A photovoltaic greenhouse flexible adjustment potential evaluation method based on data mechanism combined driving

Abstract

The application provides a photovoltaic greenhouse flexible adjustment potential evaluation method based on data mechanism joint driving, relates to the technical field of comprehensive energy systems, and comprises the following steps: based on a photovoltaic greenhouse architecture, a photovoltaic greenhouse micro-grid model is constructed; the influence of the correlation and uncertainty of outdoor environmental factors on the demand of indoor environmental factors of the photovoltaic greenhouse is represented by using a Gaussian mixture model, and a data-mechanism joint driving-based photovoltaic greenhouse crop full-growth-period energy demand model is established by combining the dynamic coupling characteristics of crops on the demand of indoor environmental factors of the photovoltaic greenhouse; in different growth stages of crops, an optimal scheduling model is established by taking the maximum photovoltaic greenhouse power adjustment potential at each moment as the target according to the actual environmental factor demand of crops and LAI and DMA constraints, so that the maximum flexible adjustment potential of the photovoltaic greenhouse at different moments is obtained. The application provides technical support for the flexible participation of the photovoltaic greenhouse in power distribution network demand response.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system technology, and more specifically, to a method for assessing the flexible regulation potential of photovoltaic greenhouses based on data mechanism-driven approaches. Background Technology

[0002] Against the backdrop of "dual carbon" goals and the green energy transition, the high proportion of distributed photovoltaic (PV) grid integration exacerbates the time mismatch between power sources and loads, leading to a significant increase in the uncertainty of the net load curve and a widening peak-to-valley difference. This results in severe overload on the distribution network and pressure on local PV consumption. Traditional solutions such as line upgrades and energy storage configurations can alleviate these problems to some extent, but their high investment costs make them difficult to meet the requirements of economic planning for the distribution network. Meanwhile, driven by the rural revitalization strategy, new agricultural industries, represented by photovoltaic greenhouses, are rapidly emerging. Their electricity loads possess strong adjustability and flexibility margins, and they are expected to participate in grid demand response as a new type of distributed resource, thereby reducing the expansion and upgrading costs of the distribution network. In this context, accurately assessing the flexible adjustment potential of photovoltaic greenhouses, quantifying their capacity boundary for participating in demand response, and establishing constraints in their interaction with the grid have become critical issues that urgently need to be addressed.

[0003] The participation of flexible resources in distribution network demand response plays a crucial role in improving the economic efficiency of distribution network planning schemes. However, current research lacks detailed modeling of the energy demand of photovoltaic greenhouses based on the dynamic growth process of crops throughout their entire growth cycle, and research on assessing their flexible regulation potential. Particularly in industrial energy consumption modeling, existing studies fail to adequately characterize the dynamic coupling and interaction mechanisms between environmental factors at different crop growth stages, making it difficult to accurately reflect their time-varying energy consumption characteristics. Consequently, it is impossible to precisely characterize the boundary range of photovoltaic greenhouses as flexible resources participating in grid interaction regulation. Summary of the Invention

[0004] Currently, there is no established method for assessing the flexible regulation potential of photovoltaic greenhouses based on data mechanisms. To address this issue, this invention constructs a two-stage crop growth characterization model based on crop growth theory, where the seedling stage is characterized by leaf area index and the vigorous growth stage by dry matter accumulation. Furthermore, by analyzing the dynamic demand curves of light intensity, temperature, and CO2 environmental factors and their interactive coupling effects, a quantitative model of photovoltaic greenhouse energy demand with agricultural ecological adaptability is established, and a method for assessing the flexible regulation potential of photovoltaic greenhouses is proposed.

[0005] Therefore, this invention provides a method for evaluating the flexible adjustment potential of photovoltaic greenhouses based on data mechanism joint driving, comprising the following steps: Step 1: Based on the photovoltaic greenhouse architecture, construct a photovoltaic greenhouse microgrid model; Step 2: Use a Gaussian mixture model to characterize the impact of the correlation and uncertainty of outdoor environmental factors on the demand for environmental factors inside the photovoltaic greenhouse. Combine the dynamic coupling characteristics of the demand of photovoltaic greenhouse crops for environmental factors inside the photovoltaic greenhouse to establish a data-mechanism joint-driven energy demand model for the entire growth cycle of photovoltaic greenhouse crops. The entire growth cycle of crops includes the seedling stage and the vigorous growth stage. Step 3: At different growth stages of the crop, based on the actual environmental factor requirements of the crop, as well as LAI and DMA constraints, an optimal scheduling model is established with the goal of maximizing the power regulation potential of the photovoltaic greenhouse at each time point, so as to obtain the maximum flexible regulation potential of the photovoltaic greenhouse at different times.

[0006] Furthermore, in step 1, the photovoltaic greenhouse structure includes: electric irrigation and drainage equipment, spatial electric field equipment, physical pest control device, supplemental lighting, electric heating equipment, electric refrigeration equipment, and carbon dioxide fertilizer generator.

[0007] The present invention has the following technical effects: (1) Taking into account the uncertainty and correlation of outdoor environmental factors affecting the growth of greenhouse crops, and by analyzing the dynamic demand of environmental factors such as light intensity, temperature and CO2 in the greenhouse and their interactive coupling effect, a quantitative model of energy demand for photovoltaic greenhouses with agricultural ecological adaptability was established.

[0008] (2) Considering the constraints of leaf area index during the seedling stage and dry matter accumulation during the vigorous growth stage, a method for evaluating the flexible adjustment potential of photovoltaic greenhouses is proposed, which provides a theoretical basis and technical support for the flexible participation of photovoltaic greenhouses in the demand response of power distribution networks. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the photovoltaic greenhouse structure of the present invention; Figure 2 This is a flowchart of the photovoltaic greenhouse flexible adjustment potential assessment method based on data mechanism joint drive of the present invention; Figure 3(a) is a schematic diagram of typical daily outdoor temperature and solar radiation intensity during the seedling stage of the present invention; Figure 3(b) is a schematic diagram of outdoor temperature and solar radiation intensity on a typical day during the peak season of the present invention; Figure 4(a) is a schematic diagram of the flexible adjustment potential of the photovoltaic greenhouse during a typical day in the seedling stage of the present invention; Figure 4(b) is a schematic diagram of the flexible adjustment potential of the photovoltaic greenhouse during a typical day of peak growth according to the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] I. Greenhouse Microgrid Modeling 1. Basic Structure of a Photovoltaic Greenhouse A photovoltaic greenhouse mainly consists of electric heating / cooling equipment, supplemental lighting, and carbon dioxide fertilizer generators to meet the dynamic requirements of crops for environmental factors such as temperature, light intensity, and carbon dioxide concentration. Simultaneously, considering the margin range of crop requirements for environmental factors and the impact of the coupling and interaction effects of environmental factors on crop growth, the power regulation potential of the photovoltaic greenhouse is evaluated. Utilizing the flexible regulation capabilities of the photovoltaic greenhouse to participate in grid demand response can effectively solve the source-load timing mismatch problem, improve the photovoltaic carrying capacity of the distribution network, and achieve two-way interaction between the grid and load. The basic structure of a photovoltaic greenhouse is as follows: Figure 1 As shown.

[0013] 2. Photovoltaic greenhouse equipment modeling (1) Electric irrigation and drainage equipment Crop irrigation is influenced by factors such as temperature, light intensity, and humidity. The Penman-Monteith equation is used to calculate the reference evapotranspiration. :

[0014] In the formula: The slope of the saturated vapor pressure-temperature curve; Net radiation; Soil heat flux; This is the hygrometer constant; Average temperature; The wind speed at a height of 2m; and These are the saturated vapor pressure and the actual vapor pressure, respectively.

[0015] Combined with crop coefficient The actual water demand can be obtained. for:

[0016] Therefore, the power requirements of electric irrigation and drainage equipment for:

[0017] In the formula: The density of water; It is the acceleration due to gravity; Water flow rate; For the water pump head; For irrigated area; For irrigation efficiency; This refers to the daily irrigation time.

[0018] (2) Space electric field The space electric field utilizes a direct current voltage to generate an electric field, which can not only eliminate bacteria but also ensure that the humidity inside the greenhouse remains within a normal range. Its mathematical model is as follows:

[0019] In the formula: This represents the total electrical power consumed by the electric field within the greenhouse space. This represents the total rated power of the electric field equipment within the greenhouse. The proportion of the number of electric field devices operating in the greenhouse at time t. .

[0020] (3) Physical insecticidal lamps Based on the phototaxis of insects, physical pest control devices can attract and kill pests using light sources of specific wavelengths, effectively suppressing pest population density in greenhouses and reducing their impact on crop growth and development. The mathematical model is as follows:

[0021] In the formula: This represents the total power consumption of the physical insecticidal lamps inside the greenhouse. This refers to the total rated power of the physical insecticidal lamps inside the greenhouse. The percentage of physically operating insecticidal lamps in the greenhouse at time t. .

[0022] (4) Fill light Irradiance of fill light With electricity demand The relationship is:

[0023] In the formula: Photoelectric conversion efficiency; The effective area for uniform illumination by the supplementary lighting.

[0024] (5) Electric heating / cooling equipment

[0025] In the formula: , These represent the cooling and heating power of the electric heating equipment and the electric chiller at time t, respectively. , These are the power consumption of the electric heating equipment and the electric chiller at time t, respectively. , These refer to the electrothermal and electrocooling conversion efficiencies of electric heating equipment and electric refrigeration machines, respectively.

[0026] (6) Carbon dioxide fertilizer generator

[0027] In the formula: Let t be the electrical power consumption of the carbon dioxide fertilizer generator at time t; The amount of carbon dioxide replenished to the carbon dioxide fertilizer generator at time t; Energy conversion efficiency; This refers to the volume of the greenhouse.

[0028] II. Multi-stage energy demand modeling of photovoltaic greenhouse crops based on data-mechanism joint driving mechanism A data- and mechanism-driven energy demand model for the entire growth cycle of photovoltaic greenhouse crops is established. This model utilizes a Gaussian mixture model to characterize the impact of the correlation and uncertainty of outdoor environmental factors on the demand for indoor environmental factors, while also considering the dynamic coupling characteristics of photovoltaic greenhouse crops to the demand for indoor environmental factors. Based on this model, a method for assessing the power regulation potential of photovoltaic greenhouses is proposed.

[0029] 1. Data-driven modeling of the correlation and uncertainty of outdoor environmental factors Because the effects of temperature, light intensity, and CO2 concentration on crop growth at different stages are uncertain and do not perfectly follow a Gaussian distribution, a Gaussian mixture model is used to quantify the uncertainty of outdoor environmental factors.

[0030] The probability density function of a single uncertain factor is:

[0031] In the formula: z is a random variable ; It is the m-th one-dimensional normal distribution; The weight of the m-th Gaussian component; and denoted as mean and variance of the m-th Gaussian component, respectively; M is the number of Gaussian components.

[0032] Since temperature, light intensity, and CO2 concentration are correlated, the multivariate joint probability density function of these three factors can be obtained according to the marginal probability invariance theorem:

[0033] In the formula: , , denoted as the variances of temperature, light intensity, and CO2 concentration in the m-th Gaussian distribution, respectively. , , denoted as covariances of temperature and light intensity, temperature and CO2 concentration, and light intensity and CO2 concentration in the m-th Gaussian distribution, respectively; d is the dimension (3-dimensional) of the random variable matrix.

[0034] 2. Mechanism-driven modeling of crop energy demand in stages To address the differentiated dynamic demands of greenhouse crops on environmental factors at different growth stages and their coupling relationships, we selected radiative heat and dry matter accumulation as indicators to measure crop growth status during the seedling and vigorous growth stages, respectively, and constructed a mechanism-driven staged energy demand model for crops.

[0035] (1) Seedling stage growth model When crops are in the seedling stage, their photosynthetic rate is slow, and the carbon dioxide concentration has a negligible effect on crop growth. Therefore, the effects of temperature and light on crop growth are mainly considered during the seedling stage. The effects of temperature and light on crop growth are represented by relative heat effect (RTE) and photosynthetically active radiation (PAR), respectively. The product of RTE and PAR is defined as the radiation heat product (TEP). The dynamic relationship model between TEP and LAI (leaf area index) is then fitted using a logistic curve, as shown in Equation (12).

[0036] In the formula: , The highest and lowest temperatures that greenhouse crops can tolerate during their seedling stage; , These are the upper and lower limits of the optimal growth temperature for greenhouse crops during the seedling stage; Let be the indoor temperature at time t; , These represent the energy percentage of the PAR band in solar radiation and the light radiation output from supplemental lighting, respectively. The light transmittance of the greenhouse; Let be the solar irradiance at time t.

[0037] The expression for the temperature inside the greenhouse is:

[0038] In the formula: , , , These represent the heat power of solar irradiance at time t, the heat transfer between the air inside the greenhouse and the covering layer, the heat transfer through ventilation, and the heat power exchange with the outside due to long-wave radiation. , These are the density and specific heat capacity of air, respectively. The photothermal conversion efficiency of solar radiation; The effective area exposed to sunlight; The heat transfer coefficient of the greenhouse microgrid; The heat transfer area of ​​the greenhouse; Let be the outdoor temperature at time t; Ventilation volume; Ventilation area; For long-wavelength emission rate; It is the Stefan-Boltzmann constant; The area of ​​the photovoltaic greenhouse is the light-transmitting area.

[0039] (2) Growth model during the vigorous growth period During the vigorous growth period, crop photosynthesis accelerates, and carbon dioxide concentration has a significant impact on crop growth. Therefore, the effects of light, temperature, and carbon dioxide concentration on crop growth need to be considered during the vigorous growth period. The dry matter accumulation (DMA) is used to measure crop growth rate, which refers to the difference between the organic matter accumulated by the photosynthetic rate and the organic matter consumed by the respiration rate of greenhouse crops, as shown in Equation (16).

[0040] In the formula: , These represent the organic matter accumulated by crop photosynthesis and the organic matter consumed by respiration at time t, respectively.

[0041] The functional relationship between temperature and crop photosynthetic rate based on the Arrhenius equation It can be represented as:

[0042] In the formula: Pre-exponential factors; Activation energy; is the gas constant.

[0043] Characterized by the Michaelis equation Functional relationship between concentration and crop photosynthetic rate It can be represented as:

[0044] In the formula: This represents the maximum photosynthetic rate of the crop. Let be the CO2 concentration at time t within the greenhouse microgrid; This is the CO2 half-saturation constant.

[0045] A non-rectangular hyperbolic model was used to characterize the functional relationship between light intensity and crop photosynthetic rate. It can be represented as:

[0046] In the formula: For apparent quantum efficiency; For the convexity of the curve.

[0047] Therefore, the photosynthetic rate and respiration rate of crops are respectively:

[0048] In the formula: The baseline respiration rate of crops at the reference temperature; For reference temperature; This is the temperature coefficient.

[0049] The expression for the CO2 concentration inside the greenhouse is:

[0050] In the formula: The amount of CO2 absorbed by crops; CO2 lost through greenhouse ventilation; Let be the net radiative power of the crop leaf area index at time t; Let t be the outdoor CO2 concentration.

[0051] 3. Assessment of the Flexible Adjustment Potential of Photovoltaic Greenhouses The relationship between crop seedling growth index (LAI), maturity growth index (DMA), and electricity demand function can be expressed as:

[0052] Therefore, by establishing an optimal scheduling model based on the actual environmental demand of crops and the constraints of LAI and DMA at different stages, with the goal of maximizing the power regulation potential of the photovoltaic greenhouse at each time point, the maximum flexible regulation potential of the photovoltaic greenhouse at different times can be obtained.

[0053] In the formula: , These represent the potential for power increase and decrease in the greenhouse microgrid at time t, respectively. , functions respectively or The maximum and minimum values; For function or The actual value.

[0054] Therefore, the potential ranges for power adjustment and reduction of photovoltaic greenhouses at different times under different scenarios are as follows: and .

[0055] 4. Case Analysis 1. Case Study Background The strawberry variety grown in the photovoltaic greenhouse is the everbearing strawberry, which can be harvested twice a year. Planting times are divided into spring planting in March and autumn planting in September. 7-10 days after planting, the strawberry enters the seedling stage, which lasts 4-6 weeks before reaching its peak growth period, typically in June or December. This example uses typical days during the seedling stage in March and the peak growth period in June for simulation calculations. The meteorological data for each typical day are shown in Figures 3(a) and 3(b). The optimal growth temperature ranges for the seedling and peak growth stages are 18-25℃ and 20-28℃, respectively; the optimal photosynthetically active radiation ranges are 100-300 μmol / (m²·s) and 300-600 μmol / (m²·s), respectively; and the optimal carbon dioxide concentration range during the peak growth period is 600-900 ppm, respectively.

[0056] 2. Results Analysis As shown in Figure 4(a), when spring-planted strawberries are in the seedling stage in March, the photovoltaic greenhouse integrated energy system has the potential to increase and decrease throughout the day due to the influence of the low outdoor temperature. Since the temperature control equipment in the greenhouse has the greatest adjustment potential, and the maximum outdoor temperature occurs at 14:00, the photovoltaic greenhouse integrated energy system has the greatest potential to decrease at this time, with a maximum decrease potential of 116.3kW.

[0057] As shown in Figure 4(b), during the peak growing season of spring-planted strawberries in June, the outdoor temperature is very high. To ensure that the crop is within its optimal growth range, the temperature control equipment is on most of the time. The outdoor temperature reaches its highest point at 13:00 noon, and the carbon supplementation equipment is also on at this time. Therefore, the maximum upward adjustment potential is 169.6 kW. Similarly, the maximum downward adjustment potential is at 19:00, with a maximum downward adjustment potential of 138.4 kW.

[0058] As described above, this invention considers the dynamic changes and interactions of environmental factor requirements throughout the entire growth cycle of photovoltaic greenhouse crops, and uses leaf area index and dry matter accumulation indicators to measure the crop growth status at different stages, thus constructing a staged growth model for photovoltaic greenhouse crops driven by a data-mechanism combination. Based on the actual environmental factor requirements of crops at different stages, as well as the constraints of leaf area index and dry matter accumulation, a photovoltaic greenhouse flexible regulation potential assessment model is established with the goal of maximizing the power regulation value at each moment.

[0059] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the flexible regulation potential of photovoltaic greenhouses based on data mechanism joint driving, characterized in that, The method includes the following steps: Step 1: Based on the photovoltaic greenhouse architecture, construct a photovoltaic greenhouse microgrid model; Step 2: Use a Gaussian mixture model to characterize the impact of the correlation and uncertainty of outdoor environmental factors on the demand for environmental factors inside the photovoltaic greenhouse. Combine the dynamic coupling characteristics of the demand of photovoltaic greenhouse crops for environmental factors inside the photovoltaic greenhouse to establish a data-mechanism joint-driven energy demand model for the entire growth cycle of photovoltaic greenhouse crops. The entire growth cycle of crops includes the seedling stage and the vigorous growth stage. Step 3: At different growth stages of the crop, based on the actual environmental factor requirements of the crop, as well as LAI and DMA constraints, an optimized scheduling model is established with the goal of maximizing the power regulation potential of the photovoltaic greenhouse at each time point, so as to obtain the maximum flexible regulation potential of the photovoltaic greenhouse at different times.

2. The method as described in claim 1, characterized in that, In step 1, the photovoltaic greenhouse structure includes: electric irrigation and drainage equipment, spatial electric field equipment, physical pest control device, supplemental lighting, electric heating equipment, electric refrigeration equipment, and carbon dioxide fertilizer generator.

3. The method as described in claim 2, characterized in that, Power requirements of electric irrigation and drainage equipment for: in, The density of water; It is the acceleration due to gravity; Water flow rate; For the water pump head; For irrigated area; For irrigation efficiency; This refers to the daily irrigation time; The actual water demand is determined by the following formula: in, For crop coefficients; For reference evaporation, it is determined by the following formula: in, The slope of the saturated vapor pressure-temperature curve; Net radiation; Soil heat flux; This is the hygrometer constant; Average temperature; The wind speed at a height of 2m; and These are the saturated vapor pressure and the actual vapor pressure, respectively.

4. The method as described in claim 2, characterized in that, The total power consumption of the space electric field equipment and the total power consumption of the physical insecticidal device are the product of their rated power and the ratio of the number of devices currently in operation.

5. The method as described in claim 2, characterized in that, In step 1, the irradiance of the supplemental light With electricity demand The relationship is: in, Photoelectric conversion efficiency; The effective area for uniform illumination by the supplementary lighting.

6. The method as described in claim 2, characterized in that, In step 1, the heating and cooling power of the electric heating and cooling equipment at time t. , They are respectively: in, , These are the power consumption of the electric heating equipment and the electric chiller at time t, respectively. , These refer to the electrothermal and electrocooling conversion efficiencies of electric heating equipment and electric refrigeration machines, respectively.

7. The method as described in claim 2, characterized in that, In step 1, the power consumption of the carbon dioxide fertilizer generator for: in, Let t be the electrical power consumption of the carbon dioxide fertilizer generator at time t; The amount of carbon dioxide replenished to the carbon dioxide fertilizer generator at time t; Energy conversion efficiency; This refers to the volume of the greenhouse.

8. The method as described in claim 1, characterized in that, In step 2, the probability density function of the single uncertain factor is: Where z is a random variable ; It is the m-th one-dimensional normal distribution; The weight of the m-th Gaussian component; and are the mean and variance of the m-th Gaussian component, respectively; The joint probability density function of temperature, light intensity, and CO2 concentration is obtained based on the marginal probability invariance theorem: in, , , denoted as the variances of temperature, light intensity, and CO2 concentration in the m-th Gaussian distribution, respectively. , , , respectively, represent the covariances of temperature and light intensity, temperature and CO2 concentration, and light intensity and CO2 concentration in the m-th Gaussian distribution; d is the dimension of the random variable matrix.

9. The method as described in claim 8, characterized in that, In step 2, the seedling growth model is specifically as follows: LAI is a growth indicator for crop seedlings. , The highest and lowest temperatures that greenhouse crops can tolerate during their seedling stage; , These are the upper and lower limits of the optimal growth temperature for greenhouse crops during the seedling stage; Let be the indoor temperature at time t; , These represent the energy percentage of the PAR band in solar radiation and the light radiation output from supplemental lighting, respectively. The light transmittance of the greenhouse; Let be the solar irradiance at time t; The expression for the temperature inside the greenhouse is: in, , , , These represent the heat power of solar irradiance at time t, the heat transfer between the air inside the greenhouse and the covering layer, the heat transfer through ventilation, and the heat power exchange with the outside due to long-wave radiation. , These are the density and specific heat capacity of air, respectively. The photothermal conversion efficiency of solar radiation; The effective area exposed to sunlight; The heat transfer coefficient of the greenhouse microgrid; The heat transfer area of ​​the greenhouse; Let be the outdoor temperature at time t; Ventilation volume; Ventilation area; For long-wavelength emissivity; It is the Stefan-Boltzmann constant; The area of ​​the photovoltaic greenhouse is its light-transmitting area; The vigorous growth model is as follows: Among them, DMA is the growth index at maturity; , These represent the organic matter accumulated by crop photosynthesis and the organic matter consumed by respiration at time t, respectively. The photosynthetic rate and respiration rate of crops are respectively: in, The baseline respiration rate of crops at the reference temperature; For reference temperature; Temperature coefficient; The functional relationship between temperature and crop photosynthetic rate based on the Arrhenius equation It can be represented as: in, Pre-exponential factors; It is the activation energy; It is the gas constant; Characterized by the Michaelis equation Functional relationship between concentration and crop photosynthetic rate It can be represented as: in, This represents the maximum photosynthetic rate of the crop. Let be the CO2 concentration at time t within the greenhouse microgrid; It is the CO2 half-saturation constant; A non-rectangular hyperbolic model was used to characterize the functional relationship between light intensity and crop photosynthetic rate. It can be represented as: in, For apparent quantum efficiency; For the convexity of the curve; The expression for the CO2 concentration inside the greenhouse is: in, The amount of CO2 absorbed by crops; CO2 lost through greenhouse ventilation; Let be the net radiative power of the crop leaf area index at time t; Let t be the outdoor CO2 concentration.

10. The method as described in claim 1, characterized in that, Step 3 specifically involves: in, , These represent the potential for power increase and decrease in the greenhouse microgrid at time t, respectively. , functions respectively or The maximum and minimum values; For function or The actual value.

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