Light storage and charging integrated equipment applied to agricultural scene and control method thereof

Abstract

The invention relates to the technical field of photoelectricity, and provides light storage and charging integrated equipment applied to an agricultural scene and a control method of the light storage and charging integrated equipment. The energy storage system module is used for storing the electricity generated by the photovoltaic panel assembly and supplying power to agricultural machinery facilities; the electric equipment charging module is used for charging electric equipment; the power feedback power grid module is used for photovoltaic power generation grid-connected connection; the intelligent operation control module is used for judging whether the energy storage system module stores the electricity generated by the photovoltaic panel assembly based on the generating capacity of the photovoltaic assembly and the electricity consumption of the agricultural facility, and controlling the energy storage system module; based on the generating capacity of the photovoltaic module, the electric quantity required by energy storage, the power consumption of agricultural facilities and the charging quantity of electric equipment, the electric quantity purchased or sold from the power grid is determined, and the power is controlled to be fed back to the power grid module. The problems of insufficient power supply, low energy utilization efficiency, complex equipment maintenance and the like in agricultural production can be effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of optoelectronic technology, and particularly to an integrated photovoltaic energy storage charging equipment applied to agricultural scenarios and its control method. Background Art

[0002] The statements in this part only provide the background art related to the present invention and do not necessarily constitute the prior art.

[0003] With the development of modern agriculture, the demand for electricity in farmland production is increasing day by day. However, due to the imperfect power infrastructure in many agricultural production areas, the power supply is insufficient, which in turn affects the normal operation of agricultural machinery and equipment. Traditional power supply methods often rely on the power grid, and in remote areas or areas with insufficient power grid coverage, the instability of power supply has caused great troubles to agricultural production. In addition, some key links in the agricultural production process, such as irrigation, fertilization, and pesticide spraying, require continuous power support, but the existing power grid power supply mode is difficult to meet these needs.

[0004] In this context, in recent years, the application of photovoltaic power generation technology and energy storage systems in agriculture has gradually attracted attention. Photovoltaic power generation can utilize solar energy resources to provide green electricity for agricultural production, while the energy storage system can provide stable power supply during peak power demand periods to balance the power load. However, the existing photovoltaic power generation and energy storage systems are mainly used for power production and storage, and have not yet formed a complete system to meet the power demand of different links in agricultural production. Summary of the Invention

[0005] In order to solve the deficiencies of the prior art, the present invention provides an integrated photovoltaic energy storage charging equipment applied to agricultural scenarios and its control method. By integrating photovoltaic power generation, energy storage systems, charging equipment, and an intelligent control system, and optimizing the utilization of power resources through intelligent scheduling, it can calmly cope with seasonal changes and diverse demands, and constructs a multi-functional, intelligent, efficient, and green agricultural machinery power guarantee system, which can effectively solve problems such as insufficient power supply, low energy utilization efficiency, and complex equipment maintenance in agricultural production, realize the independent power supply and optimized management of agricultural production, and thus improve the efficiency and sustainability of agricultural production.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present invention provides an integrated photovoltaic energy storage charging equipment applied to agricultural scenarios.

[0008] An integrated photovoltaic energy storage charging equipment applied to agricultural scenarios includes a photovoltaic panel assembly, an energy storage system module, a power feedback power grid module, and an intelligent operation control module;

[0009] The photovoltaic panel assembly is disposed on the top of the photovoltaic greenhouse and is used to convert solar energy into electrical energy and supply power to agricultural machinery facilities;

[0010] The energy storage system module is connected to the photovoltaic panel assembly through a bi - directional inverter, and is used to store the electricity generated by the photovoltaic panel assembly and supply power to agricultural machinery facilities;

[0011] The electric equipment charging module is connected to the energy storage system module and is used to charge electric equipment;

[0012] The power feedback to the grid module is disposed between the bi - directional inverter and the grid and is used for grid - connected connection of photovoltaic power generation;

[0013] The intelligent operation control module is used to judge whether the energy storage system module stores the electricity generated by the photovoltaic panel assembly based on the power generation amount of the photovoltaic module and the power consumption amount of agricultural facilities, and control the energy storage system module; based on the power generation amount of the photovoltaic module, the electricity required for energy storage, the power consumption amount of agricultural facilities, and the charging amount of electric equipment, the fourth - order Runge - Kutta method is used to determine the electricity purchased from or sold to the grid, and control the power feedback to the grid module.

[0014] Further, the photovoltaic panel assembly is connected with a DC circuit breaker, and the formula for calculating the breaking current of the DC circuit breaker is as follows:

[0015] I = 2×U / (π×f×L0×Bm)

[0016] Where, I is the breaking current, U is the breaking voltage, f is the power supply frequency, L0 is the wire length, and Bm is the magnetic induction intensity; when a current greater than I surges in, the DC circuit breaker directly disconnects the circuit to protect agricultural machinery facilities.

[0017] Further, the energy storage system module includes a lithium - ion battery pack and a plug - in inverter, and the plug - in inverter is used to convert the lithium - ion battery pack and then supply power to agricultural machinery facilities.

[0018] Further, the energy storage system module further includes a battery management system and a temperature control system;

[0019] The temperature control system includes a plurality of thermosensitive sensors, which are used to monitor the temperature data of the battery pack in real time and transmit it to the battery management system;

[0020] The battery management system adjusts the coolant flow rate, fan speed or turns on the heating device in grades according to the change of the temperature data of the battery pack.

[0021] Further, the energy storage plan of the energy storage system module is predicted by using a multiple linear regression algorithm:

[0022] y = β0 + β1x1 + β2x2 + β3x3 + ε

[0023] Among them, x1 is the electricity stored in the energy storage system module, x2 is the power generation of the photovoltaic module, x3 is the power consumption of the agricultural facilities, y is the predicted remaining energy storage time, ε is the error term, and β i is the optimal parameter.

[0024] Furthermore, the determination of the electricity quantity purchased or sold by the power grid aims to maximize the profit:

[0025]

[0026] Among them, u1(t) is the electricity quantity purchased from the power grid, u2(t) is the electricity quantity sold to the power grid, p sell (t) is the electricity selling price, and p uy (t) is the electricity purchasing price.

[0027] Furthermore, both the electricity selling price and the electricity purchasing price are obtained by analyzing the electricity price time series prediction using a long short-term memory network.

[0028] Furthermore, the determination of the electricity quantity purchased or sold by the power grid adopts the following constraint conditions:

[0029] Power balance: If the power generation is not enough to meet the charging demand, electricity needs to be purchased from the power grid: u1(t) = D(t) - (G(t) + S(t)) if G(t) + S(t) < D(t);

[0030] Electricity selling: If the power generation is sufficient, the excess electricity can be sold: u2(t) = G(t) + S(t) - D(t) if

[0031] G(t) + S(t) ≥ D(t);

[0032] Equipment operation monitoring: If T(t) > 40 and I(t) > 8, charging is prohibited;

[0033] Non-negativity constraint: All variables must be non-negative: u1(t) ≥ 0, u2(t) ≥ 0;

[0034] Among them, u1(t) is the electricity quantity purchased from the power grid, u2(t) is the electricity quantity sold to the power grid, G(t) is the power generation of the photovoltaic module, S(t) is the electricity quantity required for energy storage, D(t) is the sum of the power consumption of the agricultural facilities and the charging amount of the electric equipment, and R(t) is the electricity quantity for buying and selling.

[0035] Furthermore, the method for determining whether the energy storage system module stores the electricity generated by the photovoltaic panel module is as follows: Obtain the historical power generation data of the photovoltaic module and predict the power generation of the photovoltaic module on a certain day; obtain the historical power consumption data of the agricultural facilities and predict the power consumption of the agricultural facilities on that day; if the power generation of the photovoltaic module on that day exceeds the power consumption of the agricultural facilities on that day, the energy storage system module stores the electricity generated by the photovoltaic panel module.

[0036] The second aspect of the present invention provides a control method for a photovoltaic energy storage charging integrated equipment applied to an agricultural scenario.

[0037] A control method for a photovoltaic energy storage charging integrated equipment applied to an agricultural scenario as described in the first aspect includes the following steps:

[0038] Based on the power generation of the photovoltaic modules and the power consumption of the agricultural facilities, determine whether the energy storage system module stores the electricity generated by the photovoltaic panel modules, and control the energy storage system module;

[0039] Based on the power generation of the photovoltaic modules, the electricity required for energy storage, the power consumption of the agricultural facilities, and the charging amount of the electric equipment, use the fourth-order Runge-Kutta method to determine the electricity purchased from or sold to the power grid, and control the power feedback to the power grid module.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] The photovoltaic energy storage charging integrated equipment applied to an agricultural scenario described in the present invention integrates photovoltaic power generation, an energy storage system, charging equipment, and an intelligent control system, and optimizes the utilization of power resources through intelligent scheduling. It can calmly cope with seasonal changes and diverse demands, and constructs a multi-functional, intelligent, efficient, and green agricultural machinery power supply system. It can effectively solve problems such as insufficient power supply, low energy utilization efficiency, and complex equipment maintenance in agricultural production, realize the independent power supply and optimized management of agricultural production, and thus improve the efficiency and sustainability of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings forming a part of this invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0043] Figure 1 It is a structural diagram of a photovoltaic energy storage charging integrated equipment in an agricultural scenario provided by the present invention;

[0044] Figure 2 It is a structural diagram of the photovoltaic power generation module provided by the present invention;

[0045] Figure 3 It is a structural diagram of the energy storage system module provided by the present invention;

[0046] Figure 4 It is a structural diagram of the electric equipment charging module provided by the present invention;

[0047] Figure 5 It is a flowchart of the intelligent linkage system in the electric equipment charging module;

[0048] Figure 6Structural diagram of the power feedback grid module provided by the present invention;

[0049] Figure 7 Regulation principle diagram of the intelligent operation control system of the integrated photovoltaic energy storage charging equipment provided by the present invention.

[0050] Figure 8 Schematic diagram of the microgrid interaction under the actual application scenario of the photovoltaic energy storage charging and discharging of agricultural electric equipment provided by the present invention. Detailed implementation manners

[0051] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0052] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further descriptions of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0053] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0054] In the present invention, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only relationship terms determined for the convenience of describing the structural relationship of each component or element of the present invention and do not specifically refer to any component or element of the present invention and should not be construed as a limitation to the present invention.

[0055] In the present invention, terms such as "fixed connection", "connected", "connected" should be understood in a broad sense, indicating that it can be a fixed connection, an integral connection or a detachable connection; it can be directly connected or indirectly connected through an intermediate medium. For those related scientific research or technical personnel in the field, the specific meanings of the above terms in the present invention can be determined according to specific circumstances and should not be construed as a limitation to the present invention.

[0056] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0057] Embodiment 1

[0058] Embodiment 1 of the present invention provides an integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios, which integrates functions such as solar photovoltaic power generation, energy storage systems, charging of electric equipment, and intelligent operation control. The aim is to achieve autonomous power supply and efficient management of agricultural machinery and equipment in agricultural production through the precise and effective utilization of renewable energy, and it is particularly suitable for modern agricultural scenarios such as facility agriculture, precision irrigation, and intelligent temperature control.

[0059] Embodiment 1 of the present invention provides an integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios, which combines photovoltaic power generation, energy storage systems, and charging functions for electric equipment. It can effectively solve problems such as insufficient power supply, low energy utilization efficiency, and complex equipment maintenance in agricultural production, and achieve autonomous power supply and optimized management of agricultural production, thereby improving the efficiency and sustainability of agricultural production.

[0060] At the same time, Embodiment 1 of the present invention provides an integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios. Through an intelligent operation strategy, when the power demand is low, the excess power is used to charge the energy storage system, and during peak power demand periods, power is preferentially supplied to key agricultural equipment, thereby ensuring the continuity and stability of agricultural production.

[0061] This integrated operation strategy not only optimizes the power utilization efficiency but also reduces the dependence on grid power, achieving energy self - sufficiency in agricultural production.

[0062] Embodiment 1 of the present invention provides an integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios.

[0063] Embodiment 1 of the present invention provides an integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios, as Figure 1 shown, including a photovoltaic power generation module 11, a bidirectional inverter 12, an energy storage system module 13, a portable energy storage battery pack 14, an electric equipment charging module 15, a power feedback to the grid module 16, and an intelligent operation control module 17. Among them, the intelligent operation control system that combines deep learning and agricultural machinery big data analysis technology enables the integrated equipment to cooperate with each other, achieving efficient management of intelligent photovoltaic power generation, energy storage, power conversion, and feedback; through the intelligent control system, agricultural machinery data is collected, analyzed, and processed in real - time to optimize energy utilization efficiency and improve the sustainability and intelligent level of agricultural production; at the same time, the integrated photovoltaic energy storage and charging equipment can achieve rapid charging of agricultural electric equipment and two - way management and feedback of power, providing an efficient, intelligent, and environmentally friendly solution for the integrated application of photovoltaic energy storage and charging in agricultural scenarios.

[0064] Among them, the photovoltaic power generation device 11 includes a photovoltaic panel assembly 21, a photovoltaic greenhouse 22, and a DC power distribution unit 23.

[0065] As Figure 2As shown in the figure, the photovoltaic panel assembly 21 is installed on the top of the photovoltaic greenhouse 22, which converts solar energy into electrical energy to provide a basic power source for various power demands in agricultural production.

[0066] The design of the photovoltaic greenhouse 22 fully considers the spatial layout, uneven terrain, sunlight conditions and seasonal changes in the agricultural scenario, and adopts a detachable and angle-adjustable installation structure to maximize the photovoltaic power generation efficiency. The photovoltaic greenhouse 22 is 40m long from north to south, 6m wide from east to west, tilted 5 degrees westward, with a set of support frames every 6m, and the triangular support frames enhance the stability performance.

[0067] A total of 700W photovoltaic panel components (2384*1303*33) are laid on the top of the photovoltaic greenhouse 22, with a total of 85 components, and the total capacity is 59.5kw. It is expected that the average daily power generation is 208.25 degrees, and the annual power generation is 76,000 degrees. The modular design of the photovoltaic panel components enables the photovoltaic system to be expanded or reduced according to the specific agricultural scenario requirements, and flexibly adapts to different scales of agricultural production needs.

[0068] The DC distribution unit 23 is used to manage and monitor the transmission of direct current, which includes a DC circuit breaker 231, a surge protector 232, a data acquisition unit 233, and a data sensor 234.

[0069] The DC circuit breaker 231 is installed in the DC combiner box of the photovoltaic module. When the current suddenly exceeds the preset value, the DC circuit breaker 231 will automatically cut off the circuit to prevent the equipment from being damaged due to excessive current. Especially in the case of short circuit or other faults, the safety operation can be ensured by disconnecting the DC circuit breaker, which is convenient for maintenance or repair.

[0070] The overvoltage protection device surge protector 232 is installed in the DC combiner box adjacent to the DC circuit breaker, mainly used to prevent instantaneous high voltage (such as lightning strike or power grid fluctuation) from damaging the photovoltaic system. When overvoltage occurs, the overvoltage protection device will quickly guide the excess voltage to the grounding system, thereby protecting the photovoltaic module and other electrical equipment.

[0071] The data acquisition unit (DAQ) 233: is installed in the DC distribution unit to collect real-time data on the power generation of the photovoltaic module and the power consumed to maintain the operation of basic agricultural facilities.

[0072] The data sensor 234: is installed in the DC distribution unit to transmit the power generation data to the computer background in real time.

[0073] The first end of the bidirectional inverter 12 is connected to the DC distribution unit 23 in the photovoltaic power generation device 1, and the second end is connected to the intelligent operation control module 17, the lithium-ion battery pack 31 in the energy storage system module 13, the portable energy storage battery pack 14, and the electric device charging module 15.

[0074] As shown Figure 3 in Figure, the energy storage system module 13 includes a lithium-ion battery pack 31, a plug-in inverter 32, a battery management system 33, a temperature control system 34, a data acquisition unit 35, and a data sensor 36.

[0075] The energy storage system module 13 is configured with 1-hour energy storage of 59.5 KWH according to the scale, and the final energy storage scale is configured based on this, with a maximum configuration of 200 KWH.

[0076] The photovoltaic panel assembly 21 transports electricity to the energy storage system module 13 through a connector. Multiple plug-and-play lithium-ion battery packs 31 in the energy storage system module 13 are used to store electrical energy (each battery unit has 96V). This portable lithium-ion battery pack can be connected to a plug-in inverter 32 and can be connected to the farmer's power generation system for power supply when needed. This design greatly improves the energy storage utilization efficiency. The electricity storage process is carried out under the strict monitoring of the energy storage management system, which includes:

[0077] Battery Management System (BMS) 33: Used to monitor the charging and discharging process of the battery to prevent overcharging or over-discharging; the Battery Management System (BMS) plays a role in the energy storage system, and its functions mainly cover multiple aspects such as real-time monitoring of battery status, charge and discharge management of electricity, safety protection, data acquisition and fault diagnosis, and energy management optimization. Monitor key parameters such as battery voltage, current, and temperature to ensure that the battery operates within a safe range, prevent excessive voltage or current, or overheating, thereby avoiding battery damage or safety accidents. At the same time, the BMS accurately reflects the remaining battery power and aging degree by managing the State of Charge (SOC) and State of Health (SOH) of the battery, helps optimize the charging and discharging operations, and extends the battery life. When the power in the photovoltaic integrated system is insufficient, electrical energy can be obtained from the public grid through the rectification function of the inverter to supply electrical energy to agricultural equipment; when the power in the system is excessive, the electrical energy is transmitted back to the public grid through the inversion function of the inverter. This design not only ensures the stable operation of agricultural machinery and equipment but also achieves the effect of overall peak shaving and valley filling of electricity and reasonably utilizes electricity.

[0078] Temperature control system 34: Ensure that the battery is within a safe temperature range and has a self-protection mechanism.

[0079] Data Acquisition Unit (DAQ) 35: Collect real-time data on battery electricity storage, such as the electricity storage capacity for one hour.

[0080] Data sensor 36: Installed on the energy storage system module, fixed by electric welding, and transmits power data to the computer background in real time.

[0081] The energy storage management system, based on the data of photovoltaic power generation and stored electricity, uses a linear regression algorithm based on supervised learning to predict the energy storage completion time, and adjusts the connector switch between the energy storage system module 13 and the photovoltaic panel module 11 in a timely manner according to the prediction results. By adopting the above technical solution, the energy storage system module 13 is used to store the excess electric energy generated by the photovoltaic power generation module 11 in sufficient sunlight, and provide power support for agricultural production equipment at night or in insufficient sunlight. It can also ensure that agricultural production can continue to operate in extreme weather or emergencies.

[0082] The electric equipment charging module 15 can be configured with a total of 13 charging piles, each with a power of 7KW.

[0083] As Figure 4 shown, the electric equipment charging module 15 consists of a fixed charging pile 41 and an intelligent linkage system 42.

[0084] The DC power generated by the photovoltaic panel module 21 in real time can be converted by the bidirectional inverter 12 and supplied to each charging pile 41. There are two charging modes: fast charging and slow charging. The charging pile 41 is cylindrical, and the wires inside the cylinder are directly connected to the photovoltaic panel module 21. This structure can protect and extend the service life of the wires, and at the same time enhance the safety of the charging pile 41.

[0085] Each charging pile 41 is connected to the intelligent linkage system 42. The intelligent linkage system 42 consists of a micro camera 421, a liquid crystal display 423, and an FPGA main board 422. The intelligent linkage system 42, equipped with a neural network model trained with a large amount of data, can automatically identify the types of machine equipment and battery status, adjust the optimal charging parameters, and ensure the efficient operation of the equipment. The electric equipment charging module 15 can automatically dispatch power during peak power demand periods, optimize the allocation of power resources, avoid the shutdown of agricultural machinery due to insufficient power, and users can also see the charging status in real time through the liquid crystal display. The main feature is that farmers can see the status of agricultural machinery charging in real time, and can receive reminders according to real-time conditions, such as the current battery life of agricultural machinery and the full charge time. Another main feature is that the integrated photovoltaic energy storage charging equipment and its construction plan built according to the actual application scenarios of agricultural machinery are easy to replicate and can be quickly promoted in agricultural scenarios.

[0086] Among them, the electric power stored in the portable energy storage battery pack 14 can also be directly used to charge the machine through an inverter.

[0087] By adopting the above solution, it is possible to reasonably utilize power resources to supply power to electric machines without affecting the normal operation of agricultural machinery, thus saving costs.

[0088] The electric power generated usually may be insufficient or excessive. In this case, a power feedback grid module 16 is designed. As Figure 6As shown, the power feedback grid module 16 includes a bidirectional power management module, an intelligent power trading module 51, and a grid security monitoring module 52.

[0089] Bidirectional power management module: This management integrates a bidirectional inverter, which can convert direct current into alternating current and feed it back to the grid. At the same time, when the grid power supply is sufficient or the electricity price is low, it can also purchase electricity from the grid for future needs, further optimizing the power cost.

[0090] Intelligent power trading module 51: The intelligent algorithm can monitor the grid electricity price in real time and dynamically adjust the power feedback strategy according to market fluctuations. For example, during high electricity price periods, give priority to feeding back electric energy to obtain higher economic benefits; during low electricity price periods, give priority to energy storage or supply to agricultural equipment.

[0091] Grid security monitoring module 52: To ensure the safety and stability of the grid, the load situation of the grid will be detected when feeding back power to avoid grid overload caused by excessive power input.

[0092] In addition, the power feedback grid module 16 also has a fault isolation function. When a grid fault occurs, the program will automatically disconnect the connection with the grid to ensure that the power supply in the agricultural scenario is not affected.

[0093] Adopting the above solution, the benefits generated by photovoltaic power generation can be maximized while ensuring normal power supply on the farm.

[0094] As Figure 7 shown, the intelligent operation control module 17 includes a sensor 61, a computer server 62, and a large liquid crystal display screen 63. Each module of the integrated photovoltaic energy storage and charging equipment can operate normally. The intelligent operation control module 17 can give an optimal operation strategy to the integrated equipment, making the use efficiency and benefits of the entire integrated equipment reach the optimal. The intelligent operation control module of the present invention is the core of the entire integrated photovoltaic energy storage and charging equipment:

[0095] By monitoring multi-dimensional data such as photovoltaic power generation, energy storage status, and power consumption demand of electric equipment in real time, dynamically adjust the power distribution strategy;

[0096] It can pre-adjust the photovoltaic power generation and energy storage plans according to external information such as weather forecasts, agricultural production plans, and crop growth cycles to ensure that agricultural production can obtain optimal power guarantee under different environmental conditions;

[0097] By coordinating the photovoltaic power generation, energy storage, and charging processes, a closed-loop energy management system is formed. During periods of sufficient sunlight, give priority to storing electric energy in the energy storage module and charging electric equipment according to actual needs; during periods of insufficient sunlight, the system will dispatch the stored energy to supply power to the equipment, while optimizing the power distribution to ensure the continuity and high efficiency of agricultural production;

[0098] It is also possible to flexibly allocate power resources according to the actual needs of agricultural machinery operations, so as to achieve on-demand supply of energy.

[0099] The intelligent operation control module 17 not only supports remote management, but also integrates advanced Internet of Things (IoT) technologies and cloud computing architectures, enabling real-time monitoring and management of device status. Through the sensor network and edge computing, part of the data can be processed locally to reduce latency, while key data is uploaded to the cloud for large-scale storage and analysis. The data analysis module combines big data technology and supervised learning. Through these technologies, efficient and precise agricultural power management is achieved, data-driven optimization suggestions can be provided, the utilization efficiency of photovoltaic power can be improved, the operating cost can be reduced, and decision-making support can be provided for the operation of photovoltaic agricultural machinery integrated equipment.

[0100] Another key point of the intelligent operation control module 17 is to establish a mathematical model for the charging and discharging scheduling of power resources based on real-time parameters obtained from the actual agricultural scenario, such as charge and discharge, electricity storage, and power consumption data of agricultural machinery operations. The fourth-order Runge-Kutta method is used to solve the model, and the results obtained are used as the control signal output of the intelligent operation control module 17.

[0101] An integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios provided in this embodiment constructs a multi-functional, intelligent, efficient, and green agricultural machinery power guarantee system by integrating photovoltaic power generation, energy storage systems, charging equipment, and intelligent control systems. It can provide continuous, stable, and environmentally friendly power supply for agricultural production, and through innovative operation strategies, achieve optimal scheduling and management of power resources.

[0102] An integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios provided in this embodiment integrates solar photovoltaic power generation, energy storage systems, and electric equipment charging functions. Through deep integration with agricultural Internet of Things devices, comprehensive intelligent power management and monitoring are achieved, which is applicable to a variety of modern agricultural scenarios; the modular design enables it to be flexibly configured according to different needs, with excellent scalability and adaptability, and through intelligent scheduling, the utilization of power resources is optimized, and it can calmly cope with seasonal changes and diverse demands, significantly improving the efficiency and sustainability of agricultural production.

[0103] Embodiment 2

[0104] This embodiment provides a control method for an integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios as described in Embodiment 1, including the following steps:

[0105] Step 1: Obtain solar energy. The photovoltaic panel assembly 21 is installed at the top of the photovoltaic greenhouse 22 in a tilted manner at an angle of 45°, and is detachable. The photovoltaic panel assembly 21 collects solar beams during the day and converts them into available electrical energy.

[0106] After the direct sunlight shines down, it irradiates the laid photovoltaic panel assembly 21. The photovoltaic greenhouse 22 is 40 m from north to south and 6 m from east to west, with a stable structure. The DC power distribution unit 23 is used to manage and monitor the transmission of the generated direct current. The DC circuit breaker 231 and the surge protector 232 are used to prevent damage caused by current short - circuit or over - voltage. The formula for the breaking current of the DC circuit breaker 231 is as follows:

[0107] I = 2×U / (π×f×L0×Bm) (1)

[0108] Where I is the breaking current, U is the breaking voltage, f is the power supply frequency, L0 is the wire length, and Bm is the magnetic induction intensity. When a current I0 greater than I suddenly surges in, the DC circuit breaker 231 will directly disconnect the circuit to protect the mechanical equipment.

[0109] When the area where the photovoltaic greenhouse is located is struck by lightning, the surge protector 232 can discharge the redundant current caused by lightning into the ground, thus protecting the safety of the personnel and equipment in the building.

[0110] The data acquisition unit 233 in the DC power distribution unit 23 is used to collect the real - time power generation data of the photovoltaic modules (such as 20 degrees / h) and the real - time power consumption data of agricultural facilities (such as 5 degrees / h) for 24 hours. The data sensor 234 in the DC power distribution unit transmits the power generation and power consumption data of agricultural facilities to the computer background in real - time through a wireless network, so that Figure 1 the intelligent operation control system 17 can perform data analysis. The bidirectional inverter 12 can transmit the current between the power grid and the agricultural photovoltaic grid at any time according to the instructions.

[0111] Step 2: The electricity generated by the photovoltaic panel assembly 21, with an average daily power generation of 208.25 degrees, directly supplies power to agricultural machinery facilities through an inverter. Under the control of the intelligent operation control system 17, the redundant power is stored in the energy storage system 13 and the portable energy storage battery pack 14 through the bidirectional inverter 12. With the support of the bidirectional inverter 12, it is plug - and - play after energy storage.

[0112] Step 3: The energy storage system 13 can store the electricity generated by the photovoltaic in a timely manner, with a convenient, safe and effective energy storage method. The direct current generated by solar energy is converted by the bidirectional inverter 12 and stored in the portable lithium - ion battery pack 31. There is also a plug - in inverter 32 in this energy storage battery module to directly access the farmer's power generation system for power supply. This design is simple to operate, greatly improves the energy storage utilization efficiency, satisfies the long - term operation endurance of agricultural machinery, and can also ensure the power supply of agricultural machinery during long - term rainy weather.

[0113] Reasonable energy storage can extend the battery life and ensure safety during the power storage process. In the energy storage module, the battery management system 33 and the temperature control system 34 can monitor the battery charging and discharging process in real time. Multiple thermal sensors in the temperature control system 34 are used to monitor the temperature data of the battery pack in real time and transmit it to the battery management system 33 for analysis. The reasonable temperature ranges for the battery in three states are as follows: charging temperature: 0°C to 45°C, discharging temperature: -20°C to 60°C, storage temperature: -20°C to 45°C (short-term storage). The battery management system adjusts the coolant flow rate, fan speed or turns on the heating device in grades according to the battery temperature change, and directly suspends the energy storage operation in severe cases. This design can ensure the safe use of the battery and prevent accidents caused by sudden temperature changes.

[0114] The data acquisition unit 35 collects the battery power storage rate data. The data sensor 36 fixed by electric welding transmits the real-time power storage data to the computer background. The computer server of the intelligent operation control system 17 in the background combines the past power storage data and the photovoltaic power generation and agricultural facility power consumption data collected in the above photovoltaic power generation module as the original data, and uses the multiple linear regression algorithm to predict the real-time energy storage situation, so as to dynamically adjust the energy storage plan.

[0115] The formula of the multiple linear regression model is as follows. Three main influencing factors are used as independent variables: x1 is the power storage data (stored electricity), x2 is the photovoltaic power generation power (current power generation), x3 is the agricultural facility power consumption (consumed electricity), the target variable y is the predicted remaining energy storage time, ε is the error term, and β i is the optimal parameter solution obtained by training and solving using the least squares method based on historical data.

[0116] y = β0 + β1x1 + β2x2 + β3x3 + ε (2)

[0117] This model can continuously strengthen the training through newly added data related to the electricity quantity to obtain better model parameters with higher accuracy. For the predicted time to complete energy storage, farmers can reasonably adjust the energy storage plan according to the actual situation to ensure that there is enough electricity stored for emergencies.

[0118] Step 4: Install the charging pile 15 under the photovoltaic greenhouse 22 according to the power generation. The electric equipment charging pile 15 is directly installed under the photovoltaic greenhouse 22. The photovoltaic greenhouse 22 can block the sun and rain, and can safely charge other electrical equipment while ensuring the normal power supply of agricultural facilities.

[0119] Under the photovoltaic greenhouse, charging piles for agricultural machinery are installed, equipped with two connectors for fast charging and slow charging. According to the settings of the photovoltaic greenhouse and photovoltaic modules, a total of 13 charging piles with a capacity of 7KW per unit are installed. The charging module of the electric equipment has 41 fixed charging piles, which are fixed by electric welding, and there is water-proof protection on the ground. The charging piles are directly connected to the photovoltaic power generation modules through inverters 12 for direct charging, which reduces the loss of electric energy during transmission. To achieve intelligent charging, this application designs an intelligent linkage system 42 for charging electric equipment. Among them, the micro camera 421 can capture the image of the current charging equipment and then transmit the image to the FPGA main board 422 of the computer background. The FPGA main board 422 simultaneously reads the current battery status parameters of the charging equipment and gives relevant analysis results. Figure 5 The intelligent linkage system has 6 basic functions:

[0120] Battery health detection: By combining images and battery status parameters, the program installed in the FPGA main board 422 can judge the health status of the battery (such as aging degree, damage, etc.) by comparing with the preset health parameter values in the database, and remind the user to maintain or replace the battery;

[0121] Intelligent charging mode switching: According to the battery health status and current battery level, the system designed in the present invention can select slow charging when the battery level is extremely low or nearly full, and use fast charging when the battery level is low to medium (20%-80%). Because when the battery is close to full charge, the voltage is relatively high, and excessive fast charging may cause thermal runaway or electrolyte decomposition. This design can maximize the protection of the battery life during charging;

[0122] Battery anomaly detection: Detect abnormal situations such as battery temperature and current, and send data to the intelligent operation control system 17 in time, and automatically disconnect the charging according to the received protection instruction to prevent damage;

[0123] Multi-mode data feedback: The liquid crystal display screen 423 displays the charging status, remaining time, abnormal situations (such as too high temperature), etc., enabling users to more intuitively understand the status and can also be broadcast in voice form;

[0124] Remote notification: The charging data is synchronized to the mobile application in real time, and farmers can monitor the charging status of the equipment from a distance, and can achieve completion reminders or abnormal alarms;

[0125] Data recording and analysis: Record the charging data each time to help users understand the charging habits of the agricultural machinery and the trend of battery performance decline. This system can improve the convenience and safety of charging agricultural machinery, and at the same time extend the life of the equipment and battery, making it more suitable for the needs of different agricultural application scenarios.

[0126] Step 5: Agricultural photovoltaic power generation can be connected to the grid. The power feedback to the grid module 16, under the control of the intelligent operation control system 17, can implement the peak shaving and valley filling strategy for electricity, achieving the maximization of benefits. The intelligent operation control system 17 coordinates the operation of the overall equipment, enabling the optimal use of each device.

[0127] The power feedback to the grid module 16 can efficiently manage the interactive operation between the photovoltaic integrated equipment and the grid. Its bidirectional inverter 12 allows electricity to be freely exchanged between the photovoltaic power and the grid. The intelligent power trading module 51 carried can intelligently predict the trend of the market electricity price. The intelligent power trading module 51 adopts the long short-term memory network (LSTM), which is suitable for dealing with the long-term dependence problem of the electricity price time series. This deep learning method has a strong prediction ability for non-stationary electricity price sequences.

[0128] Set the electricity price sequence as P = {P1, P2, …, P T}, where P t is the electricity price at the t-th time step. Assume that the LSTM needs to predict the next-step electricity price P T+1 based on the data of the past n time steps. Input the electricity price data P into the LSTM, and the calculation process is as follows:

[0129] (1) Input sequence preparation: Use the electricity prices of the past n time steps as the input sequence:

[0130] X = {P T-n+1 , P T-n+2 , …, P T} (3)

[0131] (2) LSTM model calculation: Input the input sequence X into the LSTM model, and update the cell state and hidden state through mechanisms such as the forget gate, input gate, and output gate to obtain the hidden state h T of the last time step, which contains the electricity price information of the past n time steps.

[0132] Calculation formula of the LSTM model unit:

[0133] f t = σ(W f · [h t-1 , P t + b f )(4)

[0134] i t = σ(W i · [h t-1 , P t + b i )(5)

[0135] o t = σ(W o·[h t-1 ,P t +b o )(6)

[0136]

[0137] h t =o t ·tanh(C t )(9)

[0138] Here, t = T - n + 1, …, T, h T is the final hidden state, which contains the features of the input sequence X.

[0139] (3) Predict the electricity price: Use the hidden state h T to perform a linear transformation through a fully connected layer to obtain the predicted future electricity price

[0140]

[0141] where, W h and b h are the weights and biases of the fully connected layer.

[0142] According to the prediction result, it can prompt farmers when it is appropriate to sell electricity to the power grid. This prediction ability can make the operation of power feedback to the power grid more flexible and maximize the benefits of agricultural photovoltaic power generation.

[0143] At the same time, the grid safety monitoring function 52 of this module can monitor the grid load problem in real time during operation and transmit the load data to Figure 1 the intelligent operation control system 17 in real time through sensors, and automatically disconnect and alarm in case of a failure.

[0144] Step 6: The integrated photovoltaic energy storage charging device is composed of various modules. Therefore, how to coordinate the operation of each module is extremely important. The sensor 61 in the intelligent operation control system 17 can receive and send instructions to each module device to control the operation of each module.

[0145] Step 601, Intelligent Operation Control System and Photovoltaic Power Generation Module: The data acquisition unit 233 of the DC unit of the intelligent operation control system can, through wireless sensors, enable the power generation module to send the photovoltaic power generation data in real time: the hourly power generation (kW / h), the voltage (V) and current (A) of the photovoltaic panel to the computer server of the intelligent operation control system, and these data are used as the original driving data for the operation of the control algorithm. According to the historical photovoltaic power generation data, multiple linear regression is used to predict the future power generation. If the predicted average daily power generation for the next 10 days is always lower than 80 kW, then the background computer server will send a message on the display screen to prompt the farmer to suggest increasing the photovoltaic power generation module to increase the power generation.

[0146] Step 602, Intelligent Operation Control System and Energy Storage System Module: Combining the photovoltaic power generation data described in Step 601 and the daily power consumption required by the agricultural electrification equipment, the background computer server obtains a specific operation strategy according to a pre-set algorithm:

[0147] (1) If the power generation today is 100 KW and the power consumption required today is 60 KW, when the power generation today is greater than the power consumption required today, then the intelligent operation control system can send an energy storage operation instruction to the energy storage system module through the sensor; if the power generation today is less than or equal to the power consumption required today, then a prohibited energy storage operation instruction is sent.

[0148] (2) According to the data collected by the thermal sensor, the battery management system 33 associated with the computer server makes a threshold judgment. If the current storage temperature is -20°C - 0°C, then a coolant flow rate prohibited start instruction is sent; if the storage temperature is 0°C - 20°C, then a coolant flow rate start medium speed instruction is sent; if the storage temperature is 20°C - 45°C, then a coolant flow rate start high speed instruction is sent; if the storage temperature is greater than 45°C, an alarm instruction is immediately sent to the energy storage module, and the alarm device is activated, and at the same time a suspended energy storage operation instruction is sent.

[0149] Step 603, Intelligent Operation Control System and Electric Equipment Charging Module: Assume that the current average daily power generation is 100 KW and there are 10 agricultural electric equipment that need to be charged:

[0150] (1) According to the battery capacities of the 10 electric equipment recorded in advance, a charging strategy for the 10 electric equipment is arranged according to the shortest job first algorithm, and after the strategy is formed, it is directly sent to the liquid crystal display screen 423;

[0151] (2) During charging, the intelligent linkage subsystem based on the intelligent operation control system can obtain the device diagram through the micro camera 421, and according to the battery state parameter actual availability rate of 80% of the charging device obtained, the maximum charging tolerance speed of 100 kw, the current battery level of 50%, etc.; when the battery level is in the range of 20%-80%, the actual battery availability rate is greater than 50%, and the maximum charging tolerance speed is greater than or equal to 100 kw, the computer calculates that fast charging can be used, then a fast charging instruction is directly sent to the charging pile through the wireless network, thus automatically switching to fast charging; on the contrary, slow charging can be automatically switched;

[0152] (3) Abnormal battery temperature, current, etc. are sent to the calculator server of the intelligent operation control system through wireless sensors. If the sudden temperature is greater than 40° and the current is greater than 8 A, then a charging prohibition instruction is directly sent, and a prompt message is sent to the farmer through the computer to suggest checking the charging pile and equipment;

[0153] (4) Record the charging data of each device each time, such as battery temperature, current abnormality, charging duration, etc., so as to reasonably adjust the charging strategy of the device. For example, if the device often has too high temperature and unstable current during charging, then the computer will automatically try to arrange the charging time period of the device when everyone is at work. When there are many people at work, charging faults can be solved in the first time.

[0154] Step 604, intelligent operation control system and power feedback power grid module: According to the average daily photovoltaic power generation within the next 10 days collected by the background (obtained according to the prediction algorithm in step 1), the electricity required for energy storage, and the daily consumption of electric devices (electric device charging amount + agricultural machinery facility power supply), determine the interaction operation between the photovoltaic greenhouse power and the power grid:

[0155] (1) If the photovoltaic power generation is 80 kw, the electricity required for energy storage is 30 kw, and the daily consumption of electric devices is 60 kw, then because 80 - 60 - 30 = -10 < 0 and the photovoltaic power generation of 80 kw is greater than the daily consumption of electric devices of 60 kw, electricity cannot be sold to the power grid, and the intelligent operation control system will send a charging prohibition operation instruction to the power feedback power grid module;

[0156] (2) If the photovoltaic power generation is 100 kw, the electricity required for energy storage is 10 kw, and the daily consumption of electric devices is 30, then because 100 - 10 - 30 = 60 > 0, electricity can be sold to the power grid, and the intelligent operation control system will send an operation instruction to allow selling 60 kw of electricity to the power feedback power grid module;

[0157] (3) If the photovoltaic power generation is 40 kw, the electricity required for energy storage is 20 kw, and the daily power consumption of electric equipment is 50 kw, then since the photovoltaic power generation of 40 kw is less than the daily power consumption of electric equipment of 50 kw, electricity needs to be purchased. The intelligent operation control system will send an operation instruction of allowing to buy 10 kw of electricity to the power feedback power grid module;

[0158] (4) Analyze the electricity price time series according to the long short-term memory network (LSTM) to predict the future market electricity price; when the electricity price is predicted: the electricity price peak from 2 pm to 4 pm tomorrow, send an instruction to the power feedback power grid module to sell the remaining electricity to the power grid during this period; when the electricity price is predicted: the electricity price low peak from 10 pm to 12 pm tomorrow, send an instruction to the power feedback power grid module to purchase 10 kw of electricity from the power grid during this period;

[0159] (5) According to the power grid load data obtained by the sensor, if the load suddenly increases to twice the normal value, immediately send a power grid disconnection instruction to the power feedback power grid module and issue a fault alarm to the farmer.

[0160] The intelligent operation control system controls the interaction process of the DC microgrid as Figure 8 shown. It can be directly seen the central role of the intelligent control system, which can directly coordinate the energy storage module, the power grid feedback module, the photovoltaic power generation module, etc. All data such as photovoltaic power generation parameters, current energy storage state parameters, normal power consumption of agricultural machinery, intelligent power trading data, weather data, etc. are used as the driving data of the computer server 62 of the intelligent operation control system. The server receives the data of the integrated equipment, and then uses the mixed integer linear programming algorithm to find the optimal operation solution of the equipment, and formulates a dynamic adjustment strategy according to the solution result.

[0161] The mathematical modeling of the charge and discharge system in the dynamic adjustment strategy is a complex process, which involves multiple aspects such as battery energy management, charging efficiency, discharging efficiency, load demand, and possible renewable energy input. The following are the steps to establish the charge and discharge mathematical model, which are used to describe the basic behavior of the agricultural machinery charge and discharge system:

[0162] (1) Hypothesis conditions

[0163] The agricultural machinery uses a rechargeable battery as the power source. The charging and discharging processes of its battery can be approximated as linear or non-linear functions. The working load of the agricultural machinery is known, and there is a solar photovoltaic module to charge the battery.

[0164] (2) Variable definition

[0165] E(t): The remaining energy of the battery at time t (unit: Wh). P in(t): The power (unit: W) charged into the battery at time t, which may include renewable energy and grid power supply. P out (t): The power (unit: W) output by the battery at time t, used for agricultural machinery work. ηcharge: Charging efficiency (between 0 and 1). ηdischarge: Discharging efficiency (between 0 and 1). P load (t): The load power (unit: W) of the agricultural machinery at time t.

[0166] (3) Mathematical model

[0167] The energy change of the battery can be expressed as:

[0168] dtdE(t) = ηcharge·P in (t) - ηdischargeP out (t) (11)

[0169] Here, the charging power multiplied by the charging efficiency increases the battery energy, and the discharging power divided by the discharging efficiency decreases the battery energy.

[0170] The charge-discharge power limit relationship is:

[0171] 0 ≤ P in (t) ≤ P in,max (12)

[0172] 0 ≤ P out(t) ≤ P out,max (13)

[0173] Among them, P in,max and P out,max are the maximum charging and discharging powers of the battery respectively.

[0174] The relationship between the load and the output power is:

[0175] P out(t) = max(P load (t), 0) (14)

[0176] If the load is 0 or negative (indicating energy feedback), the output power is 0. If the system includes renewable energy, such as solar panels, the charging power of the battery:

[0177] P in (t) = P solar (t) + P grid (t) (15)

[0178] Among them, P solar (t) is the power generated by the solar panel at time t, P grid(t) is the power obtained from the power grid (which can be positive or negative, representing charging or discharging to the power grid).

[0179] (4) Solution method

[0180] Numerical method: In the process of solving the energy change equation, the present invention adopts a variety of numerical integration methods, and the fourth-order Runge-Kutta method is adopted in the present invention. This method can effectively handle nonlinear differential equations, provide high-precision numerical solutions, and thus accurately simulate the charging and discharging process and energy change of the battery.

[0181] Optimization algorithm: In order to maximize battery life, minimize cost or meet specific working requirements, the present invention adopts the method of linear programming to optimize the charging and discharging cycle of the battery in the battery management system to achieve the optimal allocation of electric energy. The dynamic programming algorithm can be used in the multi-stage decision-making process to gradually optimize the battery usage strategy to ensure the optimal charging and discharging operation in each time period. This can find an approximate optimal solution in the complex optimization problem of charging and discharging strategies and improve the overall performance of the system.

[0182] Comprehensively applying the numerical method and the optimization algorithm can effectively solve the complex problems in the management of the integrated photovoltaic energy storage charging equipment, realize the efficient utilization of electric energy and the maximization of system performance, and ensure that the agricultural machinery equipment can maintain the best state under various operating conditions.

[0183] To implement a comprehensive integrated photovoltaic energy storage charging equipment and operation control strategy, the present invention is based on the multi-variable linear programming of the fourth-order Runge-Kutta method, combined with real-time power generation, stored electricity, equipment charging demand, electricity price prediction and equipment operation monitoring, to establish an optimal scheduling model. The goal of this invention is to optimize the power usage, ensure the normal operation of the equipment, charge and discharge in time and intelligently schedule the power buying and selling according to the electricity price change. According to the above charging and discharging solution model, the intelligent operation control system of the present invention coordinates each module to achieve the following model for maximizing the utilization rate of power resources:

[0184] (1) Determine system parameters:

[0185] Real-time power generation G(t) (kW): Real-time data of photovoltaic power generation.

[0186] Energy storage demand S(t) (kW): The amount of electricity required by the energy storage device.

[0187] Equipment power consumption D(t) (kW): Daily power consumption of agricultural electric equipment.

[0188] Power buying and selling amount R(t) (kW): The remaining electricity available for sale or the electricity to be purchased.

[0189] Battery temperature T(t) (°C): Monitor the temperature of the battery.

[0190] Current I(t) (A): Monitor the current during charging.

[0191] Electricity price prediction P(t) (yuan / kWh): The future electricity price predicted using the LSTM model.

[0192] (2) State variables and control variables:

[0193] State variables: G(t): Current photovoltaic power generation; S(t): Current stored electricity; D(t): Current device power consumption; R(t): Bought and sold electricity.

[0194] Control variables: u1(t): Electricity purchased from the grid (kW); u2(t): Electricity sold to the grid (kW).

[0195] (3) Power optimization dynamic model.

[0196] Use the fourth-order Runge-Kutta method to describe the dynamic behavior of the system, and set the differential equations as follows:

[0197] dx / dt = G(t) - D(t) - S(t) - u1(t) + u2(t) (15)

[0198] dx2 / dt = u2(t) - D(t) (16)

[0199] (4) State update formula.

[0200] At each time step t, use the fourth-order Runge-Kutta method to update the state variables:

[0201] Calculate k1, k2, k3, k4, where h i＝0,1,2,3 is the time step, representing the interval length of each time step in numerical integration, and its role is: to determine the discretization span from the current moment t to the next moment t + h i of. <000048......k1 = G(t) - D(t) - S(t) - u1(t) + u2(t) (17)

[0203] k2 = G(t + h1) - D(t + h1) - S(t + h1) - u1(t + h1) + u2(t + h1) (18)

[0204] k3 = G(t + h2) - D(t + h2) - S(t + h2) - u1(t + h2) + u2(t + h2) (19)

[0205] k4 = G(t + h3) - D(t + h3) - S(t + h3) - u1(t + h3) + u2(t + h3) (20)

[0206]

[0206] Update the status variable:

[0207] x(t + h i ) = x(t) + h i (k1 + 2k2 + 2k3 + k4)(21)

[0208] (5) Linear programming model:

[0209] The objective function is to maximize the revenue:

[0210]

[0211] The constraint conditions include:

[0212] Power balance: If the generated power is not enough to meet the charging demand, power needs to be purchased from the grid:

[0213] u1(t) = D(t) - (G(t) + S(t)) if G(t) + S(t) < D(t) (23)

[0214] Power sale: If the generated power is sufficient, the excess power can be sold:

[0215] u2(t) = G(t) + S(t) - D(t) if G(t) + S(t) ≥ D(t) (24)

[0216] Equipment operation monitoring: If T(t) > 40 and I(t) > 8, send a charging prohibition instruction to each module.

[0217] Non - negative constraint: All variables must be non - negative: u1(t) ≥ 0, u2(t) ≥ 0.

[0218] (6) Specific implementation steps for bringing in real - time data:

[0219] 6.1) Real - time data collection: Assume that today's generated power is G(0) = 100 kW. Equipment power consumption D(0) = 50 kW. Energy storage demand S(0) = 30 kW. Battery temperature 25°, current 3 A, voltage 220 v. Future electricity price 0.6 yuan / kWh

[0220] 6.2) Calculate the remaining power:

[0221] R(0) = G(0) - D(0) - S(0) = 100 - 50 - 30 = 20 kW

[0222] 6.3) Determine electricity selling or purchasing: According to the calculation result R(0) = 20 kW > 0, the battery temperature is 25°, and the current of 3 A is reasonable. Electricity can be sold; u2(0) = R(0) = 20 kW; Send an instruction to allow selling 20 kW of electricity. Using LSTM to predict the electricity price analysis, it is concluded that the electricity price is at a peak from 2 pm to 4 pm tomorrow, and an instruction is sent to sell the remaining 20 kW of electricity to the power grid during this period. If the voltage suddenly rises to 400 V with large fluctuations, stop selling or purchasing electricity.

[0223] 6.4) Specific steps for charging time arrangement

[0224] 6.4.1) Collect data: Today's power generation is 100 kW. The daily power consumption of the equipment is 50 kW. The power required by the energy storage equipment is 30 kW. Calculate the remaining power R(t):

[0225] R(t) = G(t) - D(t) - S(t) = 100 - 50 - 30 = 20 kW

[0226] 6.4.2) Equipment charging requirements: Suppose there are 10 agricultural electric equipment that need to be charged, and the charging requirements of each equipment are as follows (in kW): Equipment 1: 2 kW, Equipment ...... Equipment 10: 3 kW.

[0227] 6.4.3) Calculate the charging time of each equipment: According to the charging requirements of each equipment and historical charging data, select the fast charging or slow charging plan and calculate the charging time of each equipment. Suppose the charging time required for each equipment is: Equipment 1: 1 hour, Equipment 2: 1 hour, Equipment 3: 1 hour, Equipment 4: 0.5 hour, Equipment 5: 0.5 hour, Equipment 6: 1 hour, Equipment 7: 0.5 hour, Equipment 8: 0.5 hour, Equipment 9: 1 hour, Equipment 10: 1 hour.

[0228] 6.4.4) Priority sorting: According to the charging requirements and charging time of the equipment, give priority to arranging the equipment with the smallest charging requirement. It can be sorted from the smallest charging power:

[0229] Equipment 4: 1 kW (0.5 hour), Equipment 8: 1 kW (0.5 hour), Equipment ...... Equipment 9: 4 kW (1 hour).

[0230] 6.4.5) Arrange the charging time period: According to historical data, it is estimated that the power generation of the photovoltaic power generation module is relatively high from 2 PM to 4 PM, and the daily power consumption of the equipment is the lowest during this period. You can choose to charge during this time period. According to the remaining power R(t) = 20 kW, arrange the charging sequence and time period:

[0231] 2:00 PM - 2:30 PM: Device 4 (1 kW fast charge) charges for 0.5 hours. Remaining power: 20 - 1×0.5 = 19.5 kW.

[0232] 2:30 PM - 3:00 PM: Device 8 (1 kW fast charge) charges for 0.5 hours. Remaining power: 19.5 - 1×0.5 = 19 kW.

[0233] 3:00 PM - 4:00 PM: Device 1 (2 kW fast charge) charges for 1 hour. Remaining power: 19 - 2×1 = 17 kW.

[0234] 4:00 PM - 4:30 PM: Device 5 (2 kW slow charge) charges for 0.5 hours. Remaining power: 17 - 2×0.5 = 16 kW.

[0235] 4:30 PM - 5:00 PM: Device 7 (2 kW fast charge) charges for 0.5 hours. Remaining power: 16 - 2×0.5 = 15 kW.

[0236] 5:00 PM - 6:00 PM: Device 2 (3 kW slow charge) charges for 1 hour. Remaining power: 15 - 3×1 = 12 kW.

[0237] 6:00 PM - 7:00 PM: Device 6 (3 kW fast charge) charges for 1 hour. Remaining power: 12 - 3×1 = 9 kW.

[0238] 7:00 PM - 8:00 PM: Device 10 (3 kW fast charge) charges for 1 hour. Remaining power: 9 - 3×1 = 6 kW.

[0239] 8:00 PM - 9:00 PM: Device 3 (4 kW slow charge) charges for 1 hour. Remaining power: 6 - 4×1 = 2 kW.

[0240] 9:00 PM - 10:00 PM: Device 9 (4 kW slow charge) charges for 1 hour. Remaining power: 2 - 4×1 = -2 kW 2 - 4×1 = -2 kW (charging is not possible at this time).

[0241] 6.4.6) Adjust the charging plan: According to the remaining power and charging requirements, the charging of Device 9 needs to be postponed or adjusted. You can choose to change the charging time or charge during a time period with higher power generation.

[0242] 6.4.7) Send the dynamically adjusted real-time plan to the management personnel to efficiently arrange the charging of agricultural electric equipment.

[0243] (7) Monitor the equipment status: Monitor the battery temperature and current. If it is found that the temperature of a certain equipment exceeds 40°C or the current exceeds 8 A during charging, immediately send a charging prohibition instruction and record the abnormal data.

[0244] (8) Handle emergencies: Monitor the grid load data. If the load suddenly increases to twice the normal value, send an instruction to disconnect the grid connection and issue a fault alarm.

[0245] This combination can not only improve the performance of the overall system, but also significantly reduce the usage cost of agricultural machinery, and even bring considerable benefits to farmers, and can meet the specific requirements of different application scenarios.

[0246] At the same time, the large liquid crystal display screen 63 carried by the intelligent operation control system can display the current operation status of each module in real time. This design can assist farmers and managers to manually adjust the operation plan of the integrated equipment when necessary.

[0247] A control method for a photovoltaic storage and charging integrated equipment applied to an agricultural scenario provided by this embodiment scientifically allocates the specific operation strategies of agricultural machinery integrated equipment through a core intelligent operation control system and intelligent strategies carried by each module, and improves the utilization rate of photovoltaic power generation. Among them, the combination of Internet of Things technology and an alarm can ensure the ultra-high safety of electric energy when used on the farm.

[0248] A photovoltaic storage and charging integrated equipment applied to an agricultural scenario and its control method provided by this embodiment can be modularly transplanted for promotion across the country. The present invention effectively innovates various shortcomings in the existing technology of agricultural machinery electrification and has high industrial utilization value.

[0249] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios, characterized in that: It includes a photovoltaic panel assembly, an energy storage system module, a power feedback grid module, and an intelligent operation control module; The photovoltaic panel assembly is arranged on the top of the photovoltaic greenhouse and is used to convert solar energy into electric energy and supply power to agricultural machinery facilities; The energy storage system module is connected to the photovoltaic panel assembly through a bidirectional inverter and is used to store the electricity generated by the photovoltaic panel assembly and supply power to agricultural machinery facilities; The electric equipment charging module is connected to the energy storage system module and is used to charge electric equipment; The power feedback grid module is arranged between the bidirectional inverter and the grid and is used for grid connection of photovoltaic power generation; The intelligent operation control module is used to judge whether the energy storage system module stores the electricity generated by the photovoltaic panel assembly based on the power generation amount of the photovoltaic module and the power consumption amount of agricultural facilities, and control the energy storage system module; based on the power generation amount of the photovoltaic module, the electricity required for energy storage, the power consumption amount of agricultural facilities, and the charging amount of electric equipment, the fourth-order Runge-Kutta method is used to determine the amount of electricity purchased from or sold to the grid, and control the power feedback grid module.

2. The integrated optical storage and charging equipment applied to agricultural scenarios according to claim 1, wherein: The photovoltaic panel assembly is connected with a DC circuit breaker, and the formula for calculating the breaking current of the DC circuit breaker is as follows: I = 2×U / (π×f×L0×Bm) where I is the breaking current, U is the breaking voltage, f is the power supply frequency, L0 is the wire length, and Bm is the magnetic induction intensity; when a current greater than I surges in, the DC circuit breaker directly disconnects the circuit to protect agricultural machinery facilities.

3. The integrated optical storage and charging equipment applied to agricultural scenarios according to claim 1, characterized in that: The energy storage system module includes a lithium-ion battery pack and a plug-in inverter, and the plug-in inverter is used to supply power to agricultural machinery facilities after conversion of the lithium-ion battery pack.

4. The integrated optical storage and charging equipment applied to the agricultural scenario according to claim 3, characterized in that: The energy storage system module further includes a battery management system and a temperature control system; The temperature control system includes a plurality of thermosensitive sensors, which are used to monitor the temperature data of the battery pack in real time and transmit it to the battery management system; The battery management system adjusts the coolant flow rate, fan speed, or turns on the heating device in grades according to the change of the temperature data of the battery pack.

5. The integrated optical storage and charging equipment applied to the agricultural scenario according to claim 1, characterized in that: The energy storage plan of the energy storage system module is predicted by using a multiple linear regression algorithm: y = β0 + β1x1 + β2x2 + β3x3 + ε where x1 is the electricity already stored in the energy storage system module, x2 is the power generation amount of the photovoltaic module, x3 is the power consumption amount of agricultural facilities, y is the predicted remaining energy storage time, ε is the error term, and βi is the optimal parameter.

6. The integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios according to claim 1, characterized in that: The determination of the amount of electricity purchased from or sold to the grid is targeted at maximizing the profit: Among them, u1(t) is the amount of electricity purchased from the power grid, u2(t) is the amount of electricity sold to the power grid, and p sell (t) is the selling price of electricity, and p uy (t) is the purchase price of electricity.

7. The integrated optical storage and charging equipment applied to agricultural scenarios according to claim 6, characterized in that: Both the electricity selling price and the electricity purchasing price are predicted by analyzing the electricity price time series using a long short-term memory network.

8. The integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios according to claim 1, characterized in that: The determination of the amount of electricity purchased from or sold to the grid adopts the following constraint conditions: Power balance: If the power generation is not enough to meet the charging demand, electricity needs to be purchased from the grid: u1(t) = D(t) - (G(t) + S(t)) if G(t) + S(t) < D(t); Electricity selling: If the power generation is sufficient, the excess electricity can be sold: u2(t) = G(t) + S(t) - D(t) if G(t) + S(t) ≥ D(t); Equipment operation monitoring: If T(t) > 40 and I(t) > 8, charging is prohibited; Non - negative constraint: All variables must be non - negative: u1(t)≥0, u2(t)≥0; Among them, u1(t) is the electricity quantity purchased from the power grid, u2(t) is the electricity quantity sold to the power grid, G(t) is the electricity generated by the photovoltaic modules, S(t) is the electricity quantity required for energy storage, D(t) is the sum of the electricity consumption of agricultural facilities and the charging amount of electric equipment, and R(t) is the electricity quantity for buying and selling.

9. The integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios according to claim 1, characterized in that: The method for judging whether the energy storage system module stores the electricity generated by the photovoltaic panel module is as follows: Obtain the historical power generation data of the photovoltaic modules and predict the power generation of the photovoltaic modules on a certain day; Obtain the historical power consumption data of the agricultural facilities and predict the power consumption of the agricultural facilities on that day; If the power generation of the photovoltaic modules on that day exceeds the power consumption of the agricultural facilities on that day, the energy storage system module stores the electricity generated by the photovoltaic panel module.

10. The control method of an integrated photovoltaic energy storage and charging equipment applied to agricultural scenarios according to any one of claims 1-9, characterized in that: It includes the following steps: Based on the power generation of the photovoltaic modules and the power consumption of the agricultural facilities, judge whether the energy storage system module stores the electricity generated by the photovoltaic panel module and control the energy storage system module; Based on the power generation of the photovoltaic modules, the electricity quantity required for energy storage, the power consumption of the agricultural facilities and the charging amount of electric equipment, use the fourth - order Runge - Kutta method to determine the electricity quantity purchased or sold from the power grid and control the power feedback to the power grid module.

Images