A control method, system, device, product and medium of a flywheel energy storage device

Abstract

The application relates to the technical field of energy management, and provides a control method, a system, equipment, products and media of a flywheel energy storage device, which comprises the following steps: installing a photovoltaic power generation component, arranging a flywheel energy storage device and a photovoltaic component temperature regulating system, connecting the flywheel energy storage device with the photovoltaic component temperature regulating system; installing a weather data sensor, collecting weather data including atmospheric particulate matter density and wind speed, and acquiring power grid load data; acquiring historical data, determining an initial neural network and obtaining a preliminary prediction result, training the initial neural network, obtaining a target neural network, and thus obtaining a target prediction result including a photovoltaic component predicted temperature parameter; controlling the charging and discharging process of the flywheel energy storage device according to the target prediction result; and controlling the temperature of the photovoltaic power generation component by the photovoltaic component temperature regulating system according to the photovoltaic component predicted temperature parameter. The application can realize efficient control of the flywheel energy storage device.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to a control method, system, device, product and medium for a flywheel energy storage device. Background Technology

[0002] Currently, electrochemical energy storage systems are commonly used in power grid energy storage due to their rapid power response characteristics. These systems can compensate for the shortcomings in response speed of traditional thermal power units and the fluctuations in photovoltaic power generation modules, improving the tracking speed and accuracy of system frequency regulation control commands. However, electrochemical energy storage systems only have a few thousand charge-discharge cycles over their lifespan, which is insufficient to cope with frequent frequency fluctuations caused by grid power imbalances. Flywheel energy storage, on the other hand, has a much faster response speed and a lifespan capacity of millions of charge-discharge cycles. Therefore, flywheel arrays can be used to suppress fluctuations in the difference between the power generated by photovoltaic power generation and the load power.

[0003] However, due to bearing friction and air resistance, the energy stored in flywheel energy storage will be continuously lost during the energy storage process. Therefore, the stored energy needs to be used as soon as possible. In addition, excessively high or low temperatures during the use of photovoltaic power generation modules will also affect the power generation efficiency and lifespan of the photovoltaic power generation modules. The adhesion of dust in the air to the surface of photovoltaic power generation modules will also affect the power generation efficiency. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a control method, system, device, product, and medium for flywheel energy storage devices, enabling efficient energy storage scheduling and utilization of flywheel energy storage devices.

[0005] This invention provides a control method for a flywheel energy storage device, comprising:

[0006] S1: Determine the installation conditions of the photovoltaic power generation module and install the photovoltaic power generation module, set up the flywheel energy storage device and the photovoltaic module temperature regulation system, and connect the flywheel energy storage device to the photovoltaic module temperature regulation system;

[0007] S2: Install a weather data sensor to collect weather data, including atmospheric particulate matter density and wind speed, and obtain power grid load data;

[0008] S3: Obtain historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter with the weather data and the target neural network.

[0009] S4: Control the charging and discharging process of the flywheel energy storage device according to the target prediction results;

[0010] S5: The photovoltaic module temperature control system regulates the temperature of the photovoltaic power generation module based on the predicted temperature parameters of the photovoltaic module.

[0011] According to a control method for a flywheel energy storage device provided by the present invention, in step S1, the photovoltaic module temperature regulation system includes a photovoltaic air-cooling module and a photovoltaic heating module, wherein the air outlet of the photovoltaic air-cooling module is directed above the photovoltaic power generation module, the photovoltaic heating module includes a heat storage medium and a gas circulation module, and the gas circulation module is located below the photovoltaic power generation module.

[0012] According to the control method of the flywheel energy storage device provided by the present invention, step S2 specifically includes:

[0013] S21: Install weather data sensors including cameras, wind speed sensors and particulate matter analyzers;

[0014] S22: Collect illumination data and cloud data through the camera, collect wind speed through the wind speed sensor, and collect atmospheric particulate matter density through the particulate matter analyzer. Use the illumination data, cloud data, wind speed, and atmospheric particulate matter density as weather data to obtain power grid load data.

[0015] According to the control method of the flywheel energy storage device provided by the present invention, step S3 specifically includes:

[0016] S31: Obtain historical data including photovoltaic module power generation and photovoltaic module temperature parameters;

[0017] S32: Determine the initial neural network, and input the historical data, the installation conditions, and the power grid load data into the fully connected layer and activation function layer of the initial neural network to obtain the preliminary prediction result;

[0018] S33: Calculate the loss value of the preliminary prediction result relative to the historical data and the power grid load data using a loss function, and train the initial neural network using the loss value and the backpropagation algorithm to obtain the target neural network;

[0019] S34: Input the weather data and the power grid load data into the target neural network to obtain the target prediction result including the photovoltaic module prediction temperature parameter.

[0020] According to the control method of the flywheel energy storage device provided by the present invention, in step S34, the initial power generation prediction result in the target prediction result is adjusted according to the particulate density prediction result and the wind speed prediction result to obtain the power generation prediction result.

[0021] According to a control method for a flywheel energy storage device provided by the present invention, in step S5, the operating temperature of the module is determined. When the predicted temperature parameter of the photovoltaic module is higher than the operating temperature of the module, cold air is blown out from the air outlet of the photovoltaic air-cooled module to cool down the photovoltaic power generation module.

[0022] When the predicted temperature parameter of the photovoltaic module is lower than the module's operating temperature, the heat storage medium of the photovoltaic heating module heats the working gas in the gas circulation module, and the heated working gas then heats the photovoltaic power generation module.

[0023] The present invention also provides a control system for a flywheel energy storage device, comprising:

[0024] Flywheel device deployment module: used to determine the installation conditions of photovoltaic power generation modules and install photovoltaic power generation modules, set up flywheel energy storage devices and photovoltaic module temperature control systems, and connect the flywheel energy storage devices to the photovoltaic module temperature control systems;

[0025] Data acquisition module: used to install weather data sensors, collect weather data including atmospheric particulate matter density and wind speed through the weather data sensors, and acquire power grid load data;

[0026] Target prediction result module: used to acquire historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter by using weather data and the target neural network;

[0027] Charge and discharge control module: used to control the charge and discharge process of the flywheel energy storage device according to the target prediction results;

[0028] Temperature control module: Used to control the temperature of the photovoltaic power generation module by the photovoltaic module temperature control system based on the predicted temperature parameters of the photovoltaic module.

[0029] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the control method for a flywheel energy storage device as described above.

[0030] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a control method for a flywheel energy storage device as described above.

[0031] The present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which, when executed by a computer, enable the computer to perform the steps of the control method for a flywheel energy storage device as described above.

[0032] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0033] This invention provides a control method, system, device, product, and medium for a flywheel energy storage device. By using a neural network to predict the power generation and temperature parameters of a photovoltaic power generation module, the flywheel energy storage device is controlled to store and convert the electrical energy generated by the photovoltaic power generation module in a timely manner. The converted electrical energy is then used to control the temperature of the photovoltaic power generation module, thereby improving the lifespan and efficiency of the photovoltaic power generation module and achieving efficient scheduling of the flywheel energy storage device and electrical energy.

[0034] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating a control method for a flywheel energy storage device provided by the present invention.

[0037] Figure 2 This is a schematic diagram of the control system of a flywheel energy storage device provided by the present invention.

[0038] Figure 3 This is a schematic diagram of the structure of the control device for a flywheel energy storage device provided by the present invention.

[0039] Figure label:

[0040] 100. Flywheel device deployment module; 200. Data acquisition module; 300. Target prediction result module; 400. Charge and discharge control module; 500. Temperature control module; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0042] In the description of the embodiments of the present invention, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention. In addition, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0043] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.

[0044] In embodiments of the present invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0045] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0046] The following is combined Figures 1 to 3 Description of embodiments of the present invention:

[0047] Figure 1 This is a flowchart illustrating a control method for a flywheel energy storage device provided by the present invention. The method includes: first, installing photovoltaic power generation modules; setting up and connecting the flywheel energy storage device and the photovoltaic module temperature control system; then installing weather data sensors to collect weather data and obtain grid load data; next, acquiring historical data and training an initial neural network to obtain a target neural network, thereby obtaining a target prediction result; then controlling the charging and discharging process of the flywheel energy storage device based on the target prediction result; and finally controlling the photovoltaic module temperature control system to regulate the temperature of the photovoltaic power generation modules.

[0048] This invention provides a control method for a flywheel energy storage device, comprising:

[0049] S1: Determine the installation conditions of the photovoltaic power generation module and install the photovoltaic power generation module, set up the flywheel energy storage device and the photovoltaic module temperature regulation system, and connect the flywheel energy storage device to the photovoltaic module temperature regulation system;

[0050] Furthermore, the objective of this stage is to install photovoltaic power generation modules and set up flywheel energy storage equipment and photovoltaic module temperature control system. Specifically, in step S1, the photovoltaic module temperature control system includes a photovoltaic air-cooled module and a photovoltaic heating module. The air outlet of the photovoltaic air-cooled module is directed upwards towards the photovoltaic power generation module, and the photovoltaic heating module includes a heat storage medium and a gas circulation module, with the gas circulation module located below the photovoltaic power generation module.

[0051] The specific implementation method for the above steps in this embodiment is as follows:

[0052] First, the installation conditions for the photovoltaic (PV) power generation modules are determined, including the installation environment, installation angle, and installation height, before the modules are installed. In this embodiment, the PV power generation modules are photovoltaic panels. A flywheel energy storage device is also installed. This device stores the electrical energy generated by the PV modules using the kinetic energy of the flywheel and releases the stored energy to the external power grid as needed. Furthermore, since PV modules have an optimal operating temperature, excessively high or low temperatures will affect their lifespan and power generation efficiency. Because the flywheel energy storage device converts electrical energy into kinetic energy, the stored energy is continuously dissipated during operation due to air resistance and friction. Once the flywheel reaches its rated speed, it can no longer store energy. This means that the flywheel energy storage device is not suitable for long-term energy storage and needs to be put into use as soon as possible. Therefore, a PV module temperature control system is implemented.

[0053] The photovoltaic (PV) module temperature control system includes a PV air-cooled module and a PV heating module. The PV air-cooled module includes a compressor, a cold storage medium, cold air piping, and an air outlet, with the air outlet facing upwards towards the PV module. The PV heating module includes a thermal storage medium and a gas circulation module, with the gas circulation module located below the PV module. The PV module, flywheel energy storage device, and external power grid are connected. The flywheel energy storage device is also connected to the external power grid and the PV module temperature control system. The electrical energy generated by the PV module is transmitted to the flywheel energy storage device. When the electrical energy generated by the PV module exceeds the demand of the external power grid, and the flywheel speed of the flywheel energy storage device exceeds an empirically set lower speed limit, the excess energy stored in the flywheel energy storage device can be used to cool the cold storage medium via the compressor and to heat the thermal storage medium via electric heating.

[0054] S2: Install a weather data sensor to collect weather data, including atmospheric particulate matter density and wind speed, and obtain power grid load data;

[0055] Furthermore, the objective of this stage is to collect weather data using weather data sensors and to obtain power grid load data. Specifically, step S2 includes:

[0056] S21: Install weather data sensors including cameras, wind speed sensors and particulate matter analyzers;

[0057] S22: Collect illumination data and cloud data through the camera, collect wind speed through the wind speed sensor, and collect atmospheric particulate matter density through the particulate matter analyzer. Use the illumination data, cloud data, wind speed, and atmospheric particulate matter density as weather data to obtain power grid load data.

[0058] The specific implementation method for the above steps in this embodiment is as follows:

[0059] First, weather data sensors, including a camera, wind speed sensor, and particulate matter analyzer, are installed in suitable locations. Then, the camera takes pictures of the sky to determine illumination data, including light intensity and angle, as well as cloud data, including cloud cover and thickness. Wind speed data is collected using the wind speed sensor, and atmospheric particulate matter density data is collected using the particulate matter analyzer. Next, the illumination data, cloud data, wind speed, and atmospheric particulate matter density are used as weather data. Finally, historical data on external power grid load is also collected as power grid load data.

[0060] S3: Obtain historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter with the weather data and the target neural network.

[0061] Furthermore, the objective of this stage is to obtain preliminary prediction results through the initial neural network, train the initial neural network to obtain the target neural network, and finally obtain the target prediction result through the target neural network. Specifically, step S3 includes:

[0062] S31: Obtain historical data including photovoltaic module power generation and photovoltaic module temperature parameters;

[0063] S32: Determine the initial neural network, and input the historical data, the installation conditions, and the power grid load data into the fully connected layer and activation function layer of the initial neural network to obtain the preliminary prediction result;

[0064] S33: Calculate the loss value of the preliminary prediction result relative to the historical data and the power grid load data using a loss function, and train the initial neural network using the loss value and the backpropagation algorithm to obtain the target neural network;

[0065] S34: Input the weather data and the power grid load data into the target neural network to obtain the target prediction result including the photovoltaic module prediction temperature parameter.

[0066] In step S34, the initial power generation prediction result in the target prediction result is adjusted based on the particulate matter density prediction result and the wind speed prediction result to obtain the power generation prediction result.

[0067] The specific implementation method for the above steps in this embodiment is as follows:

[0068] First, cleaned and standardized historical data is acquired. This data includes photovoltaic (PV) module power generation and temperature parameters, specifically historical data on PV module temperature, solar irradiance, cloud cover, wind speed, and atmospheric particulate matter density over a past period. A convolutional neural network (CNN) is then selected as the initial neural network. This initial neural network consists of fully connected layers, activation function layers, convolutional layers, and pooling layers. By inputting the earlier historical data, grid load data, and installation conditions into the fully connected and activation function layers of the initial neural network, predictions based on more recent historical data and grid load data are obtained—the preliminary prediction results.

[0069] Next, a loss function is constructed using mean squared error loss. This loss function is then used to calculate the loss value of the initial prediction result relative to later historical data and grid load data. The internal parameters of the initial neural network are adjusted using the backpropagation algorithm based on this loss value, completing the initial neural network training. This allows the initial neural network to learn the relationship between weather data and photovoltaic module power generation, and also to predict grid load and photovoltaic module temperature, thus obtaining the target neural network. Subsequently, weather data and grid load data are input into the target neural network. The target neural network first predicts future weather data, including particulate matter density and wind speed predictions. Then, based on future weather data related to clouds and sunlight, photovoltaic module installation conditions, and grid load data, the target neural network obtains an initial prediction result g. The initial prediction result is then weighted and biased, and activated using the softmax function. This yields the target prediction result, which includes future photovoltaic module temperature parameters, grid load prediction data, and the initial photovoltaic module power generation prediction. :

[0070]

[0071] in, The weighting adjustment coefficient is determined based on experience. The bias coefficient is determined empirically, and softmax() indicates that the content within the parentheses is activated using the softmax function.

[0072] Furthermore, since dust in the air falling on the surface of photovoltaic power generation modules reduces the efficiency of the modules, but wind can remove the dust to some extent and slow down the rate of efficiency reduction, the target neural network needs to adjust the initial power generation prediction in the target prediction result based on the particulate matter density prediction and wind speed prediction results in future weather data to obtain the power generation prediction result.

[0073] S4: Control the charging and discharging process of the flywheel energy storage device according to the target prediction results;

[0074] Furthermore, the objective of this stage is to control the charging and discharging process of the flywheel energy storage device. Specifically, at any given moment, when the grid load forecast in the target prediction result is higher than the power generation forecast result, the flywheel energy storage device needs to convert the kinetic energy stored in the flywheel into electrical energy and release it into the external power grid based on the difference between the two to meet the external power grid's demand. When the grid load forecast in the target prediction result is lower than the power generation forecast result and the flywheel speed is lower than the lower speed limit, the photovoltaic power generation device stores the excess electrical energy in the flywheel energy storage device, and the flywheel speed of the flywheel energy storage device increases. When the grid load forecast in the target prediction result is lower than the power generation forecast result and the flywheel speed is higher than the lower speed limit, the photovoltaic power generation device stores a portion of the electrical energy in the flywheel energy storage device. The excess energy stored in the flywheel energy storage device is used to cool the cold storage medium through a compressor and to heat the thermal storage medium through electric heating.

[0075] S5: The photovoltaic module temperature control system regulates the temperature of the photovoltaic power generation module based on the predicted temperature parameters of the photovoltaic module.

[0076] Furthermore, the objective of this stage is to control the flywheel energy storage device to drive the photovoltaic module temperature regulation system, thereby regulating the temperature of the photovoltaic power generation module. Specifically, in step S5, the module operating temperature is determined. When the predicted temperature parameter of the photovoltaic module is higher than the module operating temperature, cold air is blown out from the air outlet of the photovoltaic air-cooled module to cool the photovoltaic power generation module.

[0077] When the predicted temperature parameter of the photovoltaic module is lower than the module's operating temperature, the heat storage medium of the photovoltaic heating module heats the working gas in the gas circulation module, and the heated working gas then heats the photovoltaic power generation module.

[0078] The specific implementation method for the above steps in this embodiment is as follows:

[0079] First, it's necessary to obtain the predicted temperature parameters of the photovoltaic (PV) modules at a specific moment, i.e., a prediction of the module's temperature at that instant. Simultaneously, the module's operating temperature is determined based on its optimal operating temperature. When the predicted temperature parameter exceeds the module's operating temperature, cooling is required. This is achieved by using the cold storage medium in the PV air-cooled module to cool the air. The cooled air then flows through cold air ducts to the air outlet, where it blows out cool air, thus cooling the PV modules. Furthermore, the cool air can remove dust and debris from the surface of the PV modules, further improving their operating efficiency.

[0080] When the predicted temperature parameter of the photovoltaic module is lower than the module's operating temperature, the heat storage medium of the photovoltaic heating module heats the working gas in the gas circulation module, and then uses the heated working gas to heat the photovoltaic power generation module.

[0081] This invention can effectively improve the working efficiency of photovoltaic power generation modules and realize the efficient utilization of energy in flywheel energy storage systems.

[0082] The control device for a flywheel energy storage device provided by the present invention is described below. The control device for a flywheel energy storage device described below and the control method for a flywheel energy storage device described above can be referred to in correspondence with each other.

[0083] Figure 2 An example is a schematic diagram of the control system of a flywheel energy storage device, such as... Figure 2 As shown, a control method for performing a flywheel energy storage device as described above includes:

[0084] Flywheel device deployment module 100: used to determine the installation conditions of photovoltaic power generation modules and install photovoltaic power generation modules, set up flywheel energy storage devices and photovoltaic module temperature control systems, and connect the flywheel energy storage devices to the photovoltaic module temperature control systems;

[0085] Data acquisition module 200: used to install weather data sensors, collect weather data including atmospheric particulate matter density and wind speed through the weather data sensors, and acquire power grid load data;

[0086] Target prediction result module 300: used to acquire historical data, determine an initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain a preliminary prediction result, train the initial neural network with the historical data and the preliminary prediction result to obtain a target neural network, and obtain a target prediction result including photovoltaic module prediction temperature parameters with weather data and the target neural network;

[0087] Charge and discharge control module 400: used to control the charge and discharge process of the flywheel energy storage device according to the target prediction results;

[0088] Temperature control module 500: Used to control the temperature of the photovoltaic power generation module by the photovoltaic module temperature control system according to the predicted temperature parameters of the photovoltaic module.

[0089] on the other hand, Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logic instructions in the memory 830 to execute a control method for a flywheel energy storage device, the method including:

[0090] S1: Determine the installation conditions of the photovoltaic power generation module and install the photovoltaic power generation module, set up the flywheel energy storage device and the photovoltaic module temperature regulation system, and connect the flywheel energy storage device to the photovoltaic module temperature regulation system;

[0091] S2: Install a weather data sensor to collect weather data, including atmospheric particulate matter density and wind speed, and obtain power grid load data;

[0092] S3: Obtain historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter with the weather data and the target neural network.

[0093] S4: Control the charging and discharging process of the flywheel energy storage device according to the target prediction results;

[0094] S5: The photovoltaic module temperature control system regulates the temperature of the photovoltaic power generation module based on the predicted temperature parameters of the photovoltaic module.

[0095] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute a control method for a flywheel energy storage device provided by the above methods, the method comprising:

[0097] S1: Determine the installation conditions of the photovoltaic power generation module and install the photovoltaic power generation module, set up the flywheel energy storage device and the photovoltaic module temperature regulation system, and connect the flywheel energy storage device to the photovoltaic module temperature regulation system;

[0098] S2: Install a weather data sensor to collect weather data, including atmospheric particulate matter density and wind speed, and obtain power grid load data;

[0099] S3: Obtain historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter with the weather data and the target neural network.

[0100] S4: Control the charging and discharging process of the flywheel energy storage device according to the target prediction results;

[0101] S5: The photovoltaic module temperature control system regulates the temperature of the photovoltaic power generation module based on the predicted temperature parameters of the photovoltaic module.

[0102] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a control method for a flywheel energy storage device provided by the methods described above, the method comprising:

[0103] S1: Determine the installation conditions of the photovoltaic power generation module and install the photovoltaic power generation module, set up the flywheel energy storage device and the photovoltaic module temperature regulation system, and connect the flywheel energy storage device to the photovoltaic module temperature regulation system;

[0104] S2: Install a weather data sensor to collect weather data, including atmospheric particulate matter density and wind speed, and obtain power grid load data;

[0105] S3: Obtain historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter with the weather data and the target neural network.

[0106] S4: Control the charging and discharging process of the flywheel energy storage device according to the target prediction results;

[0107] S5: The photovoltaic module temperature control system regulates the temperature of the photovoltaic power generation module based on the predicted temperature parameters of the photovoltaic module.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a flywheel energy storage device, characterized in that, include: S1: Determine the installation conditions of the photovoltaic power generation module and install the photovoltaic power generation module, set up the flywheel energy storage device and the photovoltaic module temperature regulation system, and connect the flywheel energy storage device to the photovoltaic module temperature regulation system; S2: Install a weather data sensor to collect weather data, including atmospheric particulate matter density and wind speed, and obtain power grid load data; S3: Obtain historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter with the weather data and the target neural network. S4: Control the charging and discharging process of the flywheel energy storage device according to the target prediction results; When the grid load forecast data in the target forecast results is higher than the power generation forecast results, the flywheel energy storage device converts the kinetic energy stored in the flywheel into electrical energy and releases it into the external power grid based on the difference between the two. When the grid load forecast data in the target forecast results is lower than the power generation forecast results and the flywheel speed is lower than the lower limit of the speed, the photovoltaic power generation equipment will store the excess electrical energy in the flywheel energy storage device; when the grid load forecast data in the target forecast results is lower than the power generation forecast results and the flywheel speed is higher than the lower limit of the speed, the photovoltaic power generation equipment will store a portion of the electrical energy in the flywheel energy storage device. The excess energy stored in the flywheel energy storage device is used to cool the cold storage medium through the compressor and to heat the thermal storage medium through electric heating. S5: The photovoltaic module temperature control system regulates the temperature of the photovoltaic power generation module based on the predicted temperature parameters of the photovoltaic module.

2. The control method for a flywheel energy storage device according to claim 1, characterized in that, In step S1, the photovoltaic module temperature control system includes a photovoltaic air-cooling module and a photovoltaic heating module. The air outlet of the photovoltaic air-cooling module is directed above the photovoltaic power generation module. The photovoltaic heating module includes a heat storage medium and a gas circulation module, and the gas circulation module is located below the photovoltaic power generation module.

3. The control method for a flywheel energy storage device according to claim 1, characterized in that, Step S2 specifically includes: S21: Install weather data sensors including cameras, wind speed sensors and particulate matter analyzers; S22: Collect illumination data and cloud data through the camera, collect wind speed through the wind speed sensor, and collect atmospheric particulate matter density through the particulate matter analyzer. Use the illumination data, cloud data, wind speed, and atmospheric particulate matter density as weather data to obtain power grid load data.

4. The control method for a flywheel energy storage device according to claim 1, characterized in that, Step S3 specifically includes: S31: Obtain historical data including photovoltaic module power generation and photovoltaic module temperature parameters; S32: Determine the initial neural network, and input the historical data, the installation conditions, and the power grid load data into the fully connected layer and activation function layer of the initial neural network to obtain the preliminary prediction result; S33: Calculate the loss value of the preliminary prediction result relative to the historical data and the power grid load data using a loss function, and train the initial neural network using the loss value and the backpropagation algorithm to obtain the target neural network; S34: Input the weather data and the power grid load data into the target neural network to obtain the target prediction result including the photovoltaic module prediction temperature parameter.

5. The control method for a flywheel energy storage device according to claim 4, characterized in that, In step S34, the initial power generation prediction result in the target prediction result is adjusted based on the particulate matter density prediction result and the wind speed prediction result to obtain the power generation prediction result.

6. The control method for a flywheel energy storage device according to claim 2, characterized in that, In step S5, the operating temperature of the component is determined. When the predicted temperature parameter of the photovoltaic component is higher than the operating temperature of the component, the air outlet of the photovoltaic air-cooled component blows out cold air to cool down the photovoltaic power generation component. When the predicted temperature parameter of the photovoltaic module is lower than the module's operating temperature, the heat storage medium of the photovoltaic heating module heats the working gas in the gas circulation module, and the heated working gas then heats the photovoltaic power generation module.

7. A control system for a flywheel energy storage device, used to execute the control method for a flywheel energy storage device as described in any one of claims 1 to 6, characterized in that, include: Flywheel device deployment module: used to determine the installation conditions of photovoltaic power generation modules and install photovoltaic power generation modules, set up flywheel energy storage devices and photovoltaic module temperature control systems, and connect the flywheel energy storage devices to the photovoltaic module temperature control systems; Data acquisition module: used to install weather data sensors, collect weather data including atmospheric particulate matter density and wind speed through the weather data sensors, and acquire power grid load data; Target prediction result module: used to acquire historical data, determine the initial neural network, input the historical data, the installation conditions and the power grid load data into the initial neural network to obtain preliminary prediction results, train the initial neural network with the historical data and the preliminary prediction results to obtain the target neural network, and obtain the target prediction result including the photovoltaic module prediction temperature parameter by using weather data and the target neural network; Charge and discharge control module: used to control the charge and discharge process of the flywheel energy storage device according to the target prediction results; Temperature control module: Used to control the temperature of the photovoltaic power generation module by the photovoltaic module temperature control system based on the predicted temperature parameters of the photovoltaic module.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the control method for a flywheel energy storage device as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method for a flywheel energy storage device as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that, When the program instructions are executed by the computer, the computer is able to perform the steps of the control method for a flywheel energy storage device as described in any one of claims 1 to 6.

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