Power generation facility control method, device, equipment, storage medium and program product

Abstract

This application provides a method, apparatus, device, storage medium, and program product for controlling power generation equipment. It belongs to the field of power system technology. The method includes: receiving power generation equipment data, energy storage equipment data, and weather data sent by an edge gateway; generating energy storage control parameters and a power generation plan based on the power generation equipment data, the energy storage equipment data, and the weather data; controlling the operating state of the energy storage equipment using the energy storage control parameters; and sending the power generation plan to the grid terminal. This method solves the problem of low overall operating efficiency of the power system.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, equipment, storage medium and program product for controlling power generation equipment. Background Technology

[0002] In distributed energy storage scenarios, energy storage devices (such as battery packs and supercapacitors) need to operate in conjunction with renewable energy systems such as photovoltaics and wind power.

[0003] Currently, the control methods for the status of energy storage devices and power generation plans in related technologies are usually set manually by staff.

[0004] However, the inventors discovered that the related technology has at least the following technical problems: manual setting of the operating status and power generation plan of the energy storage device by the staff will result in low overall system operating efficiency. Summary of the Invention

[0005] This application provides a power generation equipment control method, apparatus, equipment, storage medium, and program product to solve the problem of low overall system operating efficiency.

[0006] In a first aspect, embodiments of this application provide a method for controlling power generation equipment, comprising: receiving power generation equipment data, energy storage equipment data, and weather data sent by an edge gateway; generating energy storage control parameters and a power generation plan based on the power generation equipment data, energy storage equipment data, and weather data; controlling the operating status of the energy storage equipment using the energy storage control parameters; and sending the power generation plan to the grid terminal.

[0007] In one possible implementation, energy storage control parameters and a power generation plan are generated based on power generation equipment data, energy storage equipment data, and weather data, including: generating energy storage control parameters based on energy storage equipment data and preset peak-valley electricity prices; and generating a power generation plan based on power generation equipment data, energy storage control parameters, and weather data.

[0008] In one possible implementation, the power generation equipment data includes individual cell voltage, individual cell temperature, and individual cell cycle count; the energy storage control parameters include individual cell power; based on the energy storage equipment data and preset peak and valley electricity prices, the energy storage control parameters are generated, including: normalizing the individual cell voltage, individual cell temperature, and individual cell cycle count to a target range to obtain normalized voltage, normalized cell temperature, and normalized cycle count; inputting the normalized voltage, normalized cell temperature, normalized cycle count, and peak and valley electricity prices into the power prediction model to obtain the individual cell power output by the power prediction model.

[0009] In one possible implementation, a power generation plan is generated based on power generation equipment data, energy storage control parameters, and weather data. This includes: determining the amount of clean energy generated based on the power generation equipment data and weather data; determining the variable power of the energy storage equipment based on the energy storage control parameters; and inputting the variable power, the amount of clean energy generated, and the preset peak-valley electricity price into the plan generation model to obtain the power generation plan output by the plan generation model.

[0010] In one possible implementation, after receiving power generation equipment data, energy storage equipment data, and weather data sent by the edge gateway, the method further includes: generating a digital twin system based on the power generation equipment data and energy storage equipment data; and outputting the digital twin system.

[0011] In one possible implementation, the digital twin system includes at least one device twin model; after generating the digital twin system based on power generation equipment data and energy storage equipment data, it further includes: if the operating parameters corresponding to the target device twin model exceed the preset operating standards, then the target device twin model is changed to the target color.

[0012] Secondly, embodiments of this application provide a power generation equipment control device, comprising: a data receiving module for receiving power generation equipment data, energy storage equipment data, and weather data sent by an edge gateway; a data generation module for generating energy storage control parameters and a power generation plan based on the power generation equipment data, energy storage equipment data, and weather data; a status control module for controlling the operating status of the energy storage equipment using the energy storage control parameters; and a plan sending module for sending the power generation plan to the grid terminal.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0014] The memory stores instructions that the computer executes;

[0015] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0017] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0018] The power generation equipment control method, device, equipment, storage medium, and program product provided in this application receive power generation equipment data, energy storage equipment data, and weather data sent by the edge gateway, generate corresponding energy storage control parameters and power generation plans, use energy storage control parameters to control the operating status of energy storage equipment, and send the power generation plan to the grid terminal to achieve precise operation and management of energy storage equipment, increase the operating efficiency of energy storage equipment, and synchronize the power generation plan to the grid terminal, ultimately achieving coordinated scheduling of energy storage and grid, ensuring the stable operation of the grid, and improving energy utilization efficiency and the economic benefits of energy storage operation. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 A schematic diagram of the power generation equipment control system provided in this application;

[0021] Figure 2 A flowchart illustrating the power generation equipment control method provided in an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the structure of the power generation equipment control device provided in the embodiments of this application;

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0024] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0026] In distributed energy storage and power generation scenarios, energy storage devices (such as battery packs and supercapacitors) need to operate in conjunction with renewable energy sources such as photovoltaics and wind power.

[0027] In related technologies, the status of energy storage devices and power generation plans are mainly set manually. Due to the dynamic and random nature of renewable energy output and load demand, manual settings cannot respond to system changes in real time, causing energy storage devices to be unable to operate during the optimal window period, resulting in problems such as curtailment of solar power and insufficient power supply, which has become a key bottleneck for large-scale applications.

[0028] To address the above technical problems, the inventors propose the following technical concept: by receiving data from the power generation equipment, the energy storage equipment, and weather information sent by the edge gateway, the inventors control the operating status of the energy storage equipment and generate a power generation plan based on the received information.

[0029] This application is applied to scenarios involving the control of power generation equipment. It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0030] Figure 1 A schematic diagram of the power generation equipment control system provided in this application. Figure 1 The power generation equipment control system 100 includes: an edge gateway 101, a power generation device 102, an energy storage device 103, a server 104, and a power grid terminal 105.

[0031] In the specific implementation process, the edge gateway 101 can be a core device on the edge side, such as an inter-network connector or a protocol converter, which undertakes tasks such as data preprocessing, device access adaptation, and local real-time response.

[0032] The power generation equipment 102 may include photovoltaic power generation equipment, wind power equipment, nuclear power equipment, etc.

[0033] The energy storage device 103 can be an electrochemical energy storage device, a mechanical energy storage device, etc.

[0034] Server 104 and power grid terminal 105 can be implemented using a cluster of one or more servers with stronger processing power and higher security. Where possible, computers or laptops with strong computing power can also be used as alternatives.

[0035] Edge gateway 101 is used to collect data from power generation equipment 102 and energy storage equipment 103, and send the collected data to server 104. The server determines the control parameters and power generation plan of the energy storage equipment based on the received data, uses the control parameters to control the operating status of the energy storage equipment, and sends the power generation plan to grid terminal 105.

[0036] It is understood that the scenarios illustrated in the embodiments of this application do not constitute a specific limitation on the control method of power generation equipment. In other feasible embodiments of this application, the above scenarios may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and are not limited here. Figure 1 The scenario shown can be implemented by hardware, software, or a combination of both.

[0037] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0038] Figure 2 This is a flowchart illustrating the power generation equipment control method provided in an embodiment of this application. The execution entity of this embodiment may be... Figure 1 The server in this example can be a computer or / or mobile phone, etc., and this embodiment does not impose any particular limitations on it. Figure 2 As shown, the method includes:

[0039] S201: Receives power generation data from the power generation equipment, energy storage data from the energy storage equipment, and weather data sent by the edge gateway.

[0040] In this step, based on the TCP / IP communication protocol stack, the edge gateway can receive data from the power generation equipment, energy storage equipment, and weather through its built-in communication interface.

[0041] The acquisition methods for power generation equipment data, energy storage equipment data, and weather data can include collecting operational data from power generation equipment and energy storage equipment via industrial bus protocols such as Modbus and CAN, as well as acquiring weather data through external meteorological sensors or meteorological service interfaces. After preliminary format conversion and encapsulation by the edge gateway, this data is sent to the receiving port of this system via the TCP / IP protocol. This system listens on this port, receives and parses the data packets, and extracts the required data types.

[0042] The data includes operating data of power generation equipment such as output power, voltage, and current; operating data of energy storage equipment such as cycle count, charge / discharge power, and individual cell voltage / temperature; and weather data such as light intensity, wind speed, temperature, and humidity.

[0043] S202: Generate energy storage control parameters and power generation plans based on power generation equipment data, energy storage equipment data, and weather data.

[0044] In this step, the received power generation equipment data, energy storage equipment data, and weather data can first undergo data preprocessing, including data cleaning, data normalization, and feature extraction. Then, the preprocessed data is input into a pre-set model. The model aims to maximize energy utilization efficiency, minimize energy storage equipment losses, and mitigate grid load fluctuations. It comprehensively considers factors such as peak-valley electricity prices, grid dispatching needs, and energy storage equipment charging and discharging constraints, and uses intelligent optimization algorithms such as particle swarm optimization and genetic algorithms to determine energy storage control parameters and power generation plans.

[0045] Feature extraction can include extracting key features related to energy storage control and power generation planning. Key features include the relationship between photovoltaic output and irradiance, and the relationship between battery SOC and charge / discharge efficiency. Energy storage control parameters include charge / discharge power and charge / discharge time. Power generation planning includes power generation and grid connection power for different time periods.

[0046] S203: Use energy storage control parameters to control the operating status of energy storage equipment.

[0047] In this step, control commands can be generated based on energy storage control parameters and sent to the controller of the energy storage device. The controller of the energy storage device can then generate charging and discharging power commands for individual batteries based on the current state of the batteries through its internal control algorithm and send them to the battery management unit in the battery pack. The battery management unit then controls the specific charging and discharging capacity, power, and current of the batteries.

[0048] These include energy storage control parameters such as charge / discharge power commands and target SOC values. The current state of the battery can include the current SOC and individual cell voltage / temperature.

[0049] S204: Send the power generation plan to the grid terminal.

[0050] In this step, the power generation plan can be encapsulated into a compliant data message based on a preset communication protocol and data exchange standard, and then sent to the power grid terminal.

[0051] As described in the above embodiments, this disclosure embodiment receives power generation equipment data, energy storage equipment data, and weather data sent by the edge gateway, generates corresponding energy storage control parameters and power generation plans, uses energy storage control parameters to control the operating status of energy storage equipment, and sends the power generation plan to the grid terminal, thereby achieving precise operation and management of energy storage equipment, increasing the operating efficiency of energy storage equipment, and synchronizing the power generation plan to the grid terminal, ultimately achieving coordinated scheduling of energy storage and the grid, ensuring the stable operation of the grid, and improving energy utilization efficiency and the economic benefits of energy storage operation.

[0052] In one possible implementation, step S202 above, which generates energy storage control parameters and a power generation plan based on power generation equipment data, energy storage equipment data, and weather data, includes:

[0053] S2021: Generate energy storage control parameters based on energy storage device data and preset peak-valley electricity prices.

[0054] In this step, a multi-objective optimization algorithm can be used to generate corresponding control parameters based on the operating status of the energy storage device and peak-valley electricity prices. The multi-objective optimization algorithm takes energy storage device data as input and combines it with preset peak-valley electricity prices, aiming to maximize the economic benefits of the energy storage device and minimize its lifespan degradation, thus constructing an optimization function. Intelligent optimization algorithms such as particle swarm optimization and genetic algorithms are then used to solve the optimization function, obtaining the optimal energy storage control parameters for different time periods to achieve optimal operation of the energy storage device.

[0055] The data for energy storage devices may include individual battery voltage, temperature, and cycle count; economic benefits may be calculated, such as the revenue from peak-valley electricity price differences; and energy storage control parameters may include charging and discharging power and charging and discharging time.

[0056] S2022: Generate a power generation plan based on power generation equipment data, energy storage control parameters, and weather data.

[0057] In this step, based on power generation equipment data and weather data, a predictive model is used to forecast the amount of clean energy power generation over a future period. Then, by combining energy storage control parameters, the charging and discharging amounts of the energy storage equipment at different times are calculated, thereby determining the energy input / output of the energy storage equipment to the grid and obtaining a power generation plan.

[0058] This includes power generation equipment data such as the output power characteristics of photovoltaic inverters and the wind speed and power curves of wind turbines; weather data such as sunlight intensity and wind speed; and prediction models such as long short-term memory network models.

[0059] As can be seen from the description of the above embodiments, the embodiments of this disclosure combine energy storage device status data with peak and off-peak electricity prices to accurately generate suitable energy storage control parameters, ensuring the economical operation and safe loss control of energy storage devices. Furthermore, they integrate power generation equipment data, energy storage control parameters, and weather data to generate a power generation plan that aligns with clean energy power output, achieving coordinated optimization of energy storage operation and power generation scheduling. This provides accurate decision support for subsequent energy storage device management and power transmission scheduling.

[0060] In one possible implementation, the power generation equipment data includes individual cell voltage, individual cell temperature, and individual cell cycle count. Energy storage control parameters include individual cell power.

[0061] In this context, single-cell voltage can refer to the potential difference between the positive and negative terminals of a single cell within the battery pack, or it can refer to the entire battery pack as a single cell. Similarly, single-cell temperature can be the real-time temperature of a single cell. Single-cell cycle count can be the cumulative number of complete charge-discharge cycles from full charge to discharge and back to full charge. Single-cell power can include the input or output power of a single cell. Energy storage control parameters can also include the input or output power of a single cell.

[0062] In step S2021 above, energy storage control parameters are generated based on energy storage device data and preset peak-valley electricity prices, including:

[0063] S211: Normalize the individual cell voltage, individual cell temperature, and individual cell cycle number to the target range to obtain normalized voltage, normalized cell temperature, and normalized cycle number.

[0064] In this step, the individual cell voltage, temperature, and cycle number can be mapped to their respective numerical ranges, such as 0 to 1, 0 to 10, etc. First, the numerical range of each parameter is determined. Then, a linear normalization method is used to subtract the minimum value from the actual value of each parameter, and then divide by the difference between the maximum and minimum values ​​to obtain the normalized value, which takes the range "[0,1]".

[0065] For example, if the range of a single cell voltage is 2.5V-3.65V, the range of temperature is -20℃-60℃, and the range of cycle count is 0-2000, then the single cell voltage of 2.5V can be mapped to 0, 3.65V to 1, and other values ​​can be mapped to the intermediate values. Similarly, the range of cycle count and the range of temperature can be mapped.

[0066] S212: Input the normalized voltage, normalized battery temperature, normalized cycle number, and peak-valley electricity price into the power prediction model to obtain the single-cell power output by the power prediction model.

[0067] In this step, the power prediction model can employ a particle swarm optimization (PSO) algorithm and / or a genetic algorithm. Normalized voltage, normalized battery temperature, normalized cycle count, and peak / valley electricity price are used as input variables for the PSO algorithm, with individual battery power as the optimization variable. Then, a fitness function is constructed based on the preset operating constraints and optimization objectives of the energy storage device. The PSO algorithm initializes a swarm of particles by simulating the foraging behavior of a flock of birds. Each particle updates its velocity and position based on its historical best position and the global best position of the swarm, until the maximum number of iterations is reached or the fitness fluctuation obtained after n consecutive iterations is less than a preset fluctuation threshold, thus yielding the individual battery power.

[0068] The operational constraints include, for example, the upper limit of charge / discharge power and the range of SOC variation; the optimization objectives include, for example, maximizing economic benefits and minimizing lifespan loss; each particle represents a set of individual battery power values. The power of an individual battery can correspond to a time period.

[0069] As can be seen from the description of the above embodiments, the embodiments of this disclosure can convert data of different orders of magnitude into comparable standardized data through normalization processing, which is convenient for subsequent input into the power prediction model for optimization calculation. Through multiple iterations of the power prediction model, the power value of a single battery cell suitable for the battery state can be found, thereby increasing the operating efficiency of the energy storage system.

[0070] In one possible implementation, the fitness function used by the particle swarm optimization algorithm is:

[0071]

[0072] in ; The weighting of the cycle life of a single cell is 0.3-0.5. The weighting for battery life loss is 0.2-0.4. The economic benefit weight is 0.2-0.4; σ is the standard deviation of the individual SOC (the smaller the value, the better the balance); L is the lifespan degradation rate (the smaller the value, the longer the lifespan); and E is the revenue per unit time. E = (peak discharge price - off-peak charging price) × charging and discharging capacity - charging and discharging loss cost, used to calculate revenue based on real-time electricity prices and predicted charging and discharging capacity.

[0073] In one possible implementation, step S2022 above, which generates a power generation plan based on power generation equipment data, energy storage control parameters, and weather data, includes:

[0074] S221: Determine the amount of clean energy generated based on power generation equipment data and weather data.

[0075] In this step, power generation equipment data and weather data can be input into a pre-trained power generation prediction model to obtain the clean energy power generation output by the power generation prediction model.

[0076] The power generation prediction model can be trained using methods such as linear regression, support vector machines, and neural networks. During training, historical data on power generation equipment and corresponding weather data can be collected. Key weather data affecting clean energy power generation (such as the impact of solar irradiance on photovoltaic output and wind speed on wind power output) can be input into the model for training, learning the mapping relationship between power generation equipment data and weather data.

[0077] S222: Determine the change in electricity of the energy storage device based on the energy storage control parameters.

[0078] In this step, the change in the amount of electricity stored in the energy storage device can be calculated based on the energy storage control parameters of the energy storage device.

[0079] For example, the charge and discharge capacity of the energy storage device in each time period can be calculated based on the energy storage control parameters and the charging and discharging efficiency of the energy storage device. The change in energy quantity can include the charging quantity or the discharging quantity. During the charging period, the charging quantity of the energy storage device = charging power × charging time × charging efficiency; during the discharging period, the discharging quantity of the energy storage device = discharging power × discharging time × discharging efficiency.

[0080] Among them, energy storage control parameters include charging and discharging power and charging and discharging time.

[0081] S223: Input the variable electricity volume, clean energy power generation, and preset peak-valley electricity price into the planning generation model to obtain the power generation plan output by the planning generation model.

[0082] In this step, the planning generation model can be trained using DeepQNetwork (DQN). The model's input data includes varying electricity volume, clean energy generation, and peak-valley electricity prices, forming the objective function and constraints. Solving this model yields the optimal power generation, grid connection volume, and power output curves for different time periods—essentially, the power generation plan.

[0083] As can be seen from the description of the above embodiments, the embodiments of this disclosure accurately obtain the clean energy power generation by combining power generation equipment data and weather data, then obtain the energy storage change power by combining energy storage control parameters, and output the optimal power generation plan through the planning generation model by integrating peak and valley electricity prices. This achieves precise matching between the power generation plan and the characteristics of clean energy, the energy storage operation status and the economic dispatch needs of the power grid, providing a scientific basis for the efficient and coordinated dispatch of the power grid, while taking into account both energy utilization efficiency and operational economic benefits.

[0084] In one possible implementation, after receiving the power generation data from the power generation device, the energy storage data from the energy storage device, and the weather data sent by the edge gateway in step S201, the method further includes:

[0085] S230: Generate a digital twin system based on data from power generation equipment and energy storage equipment.

[0086] In this step, a digital twin system model can be constructed based on 3D modeling and data mapping technology. This includes creating digital twin geometric models of power grid facilities such as power generation equipment, energy storage equipment, transmission lines, and substations, as well as energy storage equipment. Then, through data interfaces, the data of power generation equipment and energy storage equipment (such as operating status, power, voltage, and current) are associated and mapped with the digital twin models to establish the correspondence between data and models.

[0087] For example, the output power data of a photovoltaic inverter can be mapped to the luminous intensity of the photovoltaic panel in a digital twin model, and the SOC data of an energy storage device can be mapped to the battery power display in a digital twin model.

[0088] In one possible implementation, when generating the digital twin system, the direction of current flow can be determined based on data from the power generation equipment and energy storage equipment, and then displayed in the digital twin system. For example, when the power generation equipment data indicates that the power generation equipment is outputting electrical energy to the grid and energy storage equipment, an energy flow animation effect can be used to show that electrical energy is flowing from the power generation equipment to the energy storage equipment and the grid.

[0089] S231: Output digital twin system.

[0090] In this step, the digital twin system model can be graphically displayed to the user using computer graphics and visualization rendering technologies. Alternatively, the digital twin system can be sent to the maintenance terminal for display.

[0091] As can be seen from the description of the above embodiments, the embodiments of this disclosure generate a digital twin model based on the data of power generation equipment and energy storage equipment, and output the digital twin model, so that the digital twin system model can reflect the operating status of the physical power grid in real time, and realize the visualization monitoring and analysis of the power grid.

[0092] In one possible implementation, the digital twin system includes at least one device twin model.

[0093] The equipment twin model can include energy storage equipment twin models or power generation equipment twin models.

[0094] After generating the digital twin system based on the power generation equipment data and energy storage equipment data in step S230 above, the following steps are also included:

[0095] S232: If the operating parameters corresponding to the target device twin model exceed the preset operating standards, then change the target device twin model to the target color.

[0096] In this step, preset operating standards are established, such as the normal operating power range, temperature range, and voltage range. Real-time data from the power generation and energy storage devices are compared with these preset operating standards. If the operating parameters exceed the preset standards, the color of the corresponding device is changed (e.g., from blue to red).

[0097] As can be seen from the description of the above embodiments, the embodiments of this disclosure monitor the status of the device twin model in the digital twin system and provide early warning of anomalies through preset operating standards and visualization rendering.

[0098] In one possible implementation, abnormal data from the device can be directly sent to the operation and maintenance terminal. After changing the target device's twin model, control commands sent by the operation and maintenance terminal can be received and transmitted to the corresponding energy storage or power generation device via an edge gateway, thereby enabling the energy storage or power generation device to operate according to the parameters of the control commands. Alternatively, a long short-term memory neural network can be used to construct a battery degradation model for battery health status strategy protection and battery early warning. By inputting energy storage device data into the battery degradation model, battery degradation status data or early warning data can be obtained.

[0099] Figure 3 This is a schematic diagram of the structure of a power generation equipment control device provided in an embodiment of this application. Figure 3 As shown, the power generation equipment control device 300 includes: a data receiving module 301, a data generation module 302, a status control module 303, and a plan sending module 304.

[0100] The data receiving module 301 is used to receive power generation data from the power generation equipment, energy storage data from the energy storage equipment, and weather data sent by the edge gateway.

[0101] The data generation module 302 is used to generate energy storage control parameters and power generation plans based on power generation equipment data, energy storage equipment data and weather data.

[0102] The status control module 303 is used to control the operating status of the energy storage device using energy storage control parameters.

[0103] The plan sending module 304 is used to send the power generation plan to the grid terminal.

[0104] The apparatus provided in this embodiment can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effects are similar, and will not be described again here.

[0105] In one possible implementation, the data generation module 302 is used to generate energy storage control parameters based on energy storage device data and preset peak-valley electricity prices. A power generation plan is then generated based on power generation device data, energy storage control parameters, and weather data.

[0106] The apparatus provided in this embodiment can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effects are similar, and will not be described again here.

[0107] In one possible implementation, the power generation equipment data includes individual cell voltage, individual cell temperature, and individual cell cycle count. Energy storage control parameters include individual cell power.

[0108] The data generation module 302 is used to normalize the individual cell voltage, individual cell temperature, and individual cell cycle count to a target range to obtain normalized voltage, normalized cell temperature, and normalized cycle count. The normalized voltage, normalized cell temperature, normalized cycle count, and peak-valley electricity price are then input into the power prediction model to obtain the individual cell power output by the power prediction model.

[0109] The apparatus provided in this embodiment can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effects are similar, and will not be described again here.

[0110] In one possible implementation, the data generation module 302 is used to determine the clean energy power generation based on power generation equipment data and weather data. It also determines the variable power output of the energy storage device based on energy storage control parameters. The variable power output, the clean energy power generation, and the preset peak-valley electricity price are input into the planning generation model to obtain the power generation plan output by the model.

[0111] The apparatus provided in this embodiment can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effects are similar, and will not be described again here.

[0112] In one possible implementation, the power generation equipment control device 300 further includes a system generation module 305.

[0113] The system generation module 305 is used to generate a digital twin system based on data from power generation equipment and energy storage equipment. It then outputs the digital twin system.

[0114] The apparatus provided in this embodiment can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effects are similar, and will not be described again here.

[0115] In one possible implementation, the digital twin system includes at least one device twin model.

[0116] In one possible implementation, the power generation equipment control device 300 further includes a color changing module 306.

[0117] The color change module 306 is used to change the target device twin model to the target color if the operating parameters corresponding to the target device twin model exceed the preset operating standards.

[0118] The apparatus provided in this embodiment can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effects are similar, and will not be described again here.

[0119] To implement the above embodiments, this application also provides an electronic device.

[0120] refer to Figure 4 The diagram illustrates a structural schematic of an electronic device 400 suitable for implementing embodiments of this application. The electronic device 400 can be a terminal device or a server. The terminal device can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, personal digital assistants (PDAs), portable Android devices (PADs), portable media players (PMPs), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0121] like Figure 4 As shown, the electronic device 400 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 401 and a memory 402 communicatively connected to the processor. The processor can perform various appropriate actions and processes based on programs stored in the memory 402, computer-executed instructions, or programs loaded from storage device 408 into random access memory (RAM) 403, thereby implementing the power generation equipment control method in any of the above embodiments. The memory may be a read-only memory (ROM). The RAM 403 also stores various programs and data required for the operation of the electronic device 400. The processing device 401, the memory 402, and the RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0122] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic device 400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 400 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0123] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 409, or installed from storage device 408, or installed from memory 402. When the computer program is executed by processing device 401, it performs the functions defined in the methods of embodiments of this application.

[0124] It should be noted that the computer-readable storage medium described above in this application can be a computer-readable signal medium, a computer storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0125] The aforementioned computer-readable storage medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0126] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0127] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0129] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the units do not necessarily limit the module itself.

[0130] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0131] This application also provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the technical solution of the power generation equipment control method in any of the above embodiments. Its implementation principle and beneficial effects are similar to those of the power generation equipment control method, and can be found in the implementation principle and beneficial effects of the power generation equipment control method, which will not be repeated here.

[0132] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the technical solution of the power generation equipment control method in any of the above embodiments. Its implementation principle and beneficial effects are similar to those of the power generation equipment control method, and can be found in the implementation principle and beneficial effects of the power generation equipment control method, which will not be repeated here.

[0134] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0135] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0136] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for controlling power generation equipment, characterized in that, include: Receive data from the edge gateway, including data from the power generation equipment, energy storage equipment, and weather data. Based on the power generation equipment data, the energy storage equipment data, and the weather data, generate energy storage control parameters and a power generation plan; The operating status of the energy storage device is controlled using the aforementioned energy storage control parameters; The power generation plan is sent to the grid terminal.

2. The method according to claim 1, characterized in that, The step of generating energy storage control parameters and a power generation plan based on the power generation equipment data, the energy storage equipment data, and the weather data includes: Based on the energy storage device data and the preset peak-valley electricity price, energy storage control parameters are generated; The power generation plan is generated based on the power generation equipment data, the energy storage control parameters, and the weather data.

3. The method according to claim 2, characterized in that, The power generation equipment data includes individual battery voltage, individual battery temperature, and individual battery cycle count; the energy storage control parameters include individual battery power. Based on the energy storage device data and the preset peak-valley electricity price, energy storage control parameters are generated, including: The individual cell voltage, individual cell temperature, and individual cell cycle number are normalized to the target range to obtain normalized voltage, normalized cell temperature, and normalized cycle number; The normalized voltage, the normalized battery temperature, the normalized cycle number, and the peak-valley electricity price are used as inputs to the power prediction model to obtain the output power of the single cell from the power prediction model.

4. The method according to claim 2, characterized in that, The step of generating the power generation plan based on the power generation equipment data, the energy storage control parameters, and the weather data includes: The amount of clean energy generated is determined based on the data from the power generation equipment and the weather data. The change in power of the energy storage device is determined based on the energy storage control parameters. The variable electricity volume, the clean energy power generation, and the preset peak-valley electricity price are input into the planning generation model to obtain the power generation plan output by the planning generation model.

5. The method according to claim 1, characterized in that, After receiving the power generation data, energy storage data, and weather data from the power generation equipment, as well as the data from the energy storage equipment, sent by the edge gateway, the system further includes: A digital twin system is generated based on the data from the power generation equipment and the energy storage equipment. Output the digital twin system.

6. The method according to claim 5, characterized in that, The digital twin system includes at least one device twin model; After generating the digital twin system based on the power generation equipment data and the energy storage equipment data, the method further includes: If the operating parameters corresponding to the target device twin model exceed the preset operating standards, the target device twin model will be changed to the target color.

7. A power generation equipment control device, characterized in that, include: The data receiving module is used to receive power generation data from the power generation equipment, energy storage data from the energy storage equipment, and weather data sent by the edge gateway. The data generation module is used to generate energy storage control parameters and a power generation plan based on the power generation equipment data, the energy storage equipment data, and the weather data. The status control module is used to control the operating status of the energy storage device using the energy storage control parameters; The plan sending module is used to send the power generation plan to the grid terminal.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 6.

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