particle shading threshold and particle shading trend. After standardizing the shading response characteristics, the shading response coefficient is obtained. The product of the shading disturbance factor and the shading response coefficient is used as the dust shading index. The dust shading index is compared with the preset dust edge shading range to assess the current dust edge status of the wind and solar storage area to be tested. If the dust shading index falls within the preset dust edge shading range, then the dust edge state of the current wind and solar storage area to be tested is determined to be the dust edge formation state. Otherwise, the current dust edge state of the wind and solar storage area to be tested is determined to be a non-dust edge state.

[0010] In a preferred embodiment, in step S3, when step S2 determines that the current dust edge state of the wind-solar reservoir area to be tested is a dust edge formation state, the wind-solar complementarity identification stage is triggered. In the wind-solar hybrid identification stage, the photovoltaic output current of each photovoltaic inverter in the wind-solar storage area to be tested is first collected through the operation monitoring interface of the photovoltaic inverter. Photovoltaic output current refers to the output current formed on the DC side of the inverter after the photovoltaic module array is combined; Within the preset wind and solar identification time, the photovoltaic output current is collected based on each monitoring time and combined in chronological order to form a photovoltaic output current sequence. The difference between the photovoltaic output currents at adjacent monitoring times in the photovoltaic output current sequence is calculated and the average value is taken to obtain the average change of photovoltaic current; The pitch angle of each wind turbine in the wind and solar storage area under test is collected through the operation monitoring interface of the wind turbine control system. The pitch angle of a wind turbine is the installation angle of the wind turbine blades relative to the direction of the incoming airflow.

[0011] In a preferred embodiment, in step S3, within a preset wind and solar identification time, the wind turbine pitch angle is collected and a pitch angle sequence is formed using the same monitoring time as the photovoltaic output current as the time reference. The pitch angle is calculated by performing a difference calculation on the pitch angles at adjacent monitoring times in the pitch angle sequence and taking the average value to obtain the average change in pitch angle. The average change in photovoltaic current and the average change in pitch angle are standardized to obtain the photovoltaic descent coefficient and the wind power rise coefficient. Calculate the inverse relationship index between wind and solar power using the photovoltaic power decline coefficient and the wind power rise coefficient: ; in, This indicates the inverse relationship index between wind and light. Indicates the photovoltaic degradation coefficient. Indicates the wind power rise coefficient; The wind-sunlight inversion index is compared with the preset wind-sunlight inversion level range, and the wind-sunlight inversion level is divided into weak inversion level, moderate inversion level, and strong inversion level based on the comparison results.

[0012] In a preferred embodiment, in step S4, the meteorological monitoring database of the wind and solar storage area to be tested is accessed, and the wind speed data of the wind turbine nacelle recorded by the wind speed sensor on the top of each wind turbine nacelle within the preset wind field identification time is retrieved. The wind turbine nacelle wind speed refers to the incoming wind speed measured by an anemometer installed on the top of the wind turbine nacelle. The retrieved wind turbine nacelle wind speed data is time-aligned to form a nacelle wind speed sequence. The average wind speed change is obtained by performing difference calculations on the wind speed data at adjacent monitoring times in the cabin wind speed sequence and taking the average value. The average cabin wind speed is obtained by averaging the wind speed values ​​in the cabin wind speed sequence.

[0013] In a preferred embodiment, in step S4, the average cabin wind speed and the average wind speed change are standardized to obtain the wind speed level coefficient and the wind speed change coefficient. The wind field triggering coefficient is calculated using an exponential triggering model based on the wind speed horizontal coefficient and the wind speed variation coefficient. The corrected inverse change index is calculated based on the wind-solar inverse change index and the wind field triggering coefficient. The corrected reverse change index is compared with the preset reverse change level range for scenery, and the corrected reverse change level for scenery is redefined. When the corrected wind-solar inverse change level is strong inverse change level or medium inverse change level, it is determined to implement energy storage freeze treatment, that is, to suspend the charging and discharging operation of the energy storage system and use the increase in wind power output to naturally compensate for the decrease in photovoltaic output. When the corrected wind-solar inverse change level is a weak inverse change level, it is determined to execute energy storage dispatch processing, that is, to regulate the charging and discharging through the energy storage system to maintain the power output of the wind-solar-storage field under test.

[0014] The technical effects and advantages of this invention are as follows: This invention collects the concentration of inhalable particulate matter in the wind-solar-storage area under test and analyzes the shading trend of particles. When shading by particles is present, it further collects solar irradiance data and DC input power data of photovoltaic inverters. When the wind-solar-storage area is identified as being in a dust storm edge formation state, it collects photovoltaic output current and wind turbine pitch angle data to classify the wind-solar reversal level. It further retrieves wind turbine nacelle wind speed data to calculate the wind field trigger coefficient, corrects the wind-solar reversal level, and selects to perform energy storage freezing or energy storage scheduling based on the correction results. This enables the identification of wind-solar complementarity under dust storm edge meteorological conditions and automatically generates corresponding energy storage operation strategies, thereby reducing unnecessary charging and discharging behavior of the energy storage system. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the implementation of a method for generating a wind-solar-storage complementary operation strategy in a typical meteorological scenario of the present invention.

[0016] Figure 2 This is a schematic diagram illustrating the steps of a method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert area according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention collects the concentration of inhalable particulate matter in the wind-solar storage area to be tested and analyzes the shading trend of particles. When particle shading exists, it further collects solar irradiance data and DC input power data of photovoltaic inverters. When the wind-solar storage area is in the edge of a sandstorm formation state, it collects photovoltaic output current and wind turbine pitch angle data to classify the wind-solar inverse change level. It further retrieves wind turbine nacelle wind speed data to calculate the wind field trigger coefficient, corrects the wind-solar inverse change level, and selects to perform energy storage freezing treatment or energy storage scheduling treatment based on the correction results, so as to realize the identification of wind-solar complementarity under the meteorological conditions of the edge of a sandstorm.

[0019] Example 1, as Figures 1 to 2As shown, a method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert area includes the following steps: Step S1: Set the particle monitoring time, collect the concentration of inhalable particulate matter in the wind and solar storage area to be tested during the particle monitoring time and analyze the particle shading trend. Based on the particle shading trend, determine whether to collect solar irradiance data and DC input power data of photovoltaic inverter. Step S2: Evaluate the shading response characteristics by combining solar irradiance data and DC input power data, generate a dust shading index by combining particle shading trends, and evaluate the dust edge status of the current wind and solar storage area based on the dust shading index. Step S3: Determine whether the wind-solar hybrid identification stage is triggered based on the state of the sandstorm edge. When the wind-solar hybrid identification stage is triggered, detect the photovoltaic output current and wind turbine pitch angle of the wind-solar storage area to be tested, and analyze the wind-solar reverse change level using the detection results. Step S4: Access the meteorological monitoring database to retrieve wind speed data of the wind turbine nacelle, calculate the wind field triggering coefficient based on the wind turbine nacelle wind speed data and correct the wind-solar inverse change level, and select energy storage freezing processing or energy storage scheduling processing based on the corrected wind-solar inverse change level.

[0020] The specific implementation is as follows: In step S1, when the dust cloud is at the edge of the wind and solar energy storage area to be tested, the concentration of air particles gradually increases and forms a local shading effect on solar radiation. The photovoltaic output may decrease in a short time. Affected by the edge airflow disturbance, the wind speed may increase synchronously, thus forming an inverse process of wind power increase and photovoltaic power decrease. In order to avoid unnecessary charging and discharging operations of the energy storage system when natural complementary conditions exist, the relationship between wind and solar power output changes is jointly determined based on the meteorological conditions at the edge of the dust cloud, and an energy storage freezing strategy is generated accordingly.

[0021] The particle monitoring time is preset and divided into multiple monitoring times. The concentration of inhalable particulate matter in the wind and solar storage area to be tested is collected by a laser scattering particulate matter online monitoring instrument. The concentrations of each inhalable particulate matter are combined into a particulate matter concentration sequence according to the time sequence. Among them, inhalable particulate matter refers to suspended particulate matter with an aerodynamic equivalent particle size of less than or equal to ten micrometers; In the particulate matter concentration sequence, the difference between adjacent inhalable particulate matter concentrations is used to obtain the particulate matter concentration change. The number of positive particulate matter concentration changes is taken as the increase count, and the ratio of the increase count to the total number of particulate matter concentration changes is taken as the increase percentage of concentration change. The average particle concentration is obtained by averaging the concentrations of each inhalable particulate matter. The average particle concentration and the percentage increase in concentration were standardized to obtain the average particle concentration coefficient and the percentage increase coefficient, respectively. Calculation of particle shading trend based on average particle concentration coefficient and rising proportion coefficient: ,in, For the tendency of particles to block light, This is the average particle concentration coefficient. This is the coefficient representing the percentage increase; The larger the value of the particle shading trend, the higher the overall level of the concentration of inhalable particulate matter during the particle monitoring period and the continuous upward trend. This indicates that the scattering and shading effect of inhalable particulate matter on solar radiation may gradually increase, and a sandstorm shading process is more likely to form over the wind and solar storage area to be tested. By comparing the preset particulate shading threshold with the particulate shading trend, we can analyze whether the wind and solar reservoir area under test is under particulate shading. If the preset particle shading threshold is greater than the particle shading trend, it is determined that the wind and solar storage area under test is not in a particle shading state. Conversely, it is determined that the wind and solar storage area to be tested is in a state of particle shading. When the wind and solar storage area under test is under particle shading, further solar irradiance data and DC input power data of photovoltaic inverters are collected to analyze the correspondence between solar irradiance changes and photovoltaic power response, and to assess the impact of particle shading on photovoltaic power generation output. Specifically, within the preset particle monitoring time, the solar irradiance data of the wind and solar storage area to be tested is retrieved through the irradiance monitoring unit. The solar irradiance data is the solar radiation power intensity received per unit area by the irradiance monitoring equipment at each monitoring time. The solar irradiance data were sorted and combined into a solar irradiance sequence according to time order. Among them, the irradiance monitoring unit refers to the solar radiation observation device set up in the wind and solar storage area to be measured, including a total solar irradiance sensor, a data acquisition module and a communication interface, which is used to monitor and store the solar radiation intensity in real time; The DC input power data corresponding to each monitoring moment is obtained through the operation monitoring interface of the photovoltaic inverter. The DC input power data is sorted and combined into a DC input power sequence according to the time order. The DC input power data represents the electrical power input to the DC side of the photovoltaic inverter by the photovoltaic modules in the wind and solar storage area under test at the corresponding monitoring time, reflecting the output changes of the photovoltaic modules when they are affected by shading.

[0022] It should be noted that the preset particle monitoring time can be set based on the aerosol changes before and after the dust cloud enters the wind and solar reservoir area to be tested, as shown in historical data. The laser scattering particulate matter online monitoring instrument is a particulate matter concentration detection device that monitors suspended particulate matter in the air in real time based on the principle of laser scattering, and is deployed in the meteorological observation tower of the wind and solar reservoir area to be tested. The standardization processing methods include, but are not limited to, standard linear transformation based on interval scaling, Z-Score standardization method based on statistics, or normalization method based on nonlinear mapping function. The application methods of standardization processing will not be elaborated here. The preset particle shading threshold can be set based on the change range of particle shading trend before the occurrence of dust weather in the historical meteorological monitoring data of the wind and solar reservoir area to be tested.

[0023] In step S2, the solar irradiance sequence and the DC input power sequence are time-aligned using each monitoring time as a time reference. The solar irradiance data at adjacent monitoring times are calculated by difference in chronological order to obtain a sequence of solar irradiance changes. At the same time, the DC input power data at adjacent monitoring times are calculated by difference to obtain a sequence of DC input power changes. Furthermore, the average value of each solar irradiance change in the solar irradiance change sequence is obtained; simultaneously, the average value of each DC input power change in the DC input power change sequence is obtained. When both the average change in DC input power and the average change in solar irradiance are negative, the ratio of the average change in DC input power to the average change in solar irradiance is used as a shading response characteristic to reflect the proportional relationship of photovoltaic output response to changes in solar irradiance. The larger the value, the greater the decrease in DC input power during the decrease in solar irradiance, and the higher the impact of particulate shading on the photovoltaic module's power output; the smaller the value, the slower the change in photovoltaic DC input power, and the weaker the impact of particulate shading on photovoltaic power output. Among them, when the average change in DC input power is negative and the average change in solar irradiance is not the same as the negative value, the current dust edge state of the wind and solar storage area to be measured is determined to be a non-dust edge formation state. The shading response coefficient is obtained after standardizing the shading response characteristics. Retrieve the preset particle shading threshold and particle shading trend from step S1, and calculate the shading disturbance factor based on the preset particle shading threshold and particle shading trend: ,in, As the shading disturbance factor, To preset the particle shading threshold, The trend is towards particle-induced light shading; The product of the shading disturbance factor and the shading response coefficient is used as the dust shading index. The dust shading index reflects the comprehensive degree of influence of the particulate shading process on photovoltaic output. The larger the value, the more obvious the accumulation of particulate matter, and the photovoltaic DC input power has shown a decreasing response with the change of solar irradiance. The smaller the value, the less obvious the change of photovoltaic DC input power, and the weaker the impact of particulate shading on photovoltaic output. The dust shading index is compared with the preset dust edge shading range to assess the current dust edge status of the wind and solar storage area to be tested. If the dust shading index falls within the preset dust edge shading range, the dust edge state of the current wind and solar reservoir area to be tested is determined to be the dust edge formation state, indicating that the particle shading process of the wind and solar reservoir area to be tested has had a significant impact on solar radiation, and the area is at the edge stage of dust air mass entering or passing by. Otherwise, the current dust edge state of the wind and solar reservoir area to be tested is determined to be a non-dust edge state; It should be noted that the preset dust edge shading range is used to distinguish between the dust edge formation state and the non-dust edge state, and can be set according to the distribution of the dust shading index changes before and after the occurrence of dust weather in the historical operation data of the wind and solar storage area to be tested.

[0024] By integrating information on changes in air particles with photovoltaic power output response, misjudgments caused by relying solely on particle concentration or power changes can be avoided. This allows for a more accurate identification of the edge stage of dust storms entering the power plant area, providing a reliable basis for subsequent wind-solar hybrid identification and energy storage scheduling strategies.

[0025] In step S3, when step S2 determines that the current dust edge state of the wind-solar-storage area to be tested is the dust edge formation state, the wind-solar complementarity identification stage is triggered. This stage is used to further determine whether the dust shading process has already caused the reverse change process of wind power output increase and photovoltaic power output decrease at the power station operation level, thereby providing a basis for the generation of subsequent energy storage freezing strategies.

[0026] In the wind-solar hybrid identification phase, the photovoltaic output current of each photovoltaic inverter in the wind-solar-storage area under test is first collected through the operation monitoring interface of the photovoltaic inverter. The photovoltaic output current refers to the output current formed on the DC side of the inverter after the photovoltaic module array is combined, and its value reflects the actual power generation capacity of the photovoltaic module under the current solar irradiance conditions.

[0027] It should be noted that the operation monitoring interface refers to the data communication interface module deployed in wind turbines and photovoltaic inverters, which is used to collect, analyze and transmit equipment operation status parameters in real time, and is built based on industrial communication protocols.

[0028] When airborne particles block solar radiation, the effective irradiance received by photovoltaic modules decreases, and the photovoltaic output current decreases accordingly. Therefore, the photovoltaic output current reflects the degree of impact of sand and dust shading on photovoltaic power generation output.

[0029] Within the preset wind and solar identification time, the photovoltaic output current is collected with each monitoring time as the time reference, and the photovoltaic output current sequence is formed by combining them in time order. The difference between the photovoltaic output current at adjacent monitoring times in the photovoltaic output current sequence is calculated to obtain the photovoltaic current change sequence. The average value of each value in the photovoltaic current change sequence is taken to obtain the average photovoltaic current change. The average photovoltaic current change is used to characterize the overall change trend of photovoltaic output current within the identification time window.

[0030] When the average change in photovoltaic current is negative and the absolute value is large, it indicates that the overall photovoltaic output current is showing a significant downward trend, suggesting that the sandstorm shading process has had a substantial impact on photovoltaic power generation output.

[0031] Meanwhile, the pitch angles of each wind turbine in the wind and solar storage area under test are collected through the operation monitoring interface of the wind turbine control system. The wind turbine pitch angle is the installation angle of the wind turbine blades relative to the incoming wind direction, which is automatically adjusted by the wind turbine pitch control system according to the real-time wind speed and power generation load to optimize wind energy capture efficiency.

[0032] When the incoming wind speed increases, the wind turbine control system adjusts the pitch angle of the wind turbine to maintain the unit operating near the optimal power point. Therefore, the change in pitch angle reflects the change in wind speed and the trend of wind turbine output adjustment.

[0033] It should be noted that the wind turbine pitch control system refers to the blade angle adjustment control system installed in the wind turbine generator set, which is used to automatically adjust the installation angle of the wind turbine blades relative to the incoming wind direction according to the real-time wind speed, power generation and unit operating status.

[0034] Within the preset wind and solar recognition time, the wind turbine pitch angle is collected using the same monitoring time as the photovoltaic output current as the time reference, and the pitch angle sequence is formed by combining them in chronological order. The pitch angle at adjacent monitoring times in the pitch angle sequence is calculated by difference to obtain the pitch angle change sequence. When the pitch angle change is negative, it indicates that the wind turbine blade pitch angle has decreased, the blade frontal area has increased, and the wind energy capture capability has been enhanced. When the pitch angle change is positive, it indicates that the wind turbine blade pitch angle has increased, and the wind energy capture capability has weakened.

[0035] The average value of each value in the pitch angle change sequence is taken to obtain the average pitch angle change, which reflects the overall trend of the wind turbine blade angle within the identification time window. When the average pitch angle change is negative and the larger the absolute value, it indicates that the wind turbine pitch angle is decreasing overall, indicating that the wind turbine is actively increasing its wind energy capture capability, corresponding to an operating state of increased wind speed or increased wind power output.

[0036] After obtaining the average change in photovoltaic current and the average change in pitch angle, in order to eliminate the dimensional differences caused by different power plant scales and different wind turbine models, the average change in photovoltaic current and the average change in pitch angle are standardized to obtain the photovoltaic descent coefficient and the wind power rise coefficient.

[0037] The photovoltaic decline coefficient is used to characterize the degree of decline in photovoltaic output current. The larger the value, the more obvious the decline in photovoltaic output current. The wind power increase coefficient is used to characterize the degree of reduction in wind turbine pitch angle. The larger the value, the greater the wind turbine blade's wind-facing capability and the more obvious the upward trend in wind power output.

[0038] Based on this, the wind-solar inverse variation index is calculated using the photovoltaic decline coefficient and the wind power rise coefficient: ; in, This indicates the inverse relationship index between wind and light. Indicates the photovoltaic degradation coefficient. This represents the wind power upscaling factor.

[0039] The wind-solar inverse relationship index comprehensively reflects the declining trend of photovoltaic power output and the rising trend of wind power output. When the value of the wind-solar inverse relationship index is larger, it indicates that the photovoltaic output current is decreasing significantly and the wind turbine pitch angle is decreasing simultaneously, indicating that the wind power output is increasing while the photovoltaic output is decreasing, and the wind and solar power outputs are showing an inverse relationship. When the value of the wind-solar inverse relationship index is smaller, it indicates that the wind and solar power output changes do not have an inverse relationship.

[0040] Subsequently, the wind-sunlight reversal index is compared with the preset wind-sunlight reversal level range, and the wind-sunlight reversal level is divided according to the comparison results.

[0041] When the wind and solar reverse change index is in the low level range, the wind and solar reverse change level is determined to be the weak reverse change level. When the wind-solar inversion index is in the medium level range, the wind-solar inversion level is determined to be the medium inversion level. When the wind-solar inversion index is in the high-level range, the wind-solar inversion level is determined to be a strong inversion level.

[0042] It should be noted that the preset wind-solar inversion level range refers to the set of numerical ranges used to classify the wind-solar inversion index. This range is pre-set after statistical analysis of historical operating data of the wind-solar reservoir area under test, and is used to reflect the numerical distribution range of different intensities of wind-solar inversion processes during actual operation. Specifically, firstly, the photovoltaic output current, wind turbine pitch angle, and corresponding meteorological monitoring data of the wind-solar reservoir area under test during its historical operating cycle are retrieved. Then, the wind-solar inversion index corresponding to each historical time window is calculated using the same calculation method as in step S3, resulting in a historical wind-solar inversion index sample set. Subsequently, probability distribution statistics are performed on this sample set, and multiple boundary thresholds are determined based on the distribution characteristics of the sample data. For example, the 30th percentile of the historical wind-solar inversion index can be used as the upper limit threshold of the low-level range, and the 70th percentile as the upper limit threshold of the medium-level range, thus forming three continuous numerical ranges: low-level, medium-level, and high-level ranges.

[0043] By using the above methods, the relationship between wind and solar power output changes is quantitatively analyzed using two types of operating parameters: photovoltaic output current and wind turbine pitch angle. This allows for the identification of whether an inverse change process of increasing wind power output and decreasing photovoltaic output has already occurred under the meteorological conditions at the edge of the sandstorm. Based on this, a wind-solar inverse change level is generated, providing a basis for the selection of subsequent energy storage freezing or energy storage dispatch strategies.

[0044] In step S4, after obtaining the wind-solar inversion level in step S3, in order to further determine whether the inversion is caused by the actual wind speed increase rather than by the wind turbine control strategy or local disturbance, the wind turbine nacelle wind speed data is retrieved by accessing the meteorological monitoring database to correct the wind-solar inversion level, thereby generating a more accurate basis for energy storage scheduling decisions.

[0045] Specifically, the meteorological monitoring database of the wind and solar power storage area to be tested is accessed, and the wind speed data of each wind turbine nacelle recorded by the anemometer on top of the nacelle within a preset wind field identification time is retrieved. The wind turbine nacelle wind speed refers to the incoming wind speed measured by the anemometer installed on top of the nacelle, reflecting the actual incoming wind speed level at the turbine's height. A higher wind turbine nacelle wind speed indicates richer incoming wind energy resources and higher power generation potential for the wind turbine.

[0046] It should be noted that the meteorological monitoring database refers to the centralized storage unit of meteorological data deployed in the operation and monitoring system of the wind and solar power storage area, which is used to uniformly collect, store and manage environmental data collected by various meteorological monitoring equipment in the area; the anemometer refers to the wind speed measuring device installed on the top of the wind turbine nacelle, which is used to measure the incoming air velocity at the height of the wind turbine nacelle in real time.

[0047] Using the monitoring time in step S3 as a unified time reference, the retrieved wind turbine nacelle wind speed data is time-aligned and combined in chronological order to form a nacelle wind speed sequence. The wind speed data of adjacent monitoring times in the nacelle wind speed sequence are interpolated to obtain a wind speed change sequence. The average value of the values ​​in the wind speed change sequence is taken to obtain the average wind speed change. The average wind speed change represents the overall trend of nacelle wind speed change within the preset wind field identification time. The larger the value, the more obvious the upward trend of wind speed.

[0048] Simultaneously, the average value of each wind speed in the nacelle wind speed sequence is taken to obtain the average nacelle wind speed. The average nacelle wind speed represents the average incoming wind speed within the identification time window, and its value is used to reflect the overall wind energy resource level of the wind farm at present. The higher the average nacelle wind speed, the higher the wind energy utilization potential of the wind farm.

[0049] To comprehensively reflect wind speed levels and their changing trends, the average nacelle wind speed and the change in average wind speed were standardized to obtain a wind speed level coefficient and a wind speed change coefficient. The wind speed level coefficient represents the overall level of wind energy resources in the current wind farm; a larger value indicates a higher overall wind speed level. The wind speed change coefficient represents the upward trend of wind speed; a larger value indicates a more significant increase in wind speed.

[0050] Based on this, the wind field triggering coefficient is calculated using an exponential triggering model based on the wind speed horizontal coefficient and the wind speed variation coefficient: ; in, This is the wind field triggering coefficient. This is the horizontal coefficient of wind speed. Wind speed variation coefficient, It is a natural constant.

[0051] The wind farm triggering coefficient comprehensively assesses the degree to which wind speed levels and growth trends support the growth of wind power output. A large wind farm triggering coefficient indicates that the wind speed level is high and trending upward, suggesting that the growth in wind power output is supported by real wind farm conditions. Conversely, a small wind farm triggering coefficient indicates that the wind speed level or growth trend is insufficient, and the change in wind power output may only be caused by adjustments to the unit control strategy or short-term disturbances.

[0052] Subsequently, the wind field triggering coefficient and the wind-solar inversion index obtained in step S3 are coupled for calculation to correct the wind-solar inversion level. Specifically, the corrected inversion index is calculated based on the wind-solar inversion index and the wind field triggering coefficient: ; in, This is the corrected inverse landscape variation index. This is the inverse variation index of wind and light. This is the wind field triggering coefficient.

[0053] The corrected wind-solar inverse variation index reflects the declining trend of solar power output, changes in wind turbine operating status, and the degree of increase in actual wind speed. When the corrected wind-solar inverse variation index value is large, it indicates that the decline in solar power output and the increase in wind speed occur simultaneously, and the inverse variation of wind and solar power output is supported by real meteorological conditions.

[0054] Furthermore, the corrected reverse change index is compared with the preset reverse change level range for landscape, and the corrected reverse change level for landscape is redefined.

[0055] When the corrected wind-solar inverse change level is strong inverse change level or medium inverse change level, it is determined that there is a significant wind-solar natural complementary process in the wind-solar storage area to be tested, and energy storage freezing treatment is performed, that is, the charging and discharging operation of the energy storage system is suspended within the preset freezing time window, and the increase in wind power output is used to compensate for the decrease in photovoltaic output.

[0056] When the corrected wind-solar inverse change level is a weak inverse change level, it is determined that the wind and solar power output changes are insufficient to form effective complementarity, and energy storage dispatch processing is executed, that is, the charging and discharging regulation is carried out through the energy storage system to maintain the power output of the wind-solar-storage field under test.

[0057] By introducing nacelle wind speed data to perform meteorological verification of the wind-solar inverse relationship, it is possible to effectively distinguish between the wind-solar complementarity process caused by the enhancement of the real wind field and the pseudo-changes caused by the adjustment of wind turbine operation strategies. This can avoid unnecessary discharge behavior of the energy storage system when natural complementarity conditions exist, and improve the overall scheduling efficiency of the wind-solar-storage system.

[0058] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0059] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0060] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0061] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0062] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert-Gobi wasteland, characterized by: Includes the following steps: Step S1: Set the particle monitoring time, collect the concentration of inhalable particulate matter in the wind and solar storage area to be tested during the particle monitoring time and analyze the particle shading trend. Based on the particle shading trend, determine whether to collect solar irradiance data and DC input power data of photovoltaic inverter. Step S2: Evaluate the shading response characteristics by combining solar irradiance data and DC input power data, generate a dust shading index by combining particle shading trend, and evaluate the dust edge status of the current wind and solar storage area based on the dust shading index. Step S3: Determine whether the wind-solar hybrid identification stage is triggered based on the state of the sandstorm edge. When the wind-solar hybrid identification stage is triggered, detect the photovoltaic output current and wind turbine pitch angle of the wind-solar storage area to be tested, and analyze the wind-solar reverse change level using the detection results. Step S4: Access the meteorological monitoring database to retrieve wind speed data of the wind turbine nacelle, calculate the wind field triggering coefficient based on the wind turbine nacelle wind speed data and correct the wind-solar inverse change level, and select energy storage freezing processing or energy storage scheduling processing based on the corrected wind-solar inverse change level.

2. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert-golam area according to claim 1, characterized in that: In step S1, the particle monitoring time is preset and divided into multiple monitoring times. The concentration of inhalable particulate matter in the wind and solar storage area to be tested is collected by a laser scattering particulate matter online monitoring instrument. The concentrations of each inhalable particulate matter are combined into a particulate matter concentration sequence according to the time sequence. In the particulate matter concentration sequence, the difference between adjacent inhalable particulate matter concentrations is used to obtain the particulate matter concentration change. The number of positive particulate matter concentration changes is taken as the increase count, and the ratio of the increase count to the total number of particulate matter concentration changes is taken as the increase percentage of concentration change. The average particle concentration is obtained by averaging the concentrations of each inhalable particulate matter. The particle shading trend was calculated based on the average particle concentration and the percentage increase in concentration. If the particle shading trend is less than the preset particle shading threshold, it is determined that the wind and solar storage area under test is not in a particle shading state. Conversely, if the wind and solar storage area to be tested is determined to be in a state of particle shading.

3. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert region, as described in claim 2, is characterized in that: In step S1, when the wind and solar storage area to be tested is in a state of particle shading, solar irradiance data and DC input power data of the photovoltaic inverter are further collected. Specifically, within the preset particle monitoring time, the solar irradiance data of the wind and solar storage area to be tested is retrieved through the irradiance monitoring unit, and the solar irradiance data is sorted and combined into a solar irradiance sequence according to the time order. The DC input power data corresponding to each monitoring moment is obtained through the operation monitoring interface of the photovoltaic inverter. The DC input power data is sorted and combined into a DC input power sequence according to the time order.

4. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert region, as described in claim 3, is characterized in that: In step S2, the solar irradiance sequence and the DC input power sequence are time-aligned using each monitoring time as a time reference. The solar irradiance data at adjacent monitoring times are calculated by difference in chronological order to obtain a sequence of solar irradiance changes. At the same time, the DC input power data at adjacent monitoring times are calculated by difference to obtain a sequence of DC input power changes. Statistical calculations were performed on the solar irradiance variation sequence and the DC input power variation sequence to obtain the average solar irradiance variation and the average DC input power variation. When both the average change in DC input power and the average change in solar irradiance are negative, the ratio of the average change in DC input power to the average change in solar irradiance is used as the shading response characteristic.

5. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert-golam area according to claim 1, characterized in that: In step S2, a preset particle shading threshold and particle shading trend are retrieved, and a shading disturbance factor is calculated based on the preset particle shading threshold and particle shading trend. After standardizing the shading response characteristics, the shading response coefficient is obtained. The product of the shading disturbance factor and the shading response coefficient is used as the dust shading index. The dust shading index is compared with the preset dust edge shading range to assess the current dust edge status of the wind and solar storage area to be tested. If the dust shading index falls within the preset dust edge shading range, then the dust edge state of the current wind and solar storage area to be tested is determined to be the dust edge formation state. Otherwise, the current dust edge state of the wind and solar storage area to be tested is determined to be a non-dust edge state.

6. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert area, as described in claim 5, is characterized in that: In step S3, when step S2 determines that the current dust edge state of the wind-solar reservoir area to be tested is the dust edge formation state, the wind-solar complementarity identification stage is triggered. In the wind-solar hybrid identification stage, the photovoltaic output current of each photovoltaic inverter in the wind-solar storage area to be tested is first collected through the operation monitoring interface of the photovoltaic inverter. Photovoltaic output current refers to the output current formed on the DC side of the inverter after the photovoltaic module array is combined; Within the preset wind and solar identification time, the photovoltaic output current is collected based on each monitoring time and combined in chronological order to form a photovoltaic output current sequence. The difference between the photovoltaic output currents at adjacent monitoring times in the photovoltaic output current sequence is calculated and the average value is taken to obtain the average change of photovoltaic current; The pitch angle of each wind turbine in the wind and solar storage area under test is collected through the operation monitoring interface of the wind turbine control system. The pitch angle of a wind turbine is the installation angle of the wind turbine blades relative to the direction of the incoming airflow.

7. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert-golam area according to claim 6, characterized in that: In step S3, within the preset wind and solar identification time, the wind turbine pitch angle is collected and a pitch angle sequence is formed using the same monitoring time as the photovoltaic output current as the time reference. The pitch angle is calculated by performing a difference calculation on the pitch angles at adjacent monitoring times in the pitch angle sequence and taking the average value to obtain the average change in pitch angle. The average change in photovoltaic current and the average change in pitch angle are standardized to obtain the photovoltaic descent coefficient and the wind power rise coefficient. Calculate the inverse relationship index between wind and solar power using the photovoltaic power decline coefficient and the wind power rise coefficient: ; in, This indicates the inverse relationship index between wind and light. Indicates the photovoltaic degradation coefficient. Indicates the wind power rise coefficient; The wind-sunlight inversion index is compared with the preset wind-sunlight inversion level range, and the wind-sunlight inversion level is divided into weak inversion level, moderate inversion level, and strong inversion level based on the comparison results.

8. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert-golam area according to claim 7, characterized in that: In step S4, the meteorological monitoring database of the wind and solar storage area to be tested is accessed, and the wind speed data of the wind turbine nacelle recorded by the wind speed sensor on the top of each wind turbine nacelle within the preset wind field identification time is retrieved. The wind turbine nacelle wind speed refers to the incoming wind speed measured by an anemometer installed on the top of the wind turbine nacelle. The retrieved wind turbine nacelle wind speed data is time-aligned to form a nacelle wind speed sequence. The average wind speed change is obtained by performing difference calculations on the wind speed data of the wind turbine nacelle at adjacent monitoring times in the nacelle wind speed sequence and taking the average value. The average nacelle wind speed is obtained by averaging the nacelle wind speed data of each wind turbine in the nacelle wind speed sequence.

9. The method for generating a wind-solar-storage complementary operation strategy for a typical meteorological scenario in a desert region, as described in claim 8, is characterized in that: In step S4, the average cabin wind speed and the average wind speed change are standardized to obtain the wind speed level coefficient and the wind speed change coefficient. The wind field triggering coefficient is calculated using an exponential triggering model based on the wind speed horizontal coefficient and the wind speed variation coefficient. The corrected wind-solar inverse variation index is calculated based on the wind-solar inverse variation index and the wind field triggering coefficient. The corrected wind and light inversion index is compared with the preset wind and light inversion level range, and the corrected wind and light inversion level is redefined. When the corrected wind-solar inverse change level is strong inverse change level or medium inverse change level, it is determined to implement energy storage freeze treatment, that is, to suspend the charging and discharging operation of the energy storage system and use the increase in wind power output to naturally compensate for the decrease in photovoltaic output. When the corrected wind-solar inverse change level is a weak inverse change level, it is determined to execute energy storage dispatch processing, that is, to regulate the charging and discharging through the energy storage system to maintain the power output of the wind-solar-storage field under test.

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