A method, device and equipment for determining a wind-solar capacity matching strategy

Abstract

This invention provides a method, apparatus, and equipment for determining a wind-solar capacity allocation strategy. The method includes: acquiring the current solar radiation intensity and current wind speed of a preset area during a preset time period; calculating the similarity between the current solar radiation intensity and the solar radiation intensity corresponding to multiple typical days to obtain multiple first similarities; calculating the similarity between the current wind speed and the wind speed corresponding to multiple typical days to obtain multiple second similarities; determining a target typical day based on the multiple first similarities and multiple second similarities, with each typical day corresponding to a wind-solar capacity allocation strategy; setting the wind-solar capacity allocation strategy corresponding to the target typical day as the current wind-solar capacity allocation strategy; and adjusting the electrical load required for hydrogen production and / or methanol production by the power generation system in the preset area according to the current wind-solar capacity allocation strategy. The solution of this invention achieves dynamic coordination of multiple energy sources and hydrogen energy, as well as efficient renewable energy consumption and fluctuation mitigation.

Description

Technical Field

[0001] This invention relates to the field of new energy system planning technology, and in particular to a method, apparatus and equipment for determining a wind and solar capacity allocation strategy. Background Technology

[0002] Wind-solar coupled hydrogen production systems are an important part of integrated energy production units, coordinating the dynamic matching of wind, solar, and hydrogen energy equipment. However, the output characteristics of wind and solar power generation exhibit significant uncertainties due to the spatiotemporal dynamic changes in natural conditions such as wind speed and sunlight. To address this issue, existing research has largely focused on the static coordination of a single energy source with hydrogen energy, with insufficient research on the real-time response mechanism and multi-timescale collaborative control of wind-solar-hydrogen dynamic coupling. Furthermore, it has failed to address the intermittent nature of wind-solar complementary hydrogen production from the perspectives of capacity allocation and scheduling. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for determining the wind and solar capacity ratio strategy, which realizes the dynamic coordination of multiple energy sources and hydrogen energy, as well as the real-time response mechanism and multi-timescale collaborative control and scheduling of wind and solar energy complementary hydrogen production.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0005] In a first aspect, embodiments of the present invention provide a method for determining a wind-solar capacity allocation strategy, comprising: obtaining the current solar radiation intensity and current wind speed of a preset area during a current preset time period;

[0006] The similarity between the current solar radiation intensity and the solar radiation intensity corresponding to multiple typical days is calculated to obtain multiple first similarity scores.

[0007] The current wind speed is compared with the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

[0008] Based on the multiple first similarities and multiple second similarities, a target typical day is determined, and each typical day corresponds to a landscape matching strategy;

[0009] Set the wind and light ratio strategy corresponding to the target typical day as the current wind and light ratio strategy;

[0010] Adjust the electrical load required for the power generation system in the preset area to produce hydrogen and / or methanol according to the current wind-solar ratio strategy.

[0011] Optionally, the current solar radiation intensity is compared with the solar radiation intensity corresponding to multiple typical days to obtain multiple first similarities, including:

[0012] Obtain the spatial illumination distribution image of the current preset time period in the preset area;

[0013] The spatial illumination distribution image is processed for grayscale and features are extracted to obtain the current illumination radiation intensity vector;

[0014] Feature extraction was performed on the solar radiation intensity corresponding to multiple typical days to obtain multiple typical solar radiation intensity vectors.

[0015] The current light radiation intensity vector is sequentially compared with multiple typical light radiation intensity vectors to calculate the cosine similarity, resulting in multiple first similarity values.

[0016] Optionally, the current wind speed is compared with the wind speeds corresponding to multiple typical days to obtain multiple second similarities, including:

[0017] Get the current wind speed in the preset area;

[0018] The current wind speed is subtracted from the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

[0019] Optionally, the target typical day is determined based on the plurality of first similarities and the plurality of second similarities, including:

[0020] Set the typical day corresponding to the largest value among the plurality of first similarities and / or the largest value among the plurality of second similarities as the target typical day; and / or,

[0021] For each typical day, the first similarity and the second similarity corresponding to the typical day are added together, and the typical day corresponding to the maximum value of the sum is set as the target typical day.

[0022] Optionally, each wind-solar power allocation strategy corresponds to a wind-solar power allocation value, which represents the wind-solar power allocation ratio on a typical day and is determined through the following process:

[0023] Acquire the target solar radiation intensity and target wind speed within the target time period;

[0024] Clustering the target light radiation intensity and target wind speed yields multiple typical days;

[0025] Based on the preset wind-solar power ratio data, calculate the wind and solar power load for each typical day;

[0026] Based on the wind and solar power load, determine the wind-solar power ratio for a typical day.

[0027] Optionally, the target illumination intensity and target wind speed are clustered to obtain multiple typical days, including:

[0028] The target illumination intensity and target wind speed are preprocessed to obtain equidistant grid data;

[0029] The number of clusters K is determined using the elbow rule; clustering is performed based on the number of clusters K and the equidistant grid data to obtain the solar radiation intensity curve and wind speed curve;

[0030] Several typical days were determined based on the aforementioned solar radiation intensity curve and wind speed curve.

[0031] Optionally, based on preset wind-solar power ratio data, the wind and solar power load for each typical day is calculated, including:

[0032] The wind power load is determined based on the wind power generation model and the wind parameters corresponding to a typical day.

[0033] The photovoltaic load is determined based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day.

[0034] Based on multiple wind-solar ratio values, wind power load, and photovoltaic power load in the preset wind-solar ratio data, calculate the wind and solar power load for each typical day;

[0035] Based on the aforementioned wind and solar power load, determine the wind-solar power ratio for a typical day, including:

[0036] The preset wind-solar ratio value corresponding to the maximum value among multiple wind and solar power loads is determined as the wind-solar ratio value for a typical day.

[0037] Optionally, adjusting the electrical load required for hydrogen and / or methanol production by the power generation system in the preset area according to the current wind-solar ratio strategy includes:

[0038] The first wind-solar power ratio strategy includes: adjusting the wind-solar power supply ratio according to the preset first wind-solar power ratio value, combined with supercritical CO2 Brayton cycle units, to maintain the minimum electrical load required for hydrogen production and / or methanol production.

[0039] The second wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset second wind-solar power supply ratio value, and supplying power according to the minimum electrical load required for methanol production and the maximum electrical load required for hydrogen production;

[0040] The third wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset third wind-solar power supply ratio value, combining short-term energy storage equipment to smooth power fluctuations, and supplying power according to the highest electrical load required for hydrogen production.

[0041] The fourth wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset fourth wind-solar power supply ratio value, and supplying power according to the highest electrical load required for hydrogen production and / or methanol production.

[0042] Secondly, embodiments of the present invention also provide a device for determining a wind-solar capacity ratio strategy, comprising: an acquisition module, used to acquire the current solar radiation intensity and current wind speed of a preset area during a current preset time period;

[0043] The first processing module is used to calculate the similarity between the current light radiation intensity and the light radiation intensity corresponding to multiple typical days to obtain multiple first similarities;

[0044] The second processing module is used to calculate the similarity between the current wind speed and the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

[0045] The third processing module is used to determine the target typical day based on the multiple first similarities and multiple second similarities, with each typical day corresponding to a landscape matching strategy;

[0046] The fourth processing module is used to set the wind-solar ratio strategy corresponding to the target typical day as the current wind-solar ratio strategy;

[0047] The fifth processing module is used to adjust the electrical load required for the power generation system in the preset area to produce hydrogen and / or methanol according to the current wind-solar ratio strategy.

[0048] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for determining the wind-solar capacity allocation strategy as described in the first aspect.

[0049] The above-described solution of the present invention has at least the following beneficial effects:

[0050] The above-mentioned solution of the present invention obtains multiple first similarities and multiple second similarities based on the current solar radiation intensity and current wind speed, and then determines the wind-solar ratio strategy corresponding to the target typical day based on the multiple first similarities and multiple second similarities. This realizes a real-time response mechanism and multi-timescale collaborative control and scheduling of wind and solar energy complementary hydrogen production. According to the wind-solar ratio strategy, the electrical load required for the power generation system in the preset area to produce hydrogen and / or produce methanol is adjusted, realizing the dynamic coordination of multiple energy sources and hydrogen energy, and the efficient absorption and fluctuation smoothing of renewable energy. Attached Figure Description

[0051] Figure 1 This is a flowchart of an embodiment of the method for determining the wind-solar capacity ratio strategy of the present invention;

[0052] Figure 2 This is a specific application embodiment of the present invention;

[0053] Figure 3 It is a curve showing how the intensity of light radiation changes over time;

[0054] Figure 4 It is a curve showing the change in wind speed over time.

[0055] Figure 5 This is a schematic diagram of an embodiment of the device for determining the wind-solar capacity ratio strategy according to the present invention;

[0056] Figure 6 This is a schematic diagram of the structure of a physical embodiment of the electronic device of the present invention. Detailed Implementation

[0057] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0058] like Figure 1 As shown, an embodiment of the present invention proposes a flowchart of a method for determining a wind-solar capacity allocation strategy. The method of this embodiment includes:

[0059] Step 11: Obtain the current solar radiation intensity and current wind speed of the preset area during the current preset time period;

[0060] Step 12: Calculate the similarity between the current light radiation intensity and the light radiation intensity corresponding to multiple typical days to obtain multiple first similarities;

[0061] Step 13: Calculate the similarity between the current wind speed and the wind speeds corresponding to multiple typical days to obtain multiple second similarities;

[0062] Step 14: Based on the multiple first similarities and multiple second similarities, determine the target typical day, and each typical day corresponds to a landscape matching strategy;

[0063] Step 15: Set the wind and light ratio strategy corresponding to the target typical day as the current wind and light ratio strategy;

[0064] Step 16: Adjust the electrical load required for the power generation system in the preset area to produce hydrogen and / or methanol according to the current wind-solar ratio strategy.

[0065] The output characteristics of wind and solar power generation exhibit significant uncertainties due to the spatiotemporal dynamic changes in natural conditions such as wind speed and sunlight. Therefore, in this embodiment, a wind-solar capacity allocation strategy can be constructed using the MATLAB platform. This strategy not only integrates both wind and solar power—two renewable energy sources—but also considers their power generation characteristics, technological feasibility, economic optimization, and policy guidance under different climatic conditions, providing support for building a highly resilient, low-carbon energy internet.

[0066] Since the solar radiation intensity and wind speed are not necessarily the same in different locations every day, the solar radiation intensity and wind speed can be monitored in a preset area and time period to determine which typical day the solar radiation intensity and wind speed of a certain time period are closer to. Then, the power load required for the power generation system in the preset area to produce hydrogen and / or methanol can be adjusted according to the wind and solar power ratio strategy corresponding to the target typical day.

[0067] For step 11, as an example, the preset area could be a solar radiation intensity monitoring point installed at a medium distance in a photovoltaic power plant, and similarly, a wind speed monitoring point installed at a medium distance in a wind power plant.

[0068] As an example, the current preset time period can be the entire day, or it can be the morning or afternoon of the day.

[0069] Taking a preset area as an example with fixed monitoring points installed at equal intervals, and the current preset time period as the entire day, the following method can be used to obtain the current solar radiation intensity and current wind speed: The solar radiation intensity and wind speed from 5:00 AM to 8:00 AM (a fixed time period) can be obtained from the fixed monitoring points. The current solar radiation intensity is determined based on the average value of the monitored solar radiation intensity. The current wind speed is determined based on the average value of the monitored wind speed.

[0070] In some embodiments, the average solar radiation intensity and average wind speed of the day can be calculated by combining the weather forecast for the day, and the average solar radiation intensity and average wind speed of the day can be used as the current solar radiation intensity and current wind speed.

[0071] In some embodiments, the light radiation intensity and wind speed can be monitored in real time. If the fluctuation of the light radiation intensity or wind speed is less than a preset fluctuation value, the currently monitored light radiation intensity and wind speed are taken as the current light radiation intensity and current wind speed. If the fluctuation of the light radiation intensity or wind speed is greater than or equal to the preset fluctuation value, the current light radiation intensity and current wind speed are adjusted.

[0072] In some optional implementations, step 12, which calculates the similarity between the current solar radiation intensity and the solar radiation intensity corresponding to multiple typical days to obtain multiple first similarities, may include:

[0073] Step 121: Obtain the spatial illumination distribution image of the current preset time period in the preset area;

[0074] Step 122: Perform grayscale processing and feature extraction on the spatial illumination distribution image to obtain the current illumination radiation intensity vector;

[0075] Step 123: Extract features from the solar radiation intensity corresponding to multiple typical days in sequence to obtain multiple typical solar radiation intensity vectors;

[0076] Step 124: Calculate the cosine similarity between the current illumination radiation intensity vector and multiple typical illumination radiation intensity vectors in sequence to obtain multiple first similarity values.

[0077] In this embodiment, the current illumination radiation intensity can be obtained from a spatial illumination distribution image.

[0078] As an example, video or images of the current light radiation intensity can be obtained through cameras at fixed monitoring points.

[0079] As an example, the spatial illumination distribution image can be sequentially processed by grayscale, image enhancement, and feature extraction to obtain the current illumination radiation intensity vector. Grayscale conversion and enhancement avoid color interference and noise, making the features more stable and discriminative, which is beneficial for feature extraction. Specifically, grayscale conversion reduces computational complexity and noise interference; image enhancement improves image contrast and suppresses noise, referencing Gaussian filtering and median filtering algorithms; feature extraction focuses on key information, reduces dimensionality, and abstracts features, referencing edge detection, corner detection, or deep feature extraction algorithms.

[0080] As an example, edge and gradient features (e.g., Canny edge detection) or frequency-domain or transform-based features (e.g., Fourier transform) can be extracted from a spatial illumination distribution image to obtain the current illumination radiant intensity vector. Alternatively, edge and gradient features and frequency-domain or transform-based features can be extracted and concatenated to obtain the current illumination radiant intensity vector.

[0081] The illumination intensity corresponding to multiple typical days can be pre-stored grayscale images for rapid feature extraction and convenient viewing and maintenance by administrators. As an example, feature extraction can be performed on the pre-stored grayscale images of illumination intensity corresponding to multiple typical days, using edge and gradient features or features based on the frequency domain or transform, to obtain the current illumination intensity vector. Alternatively, edge and gradient features and features based on the frequency domain or transform can be extracted, and the features can be concatenated to obtain the current illumination intensity vector.

[0082] After obtaining the current illumination radiation intensity vector and several typical illumination radiation intensity vectors, calculate the cosine similarity sequentially. See below for reference:

[0083]

[0084] Where A represents the current light radiation intensity vector, B i Let represent the i-th typical light radiation intensity vector, where i is greater than 1. The larger the cosine similarity (i.e., the first similarity), the closer the directions of the two vectors are, and the higher the similarity.

[0085] As an example, the current light radiation intensity vector is g1. There are four typical days. The light radiation intensity vector corresponding to the first typical day is p1, the second typical day is p2, the third typical day is p3, and the fourth typical day is p4. The similarity calculated using the above method is 0.1 for the first typical day, 0.2 for the second, 0.6 for the third, and 0.4 for the fourth. Specifically, the first similarity corresponds to the first typical day, the second to the second, the third to the third, and the fourth to the fourth.

[0086] In some alternative implementations, step 13 involves calculating the similarity between the current wind speed and the wind speeds corresponding to multiple typical days to obtain multiple second similarities, including:

[0087] Step 131: Obtain the current wind speed in the preset area;

[0088] Step 132: Subtract the current wind speed from the wind speeds corresponding to multiple typical days in sequence to obtain multiple second similarities.

[0089] As an example, wind speed monitoring points can be installed at equal intervals in a grid pattern at wind farms. Wind speed can be monitored using methods such as cup anemometers, thermal anemometers, or ultrasonic anemometers.

[0090] As an example, the wind speeds corresponding to multiple typical days can be stored in the data in advance.

[0091] The second similarity is obtained by successively subtracting the current wind speed from the wind speeds corresponding to multiple typical days and taking the reciprocal of the absolute value of the difference. The larger the second similarity, the closer the wind speed of the typical day corresponding to that value is to the current preset time period.

[0092] As an example, the current wind speed is f1. There are four typical days: the wind speed corresponding to the first typical day is d1, the wind speed corresponding to the second typical day is d2, the wind speed corresponding to the third typical day is d3, and the wind speed corresponding to the fourth typical day is d4. The second similarity calculated using the above method is 0.5 for the first typical day, 0.6 for the second typical day, 0.7 for the third typical day, and 0.8 for the fourth typical day. Specifically, the first second similarity corresponds to the first typical day, the second second similarity corresponds to the second typical day, the third second similarity corresponds to the third typical day, and the fourth second similarity corresponds to the fourth typical day.

[0093] In some alternative implementations, step 14 involves determining a target typical day based on the plurality of first similarities and the plurality of second similarities, with each typical day corresponding to a landscape matching strategy, including:

[0094] Step 141, set the typical day corresponding to the largest value among the plurality of first similarities and / or the largest value among the plurality of second similarities as the target typical day; and / or,

[0095] Step 142: For each typical day, add the first similarity and the second similarity corresponding to the typical day, and set the typical day corresponding to the maximum value of the sum as the target typical day.

[0096] Using the above example, if multiple first similarities are 0.1, 0.2, 0.6, and 0.4, and multiple second similarities are 0.5, 0.6, 0.7, and 0.8, then the typical day corresponding to the largest value among the multiple first similarities can be set as the target typical day; that is, the typical day corresponding to 0.6 can be set as the target typical day. Alternatively, the typical day corresponding to the largest value among the multiple second similarities can be set as the target typical day; that is, the typical day corresponding to 0.8 can be set as the target typical day. Alternatively, the first and second similarities corresponding to the typical day can be added together, resulting in 0.1 + 0.5 = 0.6, 0.2 + 0.6 = 0.8, 0.6 + 0.7 = 1.3, and 0.4 + 0.8 = 1.2. The typical day corresponding to the largest sum of these values ​​can then be set as the target typical day; that is, the typical day corresponding to 1.3 can be set as the target typical day.

[0097] If two days with equal similarity are encountered, but the typical days corresponding to the two similarities are different, then one of them is randomly selected as the target typical day.

[0098] Step 15: Set the wind-solar ratio strategy corresponding to the target typical day as the current wind-solar ratio strategy.

[0099] Taking the above example again, the typical day corresponding to 0.6 is set as the target typical day, that is, the wind and light ratio strategy corresponding to the third typical day is set as the current wind and light ratio strategy.

[0100] Set the typical day corresponding to 0.8 as the target typical day, that is, set the wind and solar power ratio strategy corresponding to the fourth typical day as the current wind and solar power ratio strategy.

[0101] Set the typical day corresponding to 1.3 as the target typical day, that is, set the wind and light ratio strategy corresponding to the third typical day as the current wind and light ratio strategy.

[0102] In some alternative implementations, step 16, adjusting the electrical load required for hydrogen and / or methanol production by the power generation system in the preset area according to the current wind-solar ratio strategy, includes:

[0103] The first wind-solar power ratio strategy includes: adjusting the wind-solar power supply ratio according to the preset first wind-solar power ratio value, combined with supercritical CO2 Brayton cycle units, to maintain the minimum electrical load required for hydrogen production and / or methanol production.

[0104] The second wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset second wind-solar power supply ratio value, and supplying power according to the minimum electrical load required for methanol production and the maximum electrical load required for hydrogen production;

[0105] The third wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset third wind-solar power supply ratio value, combining short-term energy storage equipment to smooth power fluctuations, and supplying power according to the highest electrical load required for hydrogen production.

[0106] The fourth wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset fourth wind-solar power supply ratio value, and supplying power according to the highest electrical load required for hydrogen production and / or methanol production.

[0107] This invention achieves multi-energy complementarity and efficient synergy by scientifically planning the capacity ratio of wind and photovoltaic power, thereby coupling thermal power and renewable energy. The spatiotemporal complementarity of wind and solar power can effectively smooth out the volatility of renewable energy, reduce the peak-valley difference of the system's net load, and thus reduce the dependence on backup power and energy storage capacity.

[0108] In this embodiment, the first typical day corresponds to the first landscape-sun ratio strategy, the second typical day corresponds to the second landscape-sun ratio strategy, the third typical day corresponds to the third landscape-sun ratio strategy, and the fourth typical day corresponds to the fourth landscape-sun ratio strategy.

[0109] As an example, with Figure 2 Taking the application scenario as an example, based on the determined operating strategy, corresponding adjustments are made to various devices in the power supply system, such as wind and solar generators, electrolyzers, methanol synthesis systems, and carbon capture systems, so that they operate collaboratively according to the established strategy to achieve optimized power supply based on the wind power ratio of the day. The wind and solar power ratio strategy for each typical day is described below:

[0110] On the first typical day (when the electrical load generated by wind and solar energy is extremely low): the wind and solar power supply ratio is adjusted according to the preset first wind-solar power ratio (e.g., 1:1, meaning wind power accounts for 1 / 2 and solar power accounts for 1 / 2). This results in a higher and more stable electrical load from wind and solar energy. On this typical day, the wind gradually increases but still fluctuates, and the sunlight intensity is moderate. Overall, wind and solar resources are complementary, but there are also intermittent periods of insufficient energy. During system operation, when both wind and sunlight are sufficient, the system will fully utilize electricity to produce hydrogen and synthesize methanol, while storing excess hydrogen for later use. When the wind weakens but sunlight is sufficient, priority is given to ensuring the stable operation of the hydrogen production equipment to ensure a sufficient hydrogen supply. In the event of continuous cloudy or calm weather, the system will rely on the supercritical CO2 Brayton cycle unit to maintain the minimum production requirements, avoiding frequent shutdowns that could damage the equipment, while simultaneously using stored hydrogen to continue producing methanol.

[0111] The second typical day (PV-dominated): The wind-solar power supply ratio is adjusted according to the preset second wind-solar ratio value (e.g., 1:2, meaning wind power accounts for 1 / 3 and solar power accounts for 2 / 3). On this typical day, wind and solar resources are distributed with weak and discontinuous wind, but strong solar radiation. To some extent, wind and solar power output can complement each other, but there are still many periods of low wind and solar power. The system operation strategy is as follows: during strong winds and strong sunlight, all electricity is used to produce hydrogen for methanol synthesis, but the methanol production rate will be appropriately slowed down while ensuring the minimum methanol production rate, and excess hydrogen will be stored in hydrogen storage tanks. During weak winds and strong sunlight, wind and solar power generation prioritizes hydrogen production in the electrolyzer to ensure sufficient hydrogen production. If there is no wind or sunlight for a long period, a stable power supply is provided by the supercritical CO2 Brayton cycle unit to maintain the minimum operating threshold of the hydrogen production and synthesis system, ensuring continuous and stable methanol production.

[0112] The third typical day (wind power dominant): The wind-solar power supply ratio is adjusted according to the preset second wind-solar ratio value (e.g., 3:2, i.e., wind power accounts for 3 / 5 and solar power accounts for 2 / 5). On this typical day, wind power increases but sunlight gradually weakens, resulting in fluctuations in overall energy supply. During system operation, when both wind and sunlight are good, wind and solar power will be used efficiently to maximize hydrogen production and methanol synthesis, storing excess energy. When wind is strong but sunlight is insufficient, wind power will be used first to maintain hydrogen production, supplemented by short-term energy storage equipment to smooth power fluctuations. In the event of prolonged cloudy or rainy weather with no wind or sunlight, the system will rely on supercritical CO2 Brayton cycle units to maintain minimum production requirements, improving the overall utilization rate of wind and solar resources, avoiding frequent shutdowns that could damage equipment, and simultaneously utilizing stored hydrogen to continue methanol production, ensuring the continuity of methanol production.

[0113] The fourth typical day (wind-solar complementary): The wind-solar power supply ratio is adjusted according to the preset second wind-solar ratio value (e.g., 1:1, meaning wind power accounts for 1 / 2 and solar power accounts for 1 / 2). The distribution characteristics of wind and solar power resources on this typical day exhibit a coupling feature of seasonal decay of irradiance and continuous increase of wind speed, resulting in a significant spatiotemporal superposition effect of new energy output, exacerbating the pressure on grid peak shaving and absorption. The system adopts a multi-system joint control strategy to achieve dynamic matching of source and load. Under conditions of abundant wind and solar resources, both the water electrolysis hydrogen production unit and the CO2 catalytic hydrogenation to methanol synthesis reactor operate at rated load. When wind and solar output drops to the critical range, priority is given to ensuring the minimum stable load of the water electrolysis hydrogen production system and the power required for continuous production of the methanol reactor. Under extreme resource scarcity conditions, the supercritical CO2 Brayton cycle unit provides a stable power supply to maintain the minimum operating threshold of the hydrogen production system and the methanol synthesis system, ensuring continuous and stable methanol production.

[0114] In summary, by adjusting the subsystems within the above strategy, it can be seen that each subsystem exhibits significant time dependence and volatility in terms of electrical and hydrogen power output. On the fourth typical day, wind power output was at a high level, photovoltaic output was at a low level, and the load was at a medium level, with similar output distribution across the wind-solar-storage system. On the third typical day, wind power output was at a high level, photovoltaic output was at a low level, and the load was high. On the first and second typical days, photovoltaic output was high, wind power output was low, and the load was at a medium level.

[0115] In an optional embodiment of the present invention, each wind-solar power supply strategy in step 16 corresponds to a wind-solar power supply ratio value, wherein the wind-solar power supply ratio value on a typical day is determined through the following process:

[0116] Step 161: Obtain the target solar radiation intensity and target wind speed for the target time period;

[0117] Step 162: Cluster the target light radiation intensity and target wind speed to obtain multiple typical days;

[0118] Step 163: Calculate the wind and solar power load for each typical day based on the preset wind-solar power ratio data;

[0119] Step 164: Determine the wind-solar power ratio for a typical day based on the wind and solar power load.

[0120] As an example, for step 161, the target time period can be the entire previous year, and the target solar radiation intensity and target wind speed can be historical data of solar radiation intensity and wind speed for the entire previous year.

[0121] In some alternative implementations, step 162 involves clustering the target solar radiation intensity and target wind speed to obtain multiple typical days, including:

[0122] Step 1621: Preprocess the target light radiation intensity and target wind speed to obtain equidistant grid data;

[0123] Step 1622: Determine the number of clusters K using the elbow rule; perform clustering based on the number of clusters K and the equidistant grid data to obtain the light radiation intensity curve and wind speed curve;

[0124] Step 1623: Determine multiple typical days based on the solar radiation intensity curve and wind speed curve.

[0125] In this embodiment, the light radiation intensity of the previous year can be a pre-stored grayscale image, or the light radiation intensity value can be obtained by establishing a mapping relationship between pixel values ​​and real light based on the stored grayscale image through calibration experiments or a radiometric calibration model.

[0126] In this embodiment, the target light radiation intensity is taken as the light radiation intensity value. The target wind speed can also be a pre-stored wind speed value.

[0127] In this embodiment, for step 1621, the target illumination radiation intensity and the target wind speed need to be processed sequentially. That is, the target illumination radiation intensity is preprocessed to obtain target illumination equidistant grid data. The target wind speed is preprocessed to obtain target wind speed equidistant grid data.

[0128] The following steps, taking the target illumination radiation intensity as an example, are as follows:

[0129] 1. Because the data may contain missing, duplicate, or erroneous values, it is necessary to first process the missing values ​​and remove outliers from the target illumination radiation intensity. For example, for illumination data: fill with 0 when there is no radiation at night, and fill missing values ​​during the day with data from nearby stations or satellite interpolation (such as MODIS).

[0130] 2. Next, the cleaned data is standardized to eliminate dimensional differences and ensure that each feature contributes equally to the distance. For example, solar radiation: normalized to [0, 1] (divided by the theoretical maximum value, such as 1361 W / m²). 2 Then, based on the standardized illumination data, equidistant grid data is generated (each grid cell in the equidistant grid has the same spatial resolution).

[0131] 3. Generate target illumination isometric grid data. Spatial interpolation is obtained through inverse distance weighting; a high resolution (e.g., 100m grid) is selected as the grid resolution; boundary processing and terrain correction are performed based on the digital elevation model (DEM), spatial interpolation, and grid resolution to obtain target illumination isometric grid data.

[0132] The following steps, taking target wind speed as an example, are as follows:

[0133] 1. Because the data may contain missing, duplicate, or erroneous values, it is necessary to first process the missing values ​​and remove outlier data for the target wind speed. For example, for wind speed data: use time series interpolation (such as linear interpolation, ARIMA model) or spatial interpolation (such as Kriging).

[0134] 2. Next, the cleaned data is standardized to eliminate dimensional differences and ensure that each feature contributes equally to the distance. For example, wind speed: Z-score standardization or Weibull distribution fitting (if probabilistic modeling is required); then, equidistant grid data is generated based on the standardized wind speed data (each grid cell in the equidistant grid has the same spatial resolution).

[0135] 3. Generate target wind speed equidistant grid data. Spatial interpolation is calculated based on radial basis function (RBF); a 100m grid is selected as the grid resolution; boundary processing and terrain correction are performed based on wind profile law to correct height differences, spatial interpolation, and grid resolution, and the target wind speed equidistant grid data is calculated.

[0136] For applying logarithmic wind profile law to correct for height differences, please refer to:

[0137]

[0138] Where z0 is the surface roughness, z1 and z2 are different heights, v2 is the corrected target wind speed equidistant grid data, and v1 is the target wind speed equidistant grid data to be corrected.

[0139] Since the equidistant grid data is divided into target illumination equidistant grid data and target wind speed equidistant grid data, for step 1622, the number of clusters K is determined using the elbow rule; clustering is performed based on the number of clusters K and the equidistant grid data to obtain the illumination radiation intensity curve and wind speed curve. It is necessary to determine the illumination radiation intensity curve and wind speed curve separately based on the target illumination equidistant grid data and the target wind speed equidistant grid data, that is, to determine the number of clusters K using the elbow rule; and to perform clustering based on the number of clusters K and the target illumination equidistant grid data to obtain the illumination radiation intensity curve. The following steps can be referenced:

[0140] 1. Calculate the clustering error for different K values. Iterate through possible K values ​​(e.g., K = 1 to 10) and record the total squared error (SSE) for each K value (i.e., the sum of squared distances from the sample to its cluster center, where the sample is the target illumination equidistant grid data).

[0141] 2. Plot the elbow curve. Horizontal axis: K value; Vertical axis: SSE. Select the inflection point of the curve as the optimal K value.

[0142] 3. Based on the preset time step (which can be a day of the year), flatten the target illumination grid data into feature vectors, then calculate the distance between the flattened feature vectors and the optimal k value, and perform clustering based on the distance to obtain multiple clusters.

[0143] 4. Each cluster center represents a typical spatiotemporal distribution pattern. The cluster center vector is restored to a grid shape, the mean value at each time step is calculated, and the sample closest to the center in each cluster is selected as the representative to draw the curve, thus obtaining the light radiation intensity curve.

[0144] The number of clusters K is determined using the elbow rule; clustering is then performed based on the number of clusters K and the target wind speed equidistant grid data to obtain the wind speed intensity curve. The following steps can be referenced:

[0145] 1. Calculate the clustering error for different K values. Iterate through possible K values ​​(e.g., K = 1 to 10) and record the total squared error (SSE) for each K value (i.e., the sum of squared distances from the sample to its cluster center, where the sample is the target wind speed equidistant grid data).

[0146] 2. Plot the elbow curve. Horizontal axis: K value; Vertical axis: SSE. Select the inflection point of the curve as the optimal K value.

[0147] 3. Based on the preset time step (which can be a day of the year), flatten the target wind speed grid data into feature vectors, then calculate the distance between the flattened feature vectors and the optimal k value, and perform clustering based on the distance to obtain multiple clusters.

[0148] 4. Each cluster center represents a typical spatiotemporal distribution pattern. The cluster center vector is restored to a grid shape, the mean value at each time step is calculated, and the sample closest to the center in each cluster is selected as the representative to draw the curve, thus obtaining the wind speed intensity curve.

[0149] The algorithm described above is based on distance metrics and divides the data into different clusters through iterative operations, resulting in high similarity within clusters and large differences between clusters. This compresses complex time series data into four representative daily curves. The same operation is performed on the power load data to preserve the core fluctuation characteristics.

[0150] As an example, the light radiation intensity curve can be referenced. Figure 3 The wind speed curve can be used as a reference. Figure 4 .

[0151] Based on the above embodiments, in step 1623, by fitting the light radiation intensity curve and wind speed curve, the characteristics of light intensity and wind speed for each day of the year can be obtained, and multiple typical days can be determined.

[0152] In some optional implementations, step 163 involves calculating the wind and solar power load for each typical day based on preset wind-solar power ratio data, including:

[0153] Step 1631: Determine the wind power load based on the wind power generation model and the wind parameters corresponding to a typical day;

[0154] Step 1632: Determine the photovoltaic load based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day;

[0155] Step 1633: Calculate the wind and solar power load for each typical day based on multiple wind and solar power ratio values, wind power load, and photovoltaic power load in the preset wind and solar power ratio data.

[0156] In this embodiment, for each typical day, the wind power load and photovoltaic power load corresponding to the typical day are obtained, and the power supply ratio of wind power load and photovoltaic power load is adjusted according to multiple wind-solar ratio values ​​to achieve the purpose of stable power supply and maximum power supply.

[0157] Power is a physical quantity that quantifies electrical load, while electrical load is the power demand or set of equipment at the system level. Therefore, electrical load can be quantified by calculating power.

[0158] For step 1631, the wind power load can be determined by referring to the following formula:

[0159]

[0160] Among them, v ci v R and v co The following are the cut-in velocity, rated velocity, and cut-out velocity of the fan, in order, in m / s.

[0161] v ci The cut-in wind speed of a wind turbine is the minimum wind speed at which the turbine begins generating electricity, measured in m / s.

[0162] v R Rated wind speed of a fan, which is the wind speed at which the fan reaches its rated power output, is measured in m / s.

[0163] v co The cut-off wind speed of a wind turbine is the wind speed at which the wind turbine stops generating electricity, and the unit is m / s.

[0164] v: Actual wind speed, which is an input value. The range of values ​​varies depending on the calculation method for different intervals. The unit is m / s. It is obtained by actual measurement at the wind power plant using a wind speed measuring instrument.

[0165] P WT (υ): Output power of the wind power generation system, which is the output value in watts. It is used as the power output value of the wind power generation system for subsequent power system analysis, power balance calculation, etc.

[0166] W WT : Indicates the rated power of the wind turbine.

[0167] For step 1632, the photovoltaic load can be determined by referring to the following formula:

[0168]

[0169] Among them, P PV : This refers to the output power of the wind power generation system, measured in watts (W). It serves as the power output value of the wind power generation system and is used for subsequent power system analysis, power balance calculations, etc.

[0170] G: Luminous radiation intensity, measured in watts per square meter (W / m²) 2 ), representing the solar radiation power received per unit area, is obtained through actual measurement at the photovoltaic power plant using a solar radiation sensor.

[0171] T C The unit is degrees Celsius (°C), which refers to the actual operating temperature of photovoltaic power generation modules.

[0172] T STC Temperature under standard test conditions, typically 25℃.

[0173] β: Temperature coefficient related to module characteristics, representing the characteristic of module power changing with temperature.

[0174] W PV Rated power of a photovoltaic power generation system under standard test conditions, expressed in watts (W).

[0175] G STC The intensity of light radiation under standard test conditions is typically 1000 W / m². 2 .

[0176] For step 1633, for example, the wind-solar ratio can be 5:5, 4:6, and 6:4.

[0177] The wind power load on the first typical day is w f1 and photovoltaic load w g1 .

[0178] The wind and solar power load on the first typical day is w 总11 =5*w f1 +5*w g1 w 总12 =4*w f1 +6*w g1 w 总13 =6*w f1 +4*w g1 .

[0179] Similarly, the wind power load on the second typical day is w. f2 and photovoltaic load w g2 .

[0180] The wind and solar power load on the second typical day is w 总21 =5*w f2 +5*w g2 w 总22 =4*w f2 +6*w g2 w 总23 =6*w f2 +4*w g2 .

[0181] Similarly, the wind power load on the third typical day is w. f3 and photovoltaic load w g3 .

[0182] The wind and solar power load on the third typical day is w 总31 =5*w f3 +5*w g3 w 总32 =4*w f3 +6*w g3 w 总33 =6*w f3 +4*w g3 .

[0183] Similarly, the wind power load on the fourth typical day is w. f4 and photovoltaic load w g4 .

[0184] The wind and solar power load on the fourth typical day was w 总41 =5*w f4 +5*w g4 w 总42 =4*w f4 +6*w g4 w 总43 =6*w f4 +4*w g4 .

[0185] In some alternative implementations, step 164, determining the wind-solar power ratio for a typical day based on the wind and solar power load, includes:

[0186] Step 1641: Determine the preset wind-solar ratio value corresponding to the maximum value among multiple wind and solar power loads as the wind-solar ratio value for a typical day.

[0187] Taking the above example again, for the first typical day, we select w 总11 w 总12 and w 总13 The highest corresponding landscape ratio value is used as the landscape ratio value for the first typical day. For example, w 总11 If the ratio is the largest, then 5:5 is the scenery ratio for the first typical day. Similarly, the scenery ratios for other typical days can be determined.

[0188] Based on the embodiments of the above method, Figure 2 As an embodiment of a specific application of the present invention, in Figure 2 In the application scenario shown, during the fourth typical day, from 0:00 to 5:00 AM, the system's electricity demand is low, and there is a large surplus of wind power, resulting in a high hydrogen production capacity in the electrolyzer. From 5:00 to 11:00 AM, electricity demand increases, but wind and solar power generation decreases, requiring a reduction in the electrolyzer's hydrogen production capacity to meet the demand. From 11:00 to 4:00 PM, the system's power generation exceeds the demand, and the electrolyzer's hydrogen production capacity gradually increases. After 4:00 PM, wind and solar power generation decreases, necessitating a reduction in the electrolyzer's hydrogen production capacity. On the first, second, and third typical days, hydrogen storage mostly occurs at night, with the stored hydrogen used to produce methanol during the day. On the fourth typical day, hydrogen storage mostly occurs during the day, with the stored hydrogen used to produce methanol at night. If wind power fluctuations cause unstable power supply during hydrogen production, it may affect the continuity of the water electrolysis hydrogen production operation, thereby impacting the hydrogen storage and release strategies of the hydrogen storage tanks.

[0189] When wind power suddenly decreases, it may be necessary to release hydrogen from hydrogen storage tanks to maintain the stable operation of methanol synthesis and other processes.

[0190] When wind and solar power generation are in equal proportions, the hydrogen production curve is not stable. Therefore, by adjusting the wind and solar power ratio, a suitable ratio can be obtained to keep the hydrogen supply curve stable and reduce energy consumption.

[0191] Under four typical day scenarios, the impact of different wind-solar power ratios (9:1 to 1:9) on the system was tested, and key indicators were quantified: the degree of mismatch between wind and solar power output and load demand, the peak-shaving capacity and hydrogen utilization rate of the hydrogen storage tank, and the carbon capture system's efficiency in capturing carbon emissions from thermal power plants. Simulation verification showed that a 6:4 wind-solar power ratio (60% wind power, 40% solar power) was optimal for all key quantitative indicators on each typical day. Specifically, by studying the coordinated output of various parts of the system, including power balance, hydrogen balance, and carbon dioxide balance, the system performance under different ratios was comprehensively evaluated, including indicators such as power deficit, energy storage utilization, hydrogen storage and consumption, and carbon emissions. For example, on typical day one, the power deficit was minimized to only 48MW when the wind-solar power ratio was 6:4, and the energy storage call frequency and capacity utilization rate reached an optimal balance. The dynamics of hydrogen generation, storage, and consumption also varied under different ratios on different typical days. Based on these analysis results, a relatively optimal wind power ratio of 6:4 was determined for each typical day.

[0192] In light of current application scenarios, when testing different wind-solar power ratios, balance equations can be established based on the conservation of electrical energy, thermal energy, hydrogen mass, and carbon dioxide mass. These balance equations serve as constraints to ensure the coordinated operation of wind and solar power generators, electrolyzers, methanol synthesis systems, and carbon capture systems, achieving optimized power supply based on the daily wind power ratio. The balance equations can be referenced below.

[0193]

[0194] Among them, P PGU P is the output of a thermal power unit, measured in kW. WT Wind power output, unit is kW; P PV Photovoltaic output, unit is kW; P cCS The electrical energy consumed for carbon capture is measured in kW; P EL The electrical energy consumed in electrolysis is measured in kW. P represents the electrical energy required for methanol synthesis, measured in kW. demand This represents the user's power demand, expressed in kW.

[0195]

[0196] in, The heat energy provided by the carbon dioxide working fluid for heat exchange, Q C,out To output heat energy to the molten salt tank, Q C,in To input thermal energy into the molten salt tank, Q CCS The thermal energy required for the carbon capture and stripping tower.

[0197]

[0198] in, The amount of hydrogen produced by the electrolyzer, in kg; The amount of hydrogen released from the hydrogen tank, in kg; The amount of hydrogen entering the hydrogen tank, expressed in kg; The amount of hydrogen consumed in methanol synthesis is expressed in kg.

[0199]

[0200] in, The amount of carbon dioxide captured by carbon capture, expressed in kg. The amount of carbon dioxide released from the carbon dioxide storage tank, expressed in kg. The amount of carbon dioxide entering the carbon dioxide storage tank, expressed in kg. The amount of carbon dioxide consumed in methanol synthesis is expressed in kg.

[0201] Figure 5 This is a schematic diagram of an embodiment of the device for determining the wind-solar capacity allocation strategy according to the present invention. Figure 5 As shown, the device includes:

[0202] The acquisition module 51 is used to acquire the current solar radiation intensity and current wind speed of the preset area during the current preset time period;

[0203] The first processing module 52 is used to calculate the similarity between the current light radiation intensity and the light radiation intensity corresponding to multiple typical days to obtain multiple first similarities.

[0204] The second processing module 53 is used to calculate the similarity between the current wind speed and the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

[0205] The third processing module 54 is used to determine a target typical day based on the multiple first similarities and multiple second similarities, with each typical day corresponding to a landscape matching strategy;

[0206] The fourth processing module 55 is used to set the wind-solar ratio strategy corresponding to the target typical day as the current wind-solar ratio strategy;

[0207] The fifth processing module 56 is used to adjust the electrical load required for the power generation system in the preset area to produce hydrogen and / or methanol according to the current wind-solar ratio strategy.

[0208] Optionally, the first processing module 52 is used for:

[0209] Obtain the spatial illumination distribution image of the current preset time period in the preset area;

[0210] The spatial illumination distribution image is processed for grayscale and features are extracted to obtain the current illumination radiation intensity vector;

[0211] Feature extraction was performed on the solar radiation intensity corresponding to multiple typical days to obtain multiple typical solar radiation intensity vectors.

[0212] The current light radiation intensity vector is sequentially compared with multiple typical light radiation intensity vectors to calculate the cosine similarity, resulting in multiple first similarity values.

[0213] Optionally, the second processing module 53 is used for:

[0214] Get the current wind speed in the preset area;

[0215] The current wind speed is subtracted from the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

[0216] Optionally, the third processing module 54 is used for:

[0217] Set the typical day corresponding to the largest value among the plurality of first similarities and / or the largest value among the plurality of second similarities as the target typical day; and / or,

[0218] For each typical day, the first similarity and the second similarity corresponding to the typical day are added together, and the typical day corresponding to the maximum value of the sum is set as the target typical day.

[0219] Optionally, a sixth processing module 57 is also included, for:

[0220] Acquire the target solar radiation intensity and target wind speed within the target time period;

[0221] Clustering the target light radiation intensity and target wind speed yields multiple typical days;

[0222] Based on the preset wind-solar power ratio data, calculate the wind and solar power load for each typical day;

[0223] Based on the wind and solar power load, determine the wind-solar power ratio for a typical day.

[0224] Optionally, the sixth processing module 57 is used for:

[0225] The target illumination intensity and target wind speed are preprocessed to obtain equidistant grid data;

[0226] The number of clusters K is determined using the elbow rule; clustering is performed based on the number of clusters K and the equidistant grid data to obtain the solar radiation intensity curve and wind speed curve;

[0227] Several typical days were determined based on the aforementioned solar radiation intensity curve and wind speed curve.

[0228] Optionally, the sixth processing module 57 is used for:

[0229] The wind power load is determined based on the wind power generation model and the wind parameters corresponding to a typical day.

[0230] The photovoltaic load is determined based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day.

[0231] Based on multiple wind-solar ratio values, wind power load, and photovoltaic power load in the preset wind-solar ratio data, calculate the wind and solar power load for each typical day;

[0232] Based on the aforementioned wind and solar power load, determine the wind-solar power ratio for a typical day, including:

[0233] The preset wind-solar ratio value corresponding to the maximum value among multiple wind and solar power loads is determined as the wind-solar ratio value for a typical day.

[0234] Optionally, the fifth processing module 56 is used for:

[0235] The first wind-solar power ratio strategy includes: adjusting the wind-solar power supply ratio according to the preset first wind-solar power ratio value, combined with supercritical CO2 Brayton cycle units, to maintain the minimum electrical load required for hydrogen production and / or methanol production.

[0236] The second wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset second wind-solar power supply ratio value, and supplying power according to the minimum electrical load required for methanol production and the maximum electrical load required for hydrogen production;

[0237] The third wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset third wind-solar power supply ratio value, combining short-term energy storage equipment to smooth power fluctuations, and supplying power according to the highest electrical load required for hydrogen production.

[0238] The fourth wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset fourth wind-solar power supply ratio value, and supplying power according to the highest electrical load required for hydrogen production and / or methanol production.

[0239] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute the method for determining the wind and solar capacity allocation strategy as described above.

[0240] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0241] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0242] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0243] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining a wind-solar capacity allocation strategy, characterized in that, include: Obtain the current solar radiation intensity and current wind speed of the preset area during the current preset time period; The similarity between the current solar radiation intensity and the solar radiation intensity corresponding to multiple typical days is calculated to obtain multiple first similarity scores. The current wind speed is compared with the wind speeds corresponding to multiple typical days to obtain multiple second similarities. Based on the multiple first similarities and multiple second similarities, a target typical day is determined, and each typical day corresponds to a landscape matching strategy; Set the wind and light ratio strategy corresponding to the target typical day as the current wind and light ratio strategy; Adjust the electrical load required for hydrogen and / or methanol production in the preset area according to the current wind-solar ratio strategy; The determination of a target typical day based on the plurality of first similarities and the plurality of second similarities includes: Set the typical day corresponding to the largest value among the plurality of first similarities and / or the largest value among the plurality of second similarities as the target typical day; and / or, For each typical day, the first similarity and the second similarity corresponding to the typical day are added together, and the typical day corresponding to the maximum value of the sum is set as the target typical day; Each wind-solar power allocation strategy corresponds to a wind-solar power allocation value, which is determined through the following process: Acquire the target solar radiation intensity and target wind speed within the target time period; Clustering the target light radiation intensity and target wind speed yields multiple typical days; Based on the preset wind-solar power ratio data, calculate the wind and solar power load for each typical day; Based on the wind and solar power load, determine the wind-solar power ratio for a typical day; Based on preset wind-solar power ratio data, the wind and solar power load for each typical day is calculated, including: The wind power load is determined based on the wind power generation model and the wind parameters corresponding to a typical day. The photovoltaic load is determined based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day. Based on multiple wind-solar ratio values, wind power load, and photovoltaic power load in the preset wind-solar ratio data, calculate the wind and solar power load for each typical day; Based on the aforementioned wind and solar power load, determine the wind-solar power ratio for a typical day, including: The preset wind-solar ratio value corresponding to the maximum value among multiple wind and solar power loads is determined as the wind-solar ratio value for a typical day; Among them, according to Determine the wind power load; among which, , and The following are the wind turbine's cut-in velocity, rated velocity, and cut-out velocity, in that order. This indicates the cut-in wind speed of the fan. This indicates the rated wind speed of the fan. This indicates the cut-out air velocity of the fan. This represents the actual wind speed, which is an input value. This represents the output power of the wind power generation system, and is the output value. This indicates the rated power of the wind turbine generator; Among them, according to Determine the photovoltaic power load; among which, among which, This indicates the output power of the wind power generation system. Indicates the intensity of light radiation. This indicates the actual operating temperature of the photovoltaic power generation module. This indicates the temperature under standard test conditions. This represents the temperature coefficient related to component characteristics. This indicates the rated power of the photovoltaic power generation system under standard test conditions. This indicates the intensity of light radiation under standard test conditions.

2. The method for determining the wind-solar capacity allocation strategy according to claim 1, characterized in that, The similarity between the current solar radiation intensity and the solar radiation intensity corresponding to multiple typical days is calculated to obtain multiple first similarity scores, including: Obtain the spatial illumination distribution image of the current preset time period in the preset area; The spatial illumination distribution image is processed for grayscale and features are extracted to obtain the current illumination radiation intensity vector; Feature extraction was performed on the solar radiation intensity corresponding to multiple typical days to obtain multiple typical solar radiation intensity vectors. The current light radiation intensity vector is sequentially compared with multiple typical light radiation intensity vectors to calculate the cosine similarity, resulting in multiple first similarity values.

3. The method for determining the wind-solar capacity allocation strategy according to claim 1, characterized in that, The current wind speed is compared with the wind speeds corresponding to multiple typical days to obtain multiple second similarities, including: Get the current wind speed in the preset area; The current wind speed is subtracted from the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

4. The method for determining the wind-solar capacity allocation strategy according to claim 1, characterized in that, Clustering the target illumination intensity and target wind speed yields several typical days, including: The target illumination intensity and target wind speed are preprocessed to obtain equidistant grid data; The number of clusters K is determined using the elbow rule; Clustering is performed based on the number of clusters K and the equidistant grid data to obtain the light radiation intensity curve and wind speed curve; Several typical days were determined based on the aforementioned solar radiation intensity curve and wind speed curve.

5. The method for determining the wind-solar capacity allocation strategy according to claim 1, characterized in that, Adjusting the electrical load required for hydrogen and / or methanol production in a preset area according to the current wind-solar ratio strategy includes: The primary wind-solar power ratio strategy includes: adjusting the wind-solar power supply ratio according to a preset primary wind-solar power ratio value, combined with supercritical... Brayton cycle units are used to maintain the minimum electrical load required for hydrogen production and / or methanol production. The second wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset second wind-solar power supply ratio value, and supplying power according to the minimum electrical load required for methanol production and the maximum electrical load required for hydrogen production; The third wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset third wind-solar power supply ratio value, combining short-term energy storage equipment to smooth power fluctuations, and supplying power according to the highest electrical load required for hydrogen production. The fourth wind-solar power supply strategy includes: adjusting the wind-solar power supply ratio according to the preset fourth wind-solar power supply ratio value, and supplying power according to the highest electrical load required for hydrogen production and / or methanol production.

6. A device for determining a wind-solar capacity allocation strategy, characterized in that, include: The acquisition module is used to acquire the current solar radiation intensity and current wind speed of the preset area during the current preset time period; The first processing module is used to calculate the similarity between the current light radiation intensity and the light radiation intensity corresponding to multiple typical days to obtain multiple first similarities; The second processing module is used to calculate the similarity between the current wind speed and the wind speeds corresponding to multiple typical days to obtain multiple second similarities. The third processing module is used to determine the target typical day based on the multiple first similarities and multiple second similarities, with each typical day corresponding to a landscape matching strategy; The fourth processing module is used to set the wind-solar ratio strategy corresponding to the target typical day as the current wind-solar ratio strategy; The fifth processing module is used to adjust the electrical load required for the power generation system in the preset area to produce hydrogen and / or methanol according to the current wind-solar ratio strategy. The determination of a target typical day based on the plurality of first similarities and the plurality of second similarities includes: Set the typical day corresponding to the largest value among the plurality of first similarities and / or the largest value among the plurality of second similarities as the target typical day; and / or, For each typical day, the first similarity and the second similarity corresponding to the typical day are added together, and the typical day corresponding to the maximum value of the sum is set as the target typical day; Each wind-solar power allocation strategy corresponds to a wind-solar power allocation value, which is determined through the following process: Acquire the target solar radiation intensity and target wind speed within the target time period; Clustering the target light radiation intensity and target wind speed yields multiple typical days; Based on the preset wind-solar power ratio data, calculate the wind and solar power load for each typical day; Based on the wind and solar power load, determine the wind-solar power ratio for a typical day; Based on preset wind-solar power ratio data, the wind and solar power load for each typical day is calculated, including: The wind power load is determined based on the wind power generation model and the wind parameters corresponding to a typical day. The photovoltaic load is determined based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day. Based on multiple wind-solar ratio values, wind power load, and photovoltaic power load in the preset wind-solar ratio data, calculate the wind and solar power load for each typical day; Based on the aforementioned wind and solar power load, determine the wind-solar power ratio for a typical day, including: The preset wind-solar ratio value corresponding to the maximum value among multiple wind and solar power loads is determined as the wind-solar ratio value for a typical day; Among them, according to Determine the wind power load; among which, , and The following are the wind turbine's cut-in velocity, rated velocity, and cut-out velocity, in that order. This indicates the cut-in wind speed of the fan. This indicates the rated wind speed of the fan. This indicates the cut-out air velocity of the fan. This represents the actual wind speed, which is an input value. This represents the output power of the wind power generation system, and is the output value. This indicates the rated power of the wind turbine generator; Among them, according to Determine the photovoltaic power load; among which, among which, This indicates the output power of the wind power generation system. Indicates the intensity of light radiation. This indicates the actual operating temperature of the photovoltaic power generation module. This indicates the temperature under standard test conditions. This represents the temperature coefficient related to component characteristics. This indicates the rated power of the photovoltaic power generation system under standard test conditions. This indicates the intensity of light radiation under standard test conditions.

7. An electronic device, characterized in that, include: Processor, memory, and bus, among which, The processor and the memory communicate with each other via the bus; The memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to perform the steps of the method for determining the wind and solar capacity allocation strategy as described in any one of claims 1 to 5.

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