Plant growth greenhouse electric heating load operation optimization method, device, equipment and medium

Abstract

The invention belongs to the technical field of system optimization, and particularly relates to a plant growth greenhouse electric heating load operation optimization method and device, equipment and a medium. The method comprises the steps that according to an effective accumulated temperature range, a pre-built effective accumulated temperature model is adopted to calculate a design temperature range in a heating room; according to the design temperature range in the heating room, calculating the heat loss range of the greenhouse by adopting a pre-constructed greenhouse heat load model; according to the heat loss range, the adjustment potential of heat load equipment is determined; constructing an electric heating load optimization operation model; wherein the electric heating load optimization operation model comprises an objective function with the lowest sum of carbon transaction cost and energy consumption cost, and corresponding constraint conditions; and solving the objective function based on the thermal load equipment adjustment potential and the constraint condition to obtain an operation optimization scheme. According to the method, the economic optimization of the greenhouse electric heating load is realized by constructing the electric heating load optimization operation model and comprehensively considering the carbon transaction cost and the energy consumption cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of system optimization, and in particular relates to a method, device, equipment and medium for optimizing the operation of electric heating loads in a plant growth greenhouse. Background Art

[0002] In the agricultural field, many intelligent and automated agricultural management systems have emerged in recent years. These systems mainly use sensor technology, data analysis technology, drone technology, etc. to monitor and analyze various parameters such as soil, crops, and weather in real time to achieve intelligent management and control of agricultural planting processes such as irrigation, fertilization, and pest control. Agricultural production is highly sensitive to cost prices. The above-mentioned agricultural management systems focus on maximizing crop yields and often ignore the optimization of energy consumption. Especially in greenhouses and indoor farms, constant temperature and light control will result in high electricity costs. In addition, this method does not take into account the specific growth stages and physiological responses of plants, which affects crop quality and growth efficiency. Summary of the invention

[0003] The object of the present invention is to provide a method, device, equipment and medium for optimizing the operation of electric heating load in a plant growth greenhouse, so as to solve the economic problems existing in the prior art.

[0004] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the operation of electric heating load in a plant growth greenhouse, comprising the following steps: Determine an effective accumulated temperature range that meets the plant growth requirements; and calculate a heating indoor design temperature range using a pre-constructed effective accumulated temperature model based on the effective accumulated temperature range; According to the heating indoor design temperature range, a pre-built greenhouse heat load model is used to calculate the heat loss range of the greenhouse; according to the heat loss range, the heat load equipment regulation potential is determined; Constructing an electric heat load optimization operation model; wherein the electric heat load optimization operation model includes an objective function of minimizing the sum of carbon trading cost and energy cost, and corresponding constraints; The objective function is solved based on the heat load equipment regulation potential and constraint conditions to obtain an operation optimization plan.

[0005] Furthermore, according to the effective accumulated temperature range, a pre-built effective accumulated temperature model is used to calculate the design temperature range of the heating room, including:

[0006] In the formula, , and They are the optimum temperature, minimum temperature and maximum temperature for crop growth. ; is the accumulated temperature of crops, is the crop growth rate; Design temperature for heating room, .

[0007] Furthermore, according to the heating indoor design temperature range, a pre-built greenhouse heat load model is used to calculate the heat loss range of the greenhouse, including:

[0008] In the formula, The heat loss of the building envelope above the ground. ; The heat loss from indoor floor heat transfer in the greenhouse. ; Heat loss due to indoor and outdoor air exchange, ; is the greenhouse structure correction factor; is the additional correction factor for wind force; is the heat transfer coefficient of different enclosure structures on or above the ground, ; is the ground or enclosure area, ; Design temperature for heating room, ; Calculate the outdoor temperature for heating, °C; For the The ground heat transfer coefficient of the calculation point in the area, ; For the The area of ​​the area calculation point, ; The number of areas divided into solar greenhouses; is the wind factor, is the number of greenhouse ventilation times per unit time; is the volume of air in the greenhouse, .

[0009] Furthermore, the electric heat load optimization operation model includes an objective function that minimizes the sum of carbon trading cost and energy cost, including:

[0010] In the formula, is the carbon trading cost of the park; The cost of purchasing electricity from the power grid, yuan; The income from the surplus electricity grid connection, RMB; , Other Representatives The load power consumption during the period and the output power of the photovoltaic modules, ; is the on-grid electricity price during this period, ; For energy storage batteries The charging power of the time period, ; is the maximum charging power of energy storage, ; For a period, take 0.5 ; For energy storage batteries The discharge power of the time period, ; is the grid electricity price during this period, ; is the maximum output power of energy storage, .

[0011] Furthermore, the constraints include: equipment output constraints, growth environment constraints, and power balance constraints.

[0012] Furthermore, the constraints include: Equipment output constraints

[0013] In the formula, and are the minimum and maximum values ​​of photovoltaic output respectively; Growth environment constraints

[0014] in, and Respectively The minimum and maximum total operating time of each load type within a day; For the The start time of the load operation; For the The load operation stop time; Power balance constraints

[0015] In the formula, The input power to the grid is .

[0016] In a second aspect, the present invention provides a plant growth greenhouse electric heat load operation optimization device, comprising: The first calculation module is used to determine the effective accumulated temperature range that meets the plant growth requirements; according to the effective accumulated temperature range, a pre-constructed effective accumulated temperature model is used to calculate the heating indoor design temperature range; The second calculation module is used to calculate the heat loss range of the greenhouse according to the design temperature range of the heating room using a pre-built greenhouse heat load model; and determine the heat load equipment adjustment potential according to the heat loss range; A model optimization module, used to construct an electric heat load optimization operation model; wherein the electric heat load optimization operation model includes an objective function of minimizing the sum of carbon trading cost and energy cost, and corresponding constraints; The optimization solution module is used to solve the objective function based on the heat load equipment regulation potential and constraint conditions to obtain an operation optimization plan.

[0017] Furthermore, the electric heat load optimization operation model includes an objective function that minimizes the sum of carbon trading cost and energy cost, including:

[0018] In the formula, is the carbon trading cost of the park; The cost of purchasing electricity from the power grid, yuan; The income from the surplus electricity grid connection, RMB; , Other Representatives The load power consumption during the period and the output power of the photovoltaic modules, ; is the on-grid electricity price during this period, ; For energy storage batteries The charging power of the time period, ; is the maximum charging power of energy storage, ; For a period, take 0.5 ; For energy storage batteries The discharge power of the time period, ; is the grid electricity price during this period, ; is the maximum output power of energy storage, .

[0019] In a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the above-mentioned method for optimizing the operation of electric heating loads in a plant growth greenhouse.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the above-mentioned method for optimizing the operation of electric heating loads in a plant growth greenhouse is implemented.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention achieves economic optimization of greenhouse electric heating load by constructing an electric heating load optimization operation model and comprehensively considering carbon trading costs and energy costs, thereby reducing energy consumption and electricity costs in greenhouse operations and improving the economic benefits of agricultural production. According to the growth requirements of plants and the effective accumulated temperature range, the indoor design temperature range of the heating system and the greenhouse heat loss range are accurately calculated to ensure that the maximum utilization of energy is achieved while meeting the growth requirements of plants.

[0022] The method of the present invention is not only applicable to greenhouse environments, but can also be adjusted and optimized according to the growth characteristics and environmental requirements of specific crops. It has wide applicability and flexibility and can meet the needs of different agricultural production scenarios.

[0023] By optimizing the operation of electric and thermal loads, greenhouse gas emissions can be reduced and the carbon footprint of agricultural production can be lowered, which is of great significance for promoting sustainable agricultural development and addressing climate change.

[0024] In summary, the present invention provides a method, device, equipment and medium for optimizing the operation of electric heating load in a plant growth greenhouse. The method comprehensively considers factors such as plant growth requirements, energy consumption and economy, realizes precise control and optimized operation of greenhouse electric heating load, and provides a more efficient, environmentally friendly and intelligent solution for agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a flow chart of a method for optimizing the operation of electric heating loads in a plant growth greenhouse according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a plant growth greenhouse electric heat load operation optimization device according to an embodiment of the present invention; Figure 3 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0027] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0028] Example 1 In order to solve the balance problem between electric heat energy consumption and crop growth condition regulation in the existing agricultural management system. The present invention provides a method for optimizing the operation of electric heat load in a plant growth greenhouse, which determines different temperatures of the greenhouse according to different accumulated temperature requirements and changes the constant temperature of the greenhouse. For example, the greenhouse temperature is regulated when the plant daily accumulated temperature GDD is greater than the lower limit of the crop daily accumulated temperature, so as to ensure that the greenhouse temperature is at the most suitable temperature for crop growth.

[0029] like Figure 1 As shown, a method for optimizing the operation of electric heating load in a plant growth greenhouse comprises the following steps: S1. Determine an effective accumulated temperature range that meets the growth requirements of plants; and calculate a heating indoor design temperature range using a pre-constructed effective accumulated temperature model based on the effective accumulated temperature range.

[0030] It should be noted that the effective accumulated temperature is the sum of the effective temperatures of crops during a certain growth period, that is, the sum of the difference between the daily average temperature of crops and the biological zero degree in a certain period of time. The effective accumulated temperature reflects the demand for heat for crop growth and development, and is an important indicator for measuring regional heat resources. It eliminates the temperature below the biological lower limit temperature and above the upper limit temperature, so it is relatively stable and can more accurately reflect the crop's demand for heat. The unit of effective accumulated temperature is generally expressed in degrees Celsius (℃), which is used to represent the sum of the daily average temperature over a period of time. Therefore, in this plan, the temperature is regulated, and there is no need to ensure that the greenhouse is at normal temperature. It is enough to ensure that the plants can grow normally through accumulated temperature.

[0031] Specifically, according to the effective accumulated temperature range, a pre-built effective accumulated temperature model is used to calculate the indoor design temperature range of the heating system, including:

[0032] In the formula, , and They are the optimum temperature, minimum temperature and maximum temperature for crop growth. ; is the accumulated temperature of crops, is the crop growth rate; Design temperature for heating room, .

[0033] S2. Calculate the heat loss range of the greenhouse using a pre-built greenhouse heat load model according to the indoor design temperature range of the heating system; and determine the heat load equipment regulation potential according to the heat loss range.

[0034] Specifically, according to the indoor design temperature range of the heating room, a pre-built greenhouse heat load model is used to calculate the heat loss range of the greenhouse, including:

[0035] In the formula, The heat loss of the building envelope above the ground. ; The heat loss from indoor floor heat transfer in the greenhouse. ; It is the heat loss of indoor and outdoor air exchange, that is, the cold air infiltration heat load. . is the greenhouse structure correction factor, which is taken as 1.02; is the additional correction factor for wind force, which is taken as 1.04. is the heat transfer coefficient of different enclosure structures on or above the ground, ; is the ground or enclosure area, ; Design temperature for heating room, ; Calculate the outdoor temperature for heating, °C; For the The ground heat transfer coefficient of the calculation point in the area, ; For the The area of ​​the area calculation point, ; is the number of zones in the solar greenhouse. The heat transfer coefficients of each zone are shown in Table 1. is the wind factor, which is taken as 1.0; is the number of greenhouse ventilation times per unit time, which is taken as 1.2; is the volume of air in the greenhouse, .

[0036] It should be noted that the above greenhouse heat load model is based on the basic principles of thermodynamics and heat transfer, and ignores the heat generated by motors, supplementary lighting, lighting equipment, crop soil respiration, and solar radiation, including heat loss from heat transfer of the greenhouse enclosure structure, heat loss from heat transfer of the indoor ground, and heat loss from air exchange between indoor and outdoor areas of the greenhouse.

[0037] Table 1 Ground heat transfer coefficient

[0038] S3. Construct an electric heat load optimization operation model; wherein the electric heat load optimization operation model includes an objective function of minimizing the sum of carbon trading cost and energy cost, and corresponding constraints.

[0039] Taking into account the carbon trading costs, electricity and energy purchasing costs, operation and maintenance costs of energy supply and consumption equipment, and operation and maintenance costs of energy storage equipment of the greenhouse energy system, an optimal operation model of electric and thermal loads in agricultural parks is established with the minimum sum of carbon trading costs and energy consumption costs as the optimization objective function, taking into account equipment output constraints, growth environment constraints, and power balance constraints.

[0040] Specifically, the electric heat load optimization operation model includes an objective function that minimizes the sum of carbon trading cost and energy cost, including:

[0041] In the formula, is the carbon trading cost of the park; The cost of purchasing electricity from the power grid, yuan; The income from the surplus electricity grid connection, RMB; , Other Representatives The load power consumption during the period and the output power of the photovoltaic modules, ; is the on-grid electricity price during this period, ; For energy storage batteries The charging power of the time period, ; is the maximum charging power of energy storage, ; For a period, take 0.5 . For energy storage batteries The discharge power of the time period, ; is the grid electricity price during this period, ; is the maximum output power of energy storage, .

[0042] Constraints include: equipment output constraints, growth environment constraints, and power balance constraints.

[0043] More specifically, the constraints include: Equipment output constraint, photovoltaic output constraint expression is:

[0044] In the formula, and are the minimum and maximum values ​​of photovoltaic output respectively; Growth environment constraints: In order to ensure greenhouse temperature, humidity, carbon dioxide concentration and other indicators, the load operating time and total operating time are constrained according to the load characteristics to meet the normal growth needs of plants.

[0045]

[0046] in, and Respectively The minimum and maximum total operating time of each load type within a day; For the The start time of the load operation; For the The load operation stop time; Power balance constraints

[0047] In the formula, The input power to the grid is .

[0048] S4. Solve the objective function based on the heat load equipment regulation potential and constraint conditions to obtain an operation optimization plan.

[0049] In the process of solving the electric and thermal load optimization operation model, the typical daily load distribution characteristics are used as one of the important inputs of the model to simulate and predict the power demand of the park in different time periods, so as to formulate a more accurate electric and thermal load optimization operation strategy. The solution results, such as energy purchase costs, carbon emissions, and energy supply equipment output, are all simulated and predicted based on the typical daily load distribution characteristics.

[0050] The acquisition of typical daily load distribution characteristics specifically includes the following steps: Select appropriate sensors or data acquisition devices and install them on typical power-consuming equipment in the park to collect power consumption data in real time or at a scheduled time.

[0051] After data collection, data cleaning and preprocessing are performed to ensure data quality and accuracy: identify and process abnormal values ​​that may be generated by sensors, such as sudden peaks or sudden drops; interpolate missing data or adopt appropriate filling strategies to reduce data incompleteness; perform noise filtering and use filtering technology to eliminate noise caused by sensor errors or environmental interference to ensure data smoothness.

[0052] To ensure the accuracy of subsequent analysis, time synchronization and data standardization are performed: the collected data is time synchronized to unify the timestamp format to facilitate comparison and collaborative analysis between different devices; the power data is converted into standard energy consumption units, such as kilowatt-hours (kWh), to ensure data consistency and comparability.

[0053] Use statistics and machine learning techniques to extract and analyze load characteristics: calculate daily average load, peak load, valley load and other indicators to obtain the characteristics of typical daily load distribution: use cluster analysis, time series analysis and other methods to identify and extract load patterns in different time periods to form a classification of park load characteristics.

[0054] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a plant growth greenhouse electric heat load operation optimization device, comprising: The first calculation module is used to determine the effective accumulated temperature range that meets the plant growth requirements; according to the effective accumulated temperature range, a pre-constructed effective accumulated temperature model is used to calculate the heating indoor design temperature range; The second calculation module is used to calculate the heat loss range of the greenhouse according to the design temperature range of the heating room using a pre-built greenhouse heat load model; and determine the heat load equipment adjustment potential according to the heat loss range; A model optimization module, used to construct an electric heat load optimization operation model; wherein the electric heat load optimization operation model includes an objective function of minimizing the sum of carbon trading cost and energy cost, and corresponding constraints; The optimization solution module is used to solve the objective function based on the heat load equipment regulation potential and constraint conditions to obtain an operation optimization plan.

[0055] Furthermore, the electric heat load optimization operation model includes an objective function that minimizes the sum of carbon trading cost and energy cost, including:

[0056] In the formula, is the carbon trading cost of the park; The cost of purchasing electricity from the power grid, yuan; The income from the surplus electricity grid connection, RMB; , Other Representatives The load power consumption during the period and the output power of the photovoltaic modules, ; is the on-grid electricity price during this period, ; For energy storage batteries The charging power of the time period, ; is the maximum charging power of energy storage, ; For a period, take 0.5 ; For energy storage batteries The discharge power of the time period, ; is the grid electricity price during this period, ; is the maximum output power of energy storage, .

[0057] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for realizing a method for optimizing operation of electric and thermal loads in a plant growth greenhouse; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0058] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a method for optimizing the operation of electric heating loads in a plant growth greenhouse in Example 1 by running or executing the computer program stored in the memory 101 and calling data stored in the memory 101.

[0059] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0060] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0061] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for optimizing the operation of electric and thermal loads in a plant growth greenhouse, and the processor 102 can execute the plurality of instructions to implement: Determine an effective accumulated temperature range that meets the plant growth requirements; and calculate a heating indoor design temperature range using a pre-constructed effective accumulated temperature model based on the effective accumulated temperature range; According to the heating indoor design temperature range, a pre-built greenhouse heat load model is used to calculate the heat loss range of the greenhouse; according to the heat loss range, the heat load equipment regulation potential is determined; Constructing an electric heat load optimization operation model; wherein the electric heat load optimization operation model includes an objective function of minimizing the sum of carbon trading cost and energy cost, and corresponding constraints; The objective function is solved based on the heat load equipment regulation potential and constraint conditions to obtain an operation optimization plan.

[0062] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0063] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of electric heating load in a plant growth greenhouse, characterized in that: The steps include: Determine an effective accumulated temperature range that meets the plant growth requirements; and calculate a heating indoor design temperature range using a pre-constructed effective accumulated temperature model based on the effective accumulated temperature range; According to the heating indoor design temperature range, a pre-built greenhouse heat load model is used to calculate the heat loss range of the greenhouse; according to the heat loss range, the heat load equipment regulation potential is determined; Constructing an electric heat load optimization operation model; wherein the electric heat load optimization operation model includes an objective function of minimizing the sum of carbon trading cost and energy cost, and corresponding constraints; The objective function is solved based on the heat load equipment regulation potential and constraint conditions to obtain an operation optimization plan.

2. The method for optimizing the operation of electric heat load in a plant growth greenhouse according to claim 1, characterized in that: According to the effective accumulated temperature range, the pre-built effective accumulated temperature model is used to calculate the indoor design temperature range of the heating system, including: In the formula, , and They are the optimum temperature, minimum temperature and maximum temperature for crop growth. ; is the accumulated temperature of crops, is the crop growth rate; Design temperature for heating room, .

3. The method for optimizing the operation of electric heat load in a plant growth greenhouse according to claim 1, characterized in that: According to the heating indoor design temperature range, the heat loss range of the greenhouse is calculated using the pre-built greenhouse heat load model, including: In the formula, The heat loss of the building envelope above the ground. ; The heat loss from indoor floor heat transfer in the greenhouse. ; Heat loss due to indoor and outdoor air exchange, ; is the greenhouse structure correction factor; is the additional correction factor for wind force; is the heat transfer coefficient of different enclosure structures on or above the ground, ; is the ground or enclosure area, ; Design temperature for heating room, ; Calculate the outdoor temperature for heating, °C; For the The ground heat transfer coefficient of the calculation point in the area, ; For the The area of ​​the area calculation point, ; The number of areas divided into solar greenhouses; is the wind factor; is the number of greenhouse ventilation times per unit time; is the volume of air in the greenhouse, .

4. The method for optimizing the operation of electric heating load in a plant growth greenhouse according to claim 1, characterized in that: The electric heat load optimization operation model includes an objective function that minimizes the sum of carbon trading cost and energy cost, including: In the formula, is the carbon trading cost of the park; The cost of purchasing electricity from the power grid, yuan; The income from the surplus electricity grid connection, RMB; , Other Representatives The load power consumption during the period and the output power of the photovoltaic modules, ; is the on-grid electricity price during this period, ; For energy storage batteries The charging power of the time period, ; is the maximum charging power of energy storage, ; For a period, take 0.5 ; For energy storage batteries The discharge power of the time period, ; is the grid electricity price during this period, ; is the maximum output power of energy storage, .

5. The method for optimizing the operation of electric heating load in a plant growth greenhouse according to claim 4, characterized in that: Constraints include: equipment output constraints, growth environment constraints, and power balance constraints.

6. The method for optimizing the operation of electric heat load in a plant growth greenhouse according to claim 5, characterized in that: The constraints include: Equipment output constraints In the formula, and are the minimum and maximum values ​​of photovoltaic output respectively; Growth environment constraints in, and Respectively The minimum and maximum total operating time of each load type within a day; For the The start time of the load operation; For the The load operation stop time; Power balance constraints In the formula, The input power to the grid is .

7. A plant growth greenhouse electric heat load operation optimization device, characterized in that: include: The first calculation module is used to determine the effective accumulated temperature range that meets the plant growth requirements; According to the effective accumulated temperature range, a pre-built effective accumulated temperature model is used to calculate the design temperature range of the heating room; The second calculation module is used to calculate the heat loss range of the greenhouse according to the design temperature range of the heating room using a pre-built greenhouse heat load model; and determine the heat load equipment adjustment potential according to the heat loss range; A model optimization module, used to construct an electric heat load optimization operation model; wherein the electric heat load optimization operation model includes an objective function of minimizing the sum of carbon trading cost and energy cost, and corresponding constraints; The optimization solution module is used to solve the objective function based on the heat load equipment regulation potential and constraint conditions to obtain an operation optimization plan.

8. The plant growth greenhouse electric heat load operation optimization device according to claim 7, characterized in that: The electric heat load optimization operation model includes an objective function that minimizes the sum of carbon trading cost and energy cost, including: In the formula, is the carbon trading cost of the park; The cost of purchasing electricity from the power grid, yuan; The income from the surplus electricity grid connection, RMB; , Other Representatives The load power consumption during the period and the output power of the photovoltaic modules, ; is the on-grid electricity price during this period, ; For energy storage batteries The charging power of the time period, ; is the maximum charging power of energy storage, ; For a period, take 0.5 ; For energy storage batteries The discharge power of the time period, ; is the grid electricity price during this period, ; is the maximum output power of energy storage, .

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the method for optimizing the operation of electric heating loads in a plant growth greenhouse as claimed in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the method for optimizing the operation of electric heating loads in a plant growth greenhouse according to any one of claims 1 to 6 is implemented.

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