Method, device and equipment for determining wind-light capacity matching strategy

Abstract

The invention provides a method, a device and equipment for determining a wind-solar capacity matching strategy. The method comprises the following steps: acquiring current illumination radiation intensity and current wind speed of a current preset time period of a preset area; performing similarity calculation on the current illumination radiation intensity and the illumination radiation intensities corresponding to the plurality of typical days to obtain a plurality of first similarities; performing similarity calculation on the current wind speed and the wind speeds corresponding to the plurality of typical days to obtain a plurality of second similarities; according to the plurality of first similarities and the plurality of second similarities, a target typical day is determined, and each typical day corresponds to a wind-solar matching strategy; setting a wind-solar matching strategy corresponding to the target typical day as a current wind-solar matching strategy; and according to the current wind-solar matching strategy, adjusting the electrical load required for preparing hydrogen and / or methanol by the power generation system in the preset area. According to the scheme, dynamic coordination of multiple energy sources and hydrogen energy and efficient absorption and fluctuation stabilization of renewable energy sources are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy system planning, and in particular to a method, device and equipment for determining a wind-solar capacity ratio strategy. Background Art

[0002] Wind-solar-coupled hydrogen production systems are a key component of integrated energy production units, dynamically coordinating wind, photovoltaic, and hydrogen energy devices. However, the output characteristics of wind and solar power generation exhibit significant uncertainty due to the spatiotemporal dynamics of natural conditions such as wind speed and sunlight. To address this issue, existing research has primarily focused on the static coordination of a single energy source with hydrogen. Research on the real-time response mechanisms and multi-timescale coordinated control of the dynamic coupling of wind, solar, and hydrogen remains insufficient, failing to address the intermittent nature of wind-solar-coupled 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-solar capacity ratio strategy, realize the dynamic coordination of multiple energy sources and hydrogen energy, and realize the real-time response mechanism and multi-time scale collaborative control and scheduling of wind and solar energy complementary hydrogen production.

[0004] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for determining a wind / solar capacity ratio strategy, comprising: obtaining a current light radiation intensity and a current wind speed in a current preset time period of a preset area;

[0006] Calculating similarities between the current illumination radiation intensity and illumination radiation intensities corresponding to multiple typical days to obtain multiple first similarities;

[0007] Calculating similarities between the current wind speed and wind speeds corresponding to multiple typical days to obtain multiple second similarities;

[0008] Determining target typical days according to the multiple first similarities and the multiple second similarities, each typical day corresponding to a wind-solar ratio strategy;

[0009] The wind-solar ratio strategy corresponding to the target typical day is set as the current wind-solar ratio strategy;

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

[0011] Optionally, similarity calculation is performed between the current illumination radiation intensity and illumination radiation intensities corresponding to multiple typical days to obtain multiple first similarities, including:

[0012] Obtaining a spatial illumination distribution image of a preset area in a current preset time period;

[0013] Performing grayscale processing and feature extraction on the spatial illumination distribution image to obtain a current illumination radiation intensity vector;

[0014] Extract features of the light radiation intensity corresponding to multiple typical days in turn to obtain multiple typical light radiation intensity vectors;

[0015] Cosine similarities are calculated between the current illumination radiation intensity vector and a plurality of typical illumination radiation intensity vectors in sequence to obtain a plurality of first similarities.

[0016] Optionally, similarity calculation is performed between the current wind speed and 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 sequentially subtracted from the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

[0019] Optionally, determining a target typical day according to the plurality of first similarities and the plurality of second similarities includes:

[0020] The typical day corresponding to the maximum value among the plurality of first similarities and / or the maximum value among the plurality of second similarities is set as the target typical day; and / or,

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

[0022] Optionally, each wind-solar power supply matching strategy corresponds to a wind-solar power supply matching value, which indicates a wind-solar power supply matching value on a typical day determined by the following process:

[0023] Obtain the target light radiation intensity and target wind speed in the target time period;

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

[0025] Calculate the wind and solar power generation load for each typical day based on the preset wind and solar power ratio data;

[0026] The wind-solar ratio value for a typical day is determined based on the wind-solar power generation load.

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

[0028] Preprocessing the target light radiation intensity and target wind speed to obtain equidistant grid data;

[0029] Determine the cluster number K by the elbow rule; perform clustering based on the cluster number K and the equidistant grid data to obtain a light radiation intensity curve and a wind speed curve;

[0030] A plurality of typical days are determined according to the light radiation intensity curve and the wind speed curve.

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

[0032] Determine the wind power load based on the wind power generation model and the wind parameters corresponding to a typical day;

[0033] Determine the photovoltaic power load based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day;

[0034] Calculate the wind and solar power generation 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;

[0035] Determining the wind-solar power ratio value on a typical day based on the wind-solar power generation load includes:

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

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

[0038] The first wind-solar ratio strategy includes: adjusting the wind-solar power supply ratio according to a preset first wind-solar ratio value, and combining with a supercritical CO2 Brayton cycle unit to maintain the minimum power load required for producing hydrogen and / or methanol;

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

[0040] The third wind-solar power supply ratio 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 maximum power load required for hydrogen production;

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

[0042] In a second aspect, an embodiment of the present invention further provides a device for determining a wind-solar capacity ratio strategy, comprising: an acquisition module for acquiring a current light radiation intensity and a current wind speed in a current preset time period of a preset area;

[0043] A first processing module is configured to calculate similarities between the current light radiation intensity and the light radiation intensities corresponding to multiple typical days to obtain multiple first similarities;

[0044] A second processing module is configured to calculate similarities between the current wind speed and wind speeds corresponding to multiple typical days to obtain multiple second similarities;

[0045] A third processing module is configured to determine a target typical day based on the multiple first similarities and the multiple second similarities, where each typical day corresponds to a wind-solar ratio strategy;

[0046] A fourth processing module is configured 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] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for determining the wind-solar capacity matching strategy as described in the first aspect are implemented.

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

[0050] The above-mentioned scheme of the present invention obtains multiple first similarities and multiple second similarities based on the current light radiation intensity and the 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, thereby realizing a real-time response mechanism and multi-time scale collaborative control and scheduling of wind and solar energy complementary hydrogen production; according to the wind-solar ratio strategy, the power generation system in the preset area is adjusted to produce hydrogen and / or methanol. The electric load required for producing methanol is realized, thereby realizing dynamic coordination of multiple energy sources and hydrogen energy and efficient consumption and fluctuation smoothing of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0054] Figure 4 is the curve of wind speed variation over time

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

[0056] Figure 6 It is a schematic diagram of the structure of an electronic device entity embodiment of the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

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

[0059] Step 11, obtaining the current light radiation intensity and current wind speed of the preset area in the current preset time period;

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

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

[0062] Step 14: determining a target typical day based on the multiple first similarities and the multiple second similarities, where each typical day corresponds to a wind-solar ratio strategy;

[0063] Step 15: setting the wind-solar ratio strategy corresponding to the target typical day as the current wind-solar ratio strategy;

[0064] Step 16: adjusting 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] Wind and solar power generation output characteristics exhibit significant uncertainty due to the spatiotemporal dynamics of natural conditions such as wind speed and sunlight. Therefore, in this example, a wind-solar capacity matching strategy for wind and photovoltaic power generation can be constructed using the MATLAB platform. This wind-solar capacity matching strategy not only integrates both wind and photovoltaic renewable energy sources but also considers their power generation characteristics, technical feasibility, economic optimality, and policy guidance under different climatic conditions, providing support for building a highly resilient, low-carbon energy internet.

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

[0067] For step 11, as an example, the preset area may be to install light radiation intensity monitoring points at medium distances in a photovoltaic power plant, and similarly, to install wind speed monitoring points at medium distances in a wind power plant.

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

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

[0070] In some embodiments, the average light radiation intensity and the average wind speed for the entire day can also be calculated in combination with the weather forecast for the day, and the average light radiation intensity and the average wind speed for the entire day can be used as the current light radiation intensity and the current wind speed.

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

[0072] In some optional implementations, step 12, calculating similarities between the current illumination radiation intensity and illumination radiation intensities corresponding to multiple typical days to obtain multiple first similarities, may include:

[0073] Step 121, obtaining a spatial illumination distribution image of a preset area in a current preset time period;

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

[0075] Step 123 , sequentially extracting features of the illumination radiation intensity corresponding to a plurality of typical days to obtain a plurality of typical illumination radiation intensity vectors;

[0076] Step 124 : calculating cosine similarities between the current illumination radiation intensity vector and a plurality of typical illumination radiation intensity vectors in sequence to obtain a plurality of first similarities.

[0077] In this embodiment, the acquired current illumination radiation intensity may be a spatial illumination distribution image.

[0078] As an example, a video or image of the current light radiation intensity may be acquired through a camera at a fixed monitoring point.

[0079] As an example, a spatial illumination distribution image can be subjected to grayscale processing, image enhancement, and feature extraction in sequence to obtain the current illumination radiation intensity vector. The grayscaled and enhanced image avoids color interference and noise, making features more stable and discriminative, which facilitates feature extraction. Grayscale processing reduces computational complexity and noise interference; image enhancement improves image contrast and suppresses noise, as exemplified by Gaussian filtering and median filtering algorithms; and feature extraction focuses on key information, reduces dimensionality, and abstracts the image, as exemplified by edge detection, corner detection, or deep feature extraction algorithms.

[0080] For 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 the spatial illumination distribution image to obtain the current illumination radiation intensity vector. Alternatively, edge and gradient features and frequency-domain or transform-based features can be extracted and concatenated to obtain the current illumination radiation intensity vector.

[0081] The light radiation intensity corresponding to multiple typical days can be pre-stored grayscale images, which are used for rapid feature extraction and facilitate management personnel's review and maintenance. For example, edge and gradient features or frequency-domain or transformation-based features can be extracted from the pre-stored grayscale images of light radiation intensity corresponding to multiple typical days to obtain the current light radiation intensity vector. Alternatively, edge and gradient features and frequency-domain or transformation-based features can be extracted and concatenated to obtain the current light radiation intensity vector.

[0082] After obtaining the current illumination radiation intensity vector and multiple typical illumination radiation intensity vectors, calculate the cosine similarity in sequence. You can refer to:

[0083]

[0084] Among them, A represents the current light radiation intensity vector, B i represents the i-th typical illumination 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 illumination radiation intensity vector is g1, and there are four typical days. The illumination radiation intensity vector corresponding to the first typical day is p1, the illumination radiation intensity vector corresponding to the second typical day is p2, the illumination radiation intensity vector corresponding to the third typical day is p3, and the illumination radiation intensity vector corresponding to the fourth typical day is p4. The first second similarity calculated according to the above method is 0.1, the second second similarity is 0.2, the third second similarity is 0.6, and the fourth second similarity is 0.4. Among them, the first first similarity corresponds to the first typical day, the second first similarity corresponds to the second typical day, the third first similarity corresponds to the third typical day, and the fourth first similarity corresponds to the fourth typical day.

[0086] In some optional implementations, step 13, performing similarity calculation between the current wind speed and wind speeds corresponding to multiple typical days to obtain multiple second similarities, includes:

[0087] Step 131, obtaining the current wind speed of 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 in a grid pattern at equal intervals throughout a wind farm. Wind speed monitoring can be done using cup anemometers, thermal anemometers, or ultrasonic anemometers.

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

[0091] After subtracting the current wind speed from the wind speeds corresponding to multiple typical days in sequence, the reciprocal of the absolute value of the difference is taken to obtain a second similarity. A larger second similarity indicates that the wind speed of the typical day corresponding to the value is closer to the current preset time period.

[0092] For example, the current wind speed is f1, and 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 first second similarity calculated using the above method is 0.5, the second second similarity is 0.6, the third second similarity is 0.7, and the fourth second similarity is 0.8. 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 optional implementations, step 14, determining a target typical day based on the multiple first similarities and the multiple second similarities, where each typical day corresponds to a wind-solar matching strategy, includes:

[0094] Step 141: setting the typical day corresponding to the maximum value among the plurality of first similarities and / or the maximum 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 added values ​​as the target typical day.

[0096] Still taking 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 to obtain 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, and the typical day corresponding to the largest value among the added values ​​can 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 similarities are equal and the typical days corresponding to the two similarities are different, 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] Still taking the above example, the typical day corresponding to 0.6 is set as the target typical day, that is, the wind-solar ratio strategy corresponding to the third typical day is set as the current wind-solar ratio strategy.

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

[0101] The typical day corresponding to 1.3 is set as the target typical day, that is, the wind-solar ratio strategy corresponding to the third typical day is set as the current wind-solar ratio strategy.

[0102] In some optional 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 power matching strategy, includes:

[0103] The first wind-solar ratio strategy includes: adjusting the wind-solar power supply ratio according to a preset first wind-solar ratio value, and combining with a supercritical CO2 Brayton cycle unit to maintain the minimum power load required for producing hydrogen and / or methanol;

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

[0105] The third wind-solar power supply ratio 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 maximum power load required for hydrogen production;

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

[0107] The present invention realizes multi-energy complementarity and efficient synergy of the coupling of thermal power and renewable energy by scientifically planning the capacity ratio of wind energy and photovoltaic power. The temporal and spatial complementary characteristics of wind and photovoltaic power can effectively smooth the volatility of renewable energy, reduce the peak-to-valley difference of the system net load, and thus reduce dependence on backup power supply and energy storage capacity.

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

[0109] As an example, Figure 2 Taking the application scenario in [1] as an example, according to the determined operation strategy, the various devices in the power supply system, such as wind and solar power generators, electrolyzers, methanol synthesis systems, and carbon capture systems, are adjusted accordingly so that they can operate in coordination according to the established strategy and achieve optimized power supply based on the wind power ratio of the day. The following describes the wind and solar power ratio strategy for each typical day in turn:

[0110] On the first typical day (when wind and solar power generate extremely low electricity load), the wind-solar power supply ratio is adjusted according to the preset first wind-solar power ratio (for example, 1:1, meaning wind power accounts for 1 / 2 and solar power accounts for 1 / 2). This allows wind and solar power to generate a higher and more stable electricity load. During this typical day, wind power gradually increases but still fluctuates, and sunlight intensity is moderate. Overall, wind and solar power resources complement each other, but there are also intermittent shortages. During system operation, during periods of sufficient wind and sunlight, the system fully utilizes electricity to produce hydrogen and synthesize methanol, while storing excess hydrogen for backup. When wind power weakens but sunlight is abundant, priority is given to ensuring stable operation of the hydrogen production equipment to ensure an adequate hydrogen supply. In the event of continuous rain or calm weather, the system relies on the supercritical CO2 Brayton cycle units to maintain minimum production requirements, avoiding damage to equipment caused by frequent shutdowns, while continuing to use stored hydrogen to produce methanol.

[0111] The second typical day (PV-dominated): The wind-solar power supply ratio is adjusted according to the preset second wind-solar ratio (for example, 1:2, meaning wind power accounts for 1 / 3 of the power supply and solar power accounts for 2 / 3). The distribution of wind and solar resources on this typical day is characterized by weak and intermittent winds, but strong solar radiation. While wind and solar output can complement each other to a certain extent, there are still many periods of low wind and solar power. The system's operating strategy is to use all electricity to produce hydrogen for methanol synthesis during periods of strong wind and sunlight. However, the methanol production rate will be appropriately slowed while maintaining the minimum methanol production rate, and excess hydrogen will be stored in hydrogen storage tanks. During periods of weak wind 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 an extended period, the supercritical CO2 Brayton cycle unit will be relied upon to provide a stable power supply, maintaining the minimum operating threshold of the hydrogen production and synthesis systems and ensuring continuous and stable methanol production.

[0112] The third typical day (wind power dominated): The wind-solar power supply ratio is adjusted according to the preset second wind-solar power supply ratio value (for example, 3:2, that is, wind power accounts for 3 / 5 of the power supply and solar power accounts for 2 / 5 of the power supply). On this typical day, the wind speed increases but the sunlight gradually decreases, and the overall energy supply is volatile. When the system is in operation, during periods of good wind and sunlight, it will efficiently utilize wind and solar power to maximize hydrogen production and synthesize methanol, and store excess energy. When the wind is strong but the sunlight is insufficient, wind power generation is prioritized to maintain hydrogen production, and short-term energy storage equipment is used to smooth power fluctuations. In the event of long periods of rainy weather with no wind and no sunlight, the system will rely on supercritical CO2 Brayton cycle units to maintain minimum production requirements, improve the overall utilization of wind and solar resources, avoid frequent shutdowns that cause damage to equipment, and at the same time use stored hydrogen to continue methanol production to ensure the continuity of methanol production.

[0113] The fourth typical day (wind-solar complementarity): The wind-solar power supply ratio was adjusted according to the preset second wind-solar ratio (for example, 1:1, with wind power accounting for 1 / 2 and solar power accounting for 1 / 2). The distribution characteristics of wind and solar power generation resources on this typical day exhibited a coupling characteristic of seasonal attenuation of irradiation intensity and sustained increase in wind speed, resulting in a significant spatiotemporal superposition effect of renewable energy output, exacerbating the peak load regulation and absorption pressure of the power grid. The system adopted a multi-system joint control strategy to achieve dynamic source-load matching. Under conditions of abundant wind and solar resources, the water electrolysis hydrogen production unit and the CO2 catalytic hydrogenation methanol synthesis reactor both operated at rated load. When the wind and solar output dropped to the critical range, priority was given to ensuring the minimum stable load of the water electrolysis hydrogen production system and the electricity required for continuous production of the methanol reactor. Under extreme resource-scarce conditions, the supercritical CO2 Brayton cycle unit provided a stable power supply, maintaining the minimum operating thresholds of the hydrogen production system and the methanol synthesis system, ensuring continuous and stable methanol production.

[0114] In summary, the above strategies for regulating the various subsystems in the system demonstrate significant time-dependence and volatility in both electric power and hydrogen power. On the fourth typical day, wind power output was high, photovoltaic output was low, and load was medium. The output distribution across the wind, solar, and storage systems was similar. On the third typical day, wind power output was high, photovoltaic output was low, and load was high. On the first and second typical days, photovoltaic output was high, wind power output was low, and load was medium.

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

[0116] Step 161, obtaining target light radiation intensity and target wind speed in a target time period;

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

[0118] Step 163, calculating the wind-solar power generation load for each typical day according to the preset wind-solar ratio data;

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

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

[0121] In some optional implementations, step 162 clusters the target light radiation intensity and target wind speed to obtain multiple typical days, including:

[0122] Step 1621 , pre-processing the target light radiation intensity and target wind speed to obtain equidistant grid data;

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

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

[0125] In this embodiment, the illumination radiation intensity for the entire previous year may be a pre-stored grayscale image, or it may be a illumination radiation intensity value obtained by establishing a mapping relationship between pixel values ​​and actual illumination based on the stored grayscale image through a calibration experiment or a radiation calibration model.

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

[0127] In this embodiment, step 1621 requires sequentially processing the target illumination radiation intensity and the target wind speed. Specifically, 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] Taking the target light radiation intensity as an example, the steps to obtain the target light equidistant grid data are as follows:

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

[0130] 2. Then, normalize the cleaned data to eliminate dimensional differences and ensure that each feature contributes evenly to the distance. For example, light radiation: normalize to [0, 1] (divided by the theoretical maximum value, such as 1361W / m 2 ); then, equidistant grid data is generated based on the standardized illumination data (the spatial resolution of each grid cell in the equidistant grid is consistent).

[0131] 3. Generate target illumination equidistant grid data. Use inverse distance weighting to obtain spatial interpolation; select a high-resolution grid (e.g., 100m grid) as the grid resolution; perform boundary processing and terrain correction based on the digital elevation model (DEM), spatial interpolation, and grid resolution to obtain target illumination equidistant grid data.

[0132] Taking the target wind speed as an example, the steps to obtain the target wind speed equidistant grid data are as follows:

[0133] 1. Because data may be missing, duplicated, or erroneous, it is necessary to first process missing values ​​and remove abnormal 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. Then, normalize the cleaned data to eliminate dimensional differences and ensure that each feature contributes equally to distance. For example, for wind speed, Z-score normalization or Weibull distribution fitting (if probabilistic modeling is required) is performed. Then, generate equidistant grid data based on the normalized 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 using the radial basis function (RBF). A 100-meter grid is selected as the grid resolution. Correcting for altitude differences based on the wind profile law, spatial interpolation, and grid resolution are performed for boundary processing and terrain correction to calculate target wind speed equidistant grid data.

[0136] To apply logarithmic wind profiles to correct height differences, refer to:

[0137]

[0138] Among them, 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 by 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 the wind speed curve. It is necessary to determine the illumination radiation intensity curve and the wind speed curve based on the target illumination equidistant grid data and the target wind speed equidistant grid data, respectively. That is, the number of clusters K is determined by the elbow rule; clustering is performed 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 referred to:

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

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

[0142] 3. According to the preset time step (the time step can be a certain day of the year), the target illumination equidistant grid data is flattened into a feature vector, and then the distance between the flattened feature vector and the optimal k value is calculated. Clustering is completed 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 of each time step is calculated, and the sample closest to the center in each cluster is selected as a representative to draw a curve to obtain the light radiation intensity curve.

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

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

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

[0147] 3. According to the preset time step (the time step can be a certain day of the year), the target wind speed equidistant grid data is flattened into a feature vector, and then the distance between the flattened feature vector and the optimal k value is calculated. Clustering is completed 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 of each time step is calculated, and the sample closest to the center in each cluster is selected as the representative to draw the curve to obtain the wind speed intensity curve.

[0149] The above algorithm is based on distance measurement and divides the data into different clusters through iterative operations, so that the data within the cluster is highly similar and the data between clusters is highly different, thereby compressing the complex time series data into four representative typical daily curves. At the same time, the same operation is performed on the power load data to retain the core fluctuation characteristics.

[0150] As an example, the light radiation intensity curve can be referred to Figure 3 , the wind speed curve can be referred to Figure 4 .

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

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

[0153] Step 1631, determining 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 power load based on the photovoltaic power generation model and photovoltaic power generation parameters corresponding to a typical day;

[0155] Step 1633: Calculate the wind-solar power generation load for each typical day based on the multiple wind-solar power ratio values, wind power load, and photovoltaic power load in the preset wind-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 the wind power load and the photovoltaic power load is adjusted according to multiple wind-solar ratio values ​​to achieve the purpose of stable and maximum power supply.

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

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

[0159]

[0160] Among them, v ci , v R and v co They are the cut-in wind speed, rated wind speed and cut-out wind speed of the fan, in m / s.

[0161] v ci : The cut-in wind speed of the wind turbine, that is, the minimum wind speed at which the wind turbine starts to generate electricity, in m / s.

[0162] v R : The rated wind speed of the fan, that is, the wind speed when the fan reaches the rated power output, in m / s.

[0163] v co : The cut-out wind speed of the wind turbine, that is, the wind speed at which the wind turbine stops generating electricity, in m / s.

[0164] v: Actual wind speed, which is an input value. The value range has different calculation methods according to different intervals. The unit is m / s. It is obtained by actual measurement of wind speed measuring instruments in wind farms.

[0165] P WT (υ) is the output power of the wind power generation system. It is the output value in watts and is used as the power output value of the wind power generation system for subsequent power system analysis and power balance calculations.

[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 : The output power of the wind power generation system, in watts (W). It is used as the power output value of the wind power generation system for subsequent power system analysis, power balance calculations, etc.

[0170] G: Light radiation intensity, in watts per square meter (W / m 2 ), which represents the solar radiation power received per unit area, is actually measured by a light radiation sensor in a photovoltaic power plant.

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

[0172] T STC : Temperature under standard test conditions, usually 25°C.

[0173] β: Temperature coefficient related to component characteristics, indicating the characteristics of component power changing with temperature.

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

[0175] G STC : Light radiation intensity under standard test conditions, usually 1000W / m 2 .

[0176] For step 1633, for example, the wind-solar ratio values ​​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 generation 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 generation 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 generation 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 generation load on the fourth typical day is 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 optional implementations, step 164, determining a typical day wind-solar power ratio value based on the wind-solar power generation load, includes:

[0186] Step 1641: determine the preset wind-solar ratio value corresponding to the maximum value of multiple wind-solar power generation loads as the wind-solar ratio value of a typical day.

[0187] Still taking the above example, for the first typical day, select w 总11 、w 总12 and w 总13 The largest corresponding wind-solar ratio value among is used as the wind-solar ratio value for the first typical day. 总11 is the largest, then 5:5 is the wind-solar ratio value for the first typical day. The wind-solar ratio values ​​for other typical days can be determined in the same way.

[0188] Based on the embodiment of the above method, Figure 2 As an embodiment of a specific application of the present invention, Figure 2 In the application scenario shown, during the fourth typical day, from midnight to 5:00 AM, system electricity demand is low, and there is significant surplus wind power, resulting in a high electrolyzer hydrogen production rate. From 5:00 AM to 11:00 AM, electricity demand increases, but wind and solar power generation decreases, requiring electrolyzer hydrogen production rate to be reduced to meet demand. From 11:00 AM to 4:00 PM, system power generation exceeds demand, and electrolyzer hydrogen production rate gradually increases. After 4:00 PM, wind and solar power generation decreases, requiring electrolyzer hydrogen production rate to be reduced. Hydrogen storage on the first, second, and third typical days occurs primarily at night, with methanol produced from the stored hydrogen during the day. On the fourth typical day, hydrogen storage occurs primarily during the day, with methanol produced from the stored hydrogen at night. If wind power fluctuations during hydrogen production lead to unstable power supply, the continuity of water electrolysis hydrogen production can be compromised, further 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 operations such as methanol synthesis.

[0190] The hydrogen production curve is not stable when the same proportion of wind and solar power is used for power generation. Therefore, the appropriate ratio can be obtained through the wind-solar ratio to keep the hydrogen supply curve stable and reduce its energy consumption.

[0191] Under four typical day scenarios, the impact of different wind-solar ratios (9:1 to 1:9) on the system was tested, quantifying key metrics: the mismatch between wind-solar output and load demand, the peak-shaving capacity and hydrogen utilization rate of hydrogen storage tanks, and the efficiency of the carbon capture system in capturing carbon emissions from thermal power plants. Simulations confirmed that a 6:4 wind-solar ratio (60% wind power, 40% PV) was optimal for each typical day across all key metrics. Specifically, by analyzing the coordinated output of various system components, including electrical power balance, hydrogen balance, and CO2 balance, the system's performance under different ratios was comprehensively evaluated, including power shortfall, energy storage utilization, hydrogen storage and consumption, and carbon emissions. For example, on Typical Day 1, a 6:4 wind-solar ratio resulted in a minimum power shortfall of only 48MW, achieving an optimal balance between energy storage deployment frequency and capacity utilization. Hydrogen generation, storage, and consumption dynamics also varied across different ratios on different typical days. Based on these analytical results, a 6:4 wind-solar ratio was determined to be the optimal wind-solar ratio for each typical day.

[0192] In combination with current application scenarios, when testing different wind-solar ratios, we can establish balance equations based on the conservation of electrical energy, thermal energy, hydrogen mass, and carbon dioxide mass. Using the balance equations as constraints, we can coordinate the operation of wind-solar generators, electrolyzers, methanol synthesis systems, and carbon capture systems to achieve optimized power supply based on the wind power ratio of the day. The balance equations can be referenced as follows:

[0193]

[0194] Among them, P PGU is the output of thermal power unit, in kW; P WT is the wind power output, in kW; P PV is the photovoltaic output, in kW; P cCS Electricity consumed for carbon capture, in kW; P EL The electrical energy consumed by electrolysis is in kW; The electric energy required for methanol synthesis, in kW; P demand It is the electricity demand at the user side, in kW.

[0195]

[0196] in, The heat energy provided for the heat exchange of carbon dioxide working fluid cycle, Q C,out Output heat energy to the molten salt tank, Q C,in is the heat energy input to the molten salt tank, Q CCS The heat energy required for the carbon capture and desorption tower.

[0197]

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

[0199]

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

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

[0202] An acquisition module 51 is configured to acquire the current light radiation intensity and the current wind speed of a preset area in a current preset time period;

[0203] A first processing module 52 is configured to calculate similarities between the current light radiation intensity and the light radiation intensities corresponding to multiple typical days to obtain multiple first similarities;

[0204] A second processing module 53 is configured to calculate similarities between the current wind speed and wind speeds corresponding to multiple typical days to obtain multiple second similarities;

[0205] A third processing module 54 is configured to determine a target typical day based on the plurality of first similarities and the plurality of second similarities, each typical day corresponding to a wind-solar ratio strategy;

[0206] A fourth processing module 55 is configured 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 configured to:

[0209] Obtaining a spatial illumination distribution image of a preset area in a current preset time period;

[0210] Performing grayscale processing and feature extraction on the spatial illumination distribution image to obtain a current illumination radiation intensity vector;

[0211] Extract features of the light radiation intensity corresponding to multiple typical days in turn to obtain multiple typical light radiation intensity vectors;

[0212] Cosine similarities are calculated between the current illumination radiation intensity vector and a plurality of typical illumination radiation intensity vectors in sequence to obtain a plurality of first similarities.

[0213] Optionally, the second processing module 53 is configured to:

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

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

[0216] Optionally, the third processing module 54 is configured to:

[0217] The typical day corresponding to the maximum value among the plurality of first similarities and / or the maximum value among the plurality of second similarities is set as the target typical day; and / or,

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

[0219] Optionally, a sixth processing module 57 is further included, configured to:

[0220] Obtain the target light radiation intensity and target wind speed in the target time period;

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

[0222] Calculate the wind and solar power generation load for each typical day based on the preset wind and solar power ratio data;

[0223] The wind-solar ratio value for a typical day is determined based on the wind-solar power generation load.

[0224] Optionally, the sixth processing module 57 is configured to:

[0225] Preprocessing the target light radiation intensity and target wind speed to obtain equidistant grid data;

[0226] Determine the cluster number K by the elbow rule; perform clustering based on the cluster number K and the equidistant grid data to obtain a light radiation intensity curve and a wind speed curve;

[0227] A plurality of typical days are determined according to the light radiation intensity curve and the wind speed curve.

[0228] Optionally, the sixth processing module 57 is configured to:

[0229] Determine the wind power load based on the wind power generation model and the wind parameters corresponding to a typical day;

[0230] Determine the photovoltaic power load based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day;

[0231] Calculate the wind and solar power generation 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;

[0232] Determining the wind-solar power ratio value on a typical day based on the wind-solar power generation load includes:

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

[0234] Optionally, the fifth processing module 56 is configured to:

[0235] The first wind-solar ratio strategy includes: adjusting the wind-solar power supply ratio according to a preset first wind-solar ratio value, and combining with a supercritical CO2 Brayton cycle unit to maintain the minimum power load required for producing hydrogen and / or methanol;

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

[0237] The third wind-solar power supply ratio 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 maximum power load required for hydrogen production;

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

[0239] Figure 6 An example of a physical structure diagram of an electronic device is shown below. Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640. The processor 610, the communication interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the above-mentioned method for determining the wind-solar capacity ratio strategy.

[0240] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0241] The device embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0242] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or certain parts of the embodiment.

[0243] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for determining a wind-solar capacity ratio strategy, characterized in that: include: Obtain the current light radiation intensity and current wind speed of the preset area in the current preset time period; Calculating similarities between the current illumination radiation intensity and illumination radiation intensities corresponding to multiple typical days to obtain multiple first similarities; Calculating similarities between the current wind speed and wind speeds corresponding to multiple typical days to obtain multiple second similarities; Determining target typical days according to the multiple first similarities and the multiple second similarities, each typical day corresponding to a wind-solar ratio strategy; The wind-solar ratio strategy corresponding to the target typical day is set as the current wind-solar ratio strategy; The electric load required for the power generation system in the preset area to produce hydrogen and / or methanol is adjusted according to the current wind-solar ratio strategy.

2. The method for determining the wind-solar capacity ratio strategy according to claim 1, characterized in that: Similarity calculation is performed on the current light radiation intensity and the light radiation intensities corresponding to multiple typical days to obtain multiple first similarities, including: Obtaining a spatial illumination distribution image of a preset area in a current preset time period; Performing grayscale processing and feature extraction on the spatial illumination distribution image to obtain a current illumination radiation intensity vector; Extract features of the light radiation intensity corresponding to multiple typical days in turn to obtain multiple typical light radiation intensity vectors; Cosine similarities are calculated between the current illumination radiation intensity vector and a plurality of typical illumination radiation intensity vectors in sequence to obtain a plurality of first similarities.

3. The method for determining the wind-solar capacity ratio strategy according to claim 1, characterized in that: Calculating similarities between the current wind speed and 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 sequentially subtracted from the wind speeds corresponding to multiple typical days to obtain multiple second similarities.

4. The method for determining the wind-solar capacity ratio strategy according to claim 1, characterized in that: Determining a target typical day according to the plurality of first similarities and the plurality of second similarities includes: The typical day corresponding to the maximum value among the plurality of first similarities and / or the maximum value among the plurality of second similarities is set as the target typical day; and / or, According to each typical day, the first similarity and the second similarity corresponding to the typical day are added, and the typical day corresponding to the maximum value of the added values ​​is set as the target typical day.

5. The method for determining the wind-solar capacity ratio strategy according to claim 4 is characterized in that: Each wind-solar power supply matching strategy corresponds to a wind-solar power supply matching value. The wind-solar power supply matching value on a typical day is determined by the following process: Obtain the target light radiation intensity and target wind speed in the target time period; Clustering the target light radiation intensity and target wind speed to obtain multiple typical days; Calculate the wind and solar power generation load for each typical day based on the preset wind and solar power ratio data; The wind-solar ratio value for a typical day is determined based on the wind-solar power generation load.

6. The method for determining the wind-solar capacity ratio strategy according to claim 4, characterized in that: Clustering the target light radiation intensity and target wind speed to obtain multiple typical days, including: Preprocessing the target light radiation intensity and target wind speed to obtain equidistant grid data; Determine the number of clusters K by the elbow rule; Clustering is performed according to the cluster number K and the equidistant grid data to obtain a light radiation intensity curve and a wind speed curve; A plurality of typical days are determined according to the light radiation intensity curve and the wind speed curve.

7. The method for determining the wind-solar capacity ratio strategy according to claim 4, characterized in that: Based on the preset wind-solar ratio data, calculate the wind-solar power generation load for each typical day, including: Determine the wind power load based on the wind power generation model and the wind parameters corresponding to a typical day; Determine the photovoltaic power load based on the photovoltaic power generation model and the photovoltaic power generation parameters corresponding to a typical day; Calculate the wind and solar power generation 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; Determining the wind-solar power ratio value on a typical day based on the wind-solar power generation load includes: The preset wind-solar ratio value corresponding to the maximum value among multiple wind-solar power generation loads is determined as the wind-solar ratio value for a typical day.

8. The method for determining the wind-solar capacity ratio strategy according to claim 7, characterized in that: 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: The first wind-solar ratio strategy includes: adjusting the wind-solar power supply ratio according to a preset first wind-solar ratio value, and combining with a supercritical CO2 Brayton cycle unit to maintain the minimum power load required for producing hydrogen and / or methanol; The second wind-solar ratio strategy includes: adjusting the wind-solar power supply ratio according to a preset second wind-solar ratio value, and supplying power according to the minimum power load required for methanol production and the maximum power load required for hydrogen production; The third wind-solar power supply ratio 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 maximum power load required for hydrogen production; The fourth wind-solar ratio strategy includes: adjusting the wind-solar power supply ratio according to a preset fourth wind-solar ratio value, and supplying power according to the maximum electrical load required for producing hydrogen and / or methanol.

9. A device for determining a wind-solar capacity ratio strategy, characterized in that: include: An acquisition module is used to obtain the current light radiation intensity and current wind speed of a preset area in a current preset time period; A first processing module is configured to calculate similarities between the current light radiation intensity and the light radiation intensities corresponding to multiple typical days to obtain multiple first similarities; A second processing module is configured to calculate similarities between the current wind speed and wind speeds corresponding to multiple typical days to obtain multiple second similarities; A third processing module is configured to determine a target typical day based on the multiple first similarities and the multiple second similarities, where each typical day corresponds to a wind-solar ratio strategy; A fourth processing module is configured 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.

10. An electronic device, characterized in that: include: processor, memory, and bus, wherein 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 calls the program instructions to execute the steps of the method for determining the wind-solar capacity ratio strategy as described in any one of claims 1 to 8.

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