Power data processing method and power dispatching method for renewable energy power generation system

Abstract

A power data processing method and a power dispatching method for a renewable energy power generation system. The power data processing method is applied to an edge control module of the renewable energy power generation system, and comprises: using operation data and environmental data as inputs, calling a preset power dispatching model, and performing prediction to obtain load demand change data and power generation capacity change data of the renewable energy power generation system, wherein the power dispatching model is obtained by training a long short-term memory model on the basis of historical environmental data and historical operation data of the renewable energy power generation system; and further determining a power dispatching strategy on the basis of the load demand change data and the power generation capacity change data, thereby providing data support for subsequent efficient power dispatching performed by a cloud server. The power dispatching method is applied to the cloud server of the renewable energy power generation system, and power dispatching is performed on the renewable energy power generation system on the basis of the power dispatching strategy, thereby improving the dispatching efficiency of the renewable energy power generation system.

Description

Power data processing methods and power dispatching methods for renewable energy power generation systems

[0001] Related applications

[0002] This application claims priority to Chinese patent application filed on November 5, 2024, application number 202411563854.0, entitled "Power Data Processing Method and Power Dispatch Method for Renewable Energy Power Generation System", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of electronic power technology, and in particular to a power data processing method for a renewable energy power generation system, a power dispatching method for a renewable energy power generation system, a renewable energy power generation system, a power data processing device for a renewable energy power generation system, computer equipment, a computer-readable storage medium, and a computer program product. Background Technology

[0004] With the rapid development of renewable energy, renewable energy power generation systems are playing an increasingly important role in energy management and the power system. However, due to the frequent fluctuations in renewable energy power generation, current renewable energy power generation systems suffer from low energy efficiency and low levels of intelligence.

[0005] In traditional solutions, cloud servers are typically deployed in renewable energy power generation systems. These remote cloud servers receive operational data from the renewable energy power generation systems and then remotely schedule the systems based on this data, thereby improving the energy efficiency and intelligence of the renewable energy power generation systems.

[0006] However, as renewable energy power generation systems become more complex, the amount of operational data that needs to be uploaded to cloud servers in traditional solutions is increasing, and the time required to upload operational data is also increasing. Cloud servers also need to spend a lot of time processing so much operational data, which leads to a slower response speed of renewable energy power generation systems to emergencies and low scheduling efficiency. Summary of the Invention

[0007] Based on this, it is necessary to provide a power data processing method for a renewable energy power generation system, a power dispatching method for a renewable energy power generation system, a renewable energy power generation system, a power data processing device for a renewable energy power generation system, a computer device, a computer-readable storage medium, and a computer program product that can improve dispatching efficiency in response to the above-mentioned technical problems.

[0008] In a first aspect, this application provides a power data processing method for a renewable energy power generation system, the system including an edge control module deployed on the power supply module side; the method is applied to the edge control module, including:

[0009] Acquire environmental data, as well as the operating data of the renewable energy power generation system transmitted by the power supply module;

[0010] Using the operational data and environmental data as input, a preset power dispatch model is invoked to predict the load demand change data and power generation change data of the renewable energy power generation system.

[0011] Based on the load demand change data and the power generation change data, a power dispatch strategy is determined;

[0012] The power dispatch model is trained on a long short-term memory model based on historical environmental data and historical operating data of the renewable energy power generation system.

[0013] In one embodiment, the power data processing method further includes:

[0014] Using the aforementioned operating data as input, a preset operating state prediction model is invoked to predict the operating state of the renewable energy power generation system.

[0015] The operational status prediction model is obtained by training a long short-term memory model based on historical operational data and the historical operational status of the renewable energy power generation system.

[0016] In one embodiment, the method further includes:

[0017] Based on the operating status, the health status of the renewable energy power generation system is assessed to obtain the health status assessment result;

[0018] If the health status assessment results indicate that the renewable energy power generation system is operating abnormally, a preset alarm mechanism is triggered to generate an operation status adjustment command. The operation status adjustment command is then sent to the power supply module in the renewable energy power generation system to adjust the operation status of the power supply module.

[0019] In one embodiment, the method further includes:

[0020] Based on the operational data, the power generation efficiency of the power supply module in the renewable energy power generation system is determined;

[0021] If the power supply module is determined to be in an abnormal working state based on the power generation efficiency, a device maintenance message is pushed.

[0022] Secondly, this application also provides a power data processing device for a renewable energy power generation system, the system including an edge control module deployed on the power supply module side; the device is applied to the edge control module. The device includes:

[0023] The data acquisition module is used to acquire environmental data and the operating data of the renewable energy power generation system sent by the power supply module;

[0024] The model prediction module is used to take the operating data and the environmental data as input, call the preset power dispatch model, and predict the load demand change data and power generation change data of the renewable energy power generation system.

[0025] The strategy determination module is used to determine the power dispatch strategy based on the load demand change data and the power generation change data;

[0026] The power dispatch model is trained on a long short-term memory model based on historical environmental data and historical operating data of the renewable energy power generation system.

[0027] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the power data processing method for renewable energy power generation systems.

[0028] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in any of the above embodiments of the power data processing method for renewable energy power generation systems.

[0029] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the power data processing method for renewable energy power generation systems.

[0030] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for processing power data in renewable energy power generation systems differ from traditional solutions that directly upload the operating data of renewable energy power generation systems to a cloud server. This solution deploys an edge control module within the renewable energy power generation system. This edge control module pre-stores a power dispatch model trained on a long short-term memory (LSTM) model based on historical environmental and operational data. Instead of directly uploading environmental and operational data to the cloud server, the power dispatch model uses these data as input. The model outputs load demand and power generation variation data for the renewable energy power generation system. The edge control module can then determine the power dispatch strategy based on these data. Because the LSM model can capture complex time-series dependencies, it effectively handles the fluctuations in renewable energy power generation over time, resulting in an accurate dispatch strategy. This eliminates the need to upload large amounts of operational data to a cloud server; instead, the edge control module, closer to the data source, processes the operational data, efficiently and accurately generating the dispatch strategy, thus providing a foundation for efficient system dispatching by the cloud server.

[0031] Sixthly, this application provides a power dispatching method for a renewable energy power generation system, the system comprising interconnected edge control modules and a cloud server, the method being applied to the cloud server, comprising:

[0032] The edge control module receives a power dispatching strategy, which is obtained by the edge control module using the steps in any of the above-described power data processing method embodiments.

[0033] Based on the power dispatch strategy, the renewable energy power generation system is dispatched.

[0034] In one embodiment, the number of edge control modules is multiple, and receiving the power dispatching strategy sent by the edge control modules includes:

[0035] Receive multiple power dispatch strategies sent by multiple edge control modules;

[0036] The dispatching of the renewable energy generation system based on the power dispatch strategy includes:

[0037] Based on multiple power dispatch strategies, a global power dispatch strategy for the renewable energy power generation system is determined;

[0038] Based on the global power dispatching strategy, dispatching instructions are generated;

[0039] The dispatching instruction is sent to the power supply module of the renewable energy generation system so that the power supply module executes the global power dispatching strategy.

[0040] In one embodiment, the number of edge control modules is multiple, and the power dispatching method further includes:

[0041] Resources are allocated to the edge control module with the goal of maximizing the resource utilization rate of the renewable energy power generation system.

[0042] In one embodiment, the power dispatching method further includes:

[0043] Receive the operating data from the edge control module;

[0044] Based on the operational data, the operating status of the edge control module is determined;

[0045] If the operating status of the edge control module is abnormal, take over the operation tasks of the edge control module.

[0046] Seventhly, this application also provides a power dispatching device for a renewable energy power generation system, the system including an interconnected edge control module and a cloud server, the device being applied to the cloud server, including:

[0047] The strategy acquisition module is used to receive the power dispatch strategy sent by the edge control module, wherein the power dispatch strategy is obtained by the edge control module using the steps in any of the above-described power data processing method embodiments;

[0048] The power dispatch module is used to dispatch the renewable energy power generation system based on the power dispatch strategy.

[0049] Eighthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the power dispatching method for renewable energy power generation systems.

[0050] Ninthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in any of the above embodiments of the power dispatching method for renewable energy power generation systems.

[0051] In a tenth aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the power dispatching method for renewable energy power generation systems.

[0052] The power dispatching method, apparatus, computer equipment, computer-readable storage medium, and computer program products for the aforementioned renewable energy power generation system differ from traditional solutions where large amounts of data are directly received and processed by the cloud server. This solution utilizes data transmission between an edge control module and the cloud server. The edge control module acts as a relay station between the cloud server and the renewable energy power generation system. Operational data undergoes preliminary processing by the edge control module, with only necessary information transmitted to the cloud server. This significantly reduces data transmission time and alleviates the burden on the cloud server. Furthermore, the power dispatching strategy generated by the edge control module provides a basis for the cloud server's dispatching, enabling efficient dispatching of the renewable energy power generation system based on this strategy.

[0053] In the eleventh aspect, this application provides a renewable energy power generation system, the system including a power supply module, an edge control module deployed on the side of the power supply module, and a cloud server connected to the edge control module;

[0054] The power supply module is used to send the operating data of the renewable energy power generation system to the edge control module;

[0055] The edge control module is used to acquire environmental data and the operating data of the renewable energy power generation system sent by the power supply module. Taking the operating data and the environmental data as input, it calls a preset power dispatch model to predict the load demand change data and power generation change data of the renewable energy power generation system. Based on the load demand change data and the power generation change data, it determines the power dispatch strategy. The power dispatch model is obtained by training a long short-term memory model based on historical environmental data and historical operating data of the renewable energy power generation system.

[0056] The cloud server is used to receive the power dispatch strategy and, based on the power dispatch strategy, dispatch the renewable energy power generation system.

[0057] Unlike traditional power systems that use cloud servers to analyze and process various power data, the renewable energy power generation system described above employs an edge control module located close to the power supply module. This edge control module pre-stores a power dispatch model trained on a long short-term memory model based on historical environmental and operational data. Using environmental and operational data as input, the model outputs load demand and power generation change data for the renewable energy power generation system. The edge control module then determines the power dispatch strategy, which the cloud server uses to dispatch power to the renewable energy power generation system. This eliminates the need to upload large amounts of data to the cloud server; instead, the edge control module, closer to the data source, processes the operational data, generating dispatch strategies efficiently and accurately. This reduces data transmission time, thereby improving the response speed of the renewable energy power generation system to emergencies and enhancing its dispatch efficiency. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the disclosed drawings without creative effort.

[0059] Figure 1 is an application environment diagram of a power data processing method or power dispatching method for a renewable energy power generation system in one embodiment.

[0060] Figure 2 is a flowchart illustrating a power data processing method in one embodiment;

[0061] Figure 3 is a flowchart illustrating the power data processing method in a detailed embodiment;

[0062] Figure 4 is a flowchart illustrating a power dispatching method in one embodiment;

[0063] Figure 5 is a flowchart illustrating the power dispatching method in another embodiment;

[0064] Figure 6 is a flowchart illustrating the power dispatching method in a detailed embodiment;

[0065] Figure 7 is a structural block diagram of a renewable energy power generation system in one embodiment;

[0066] Figure 8 is a structural block diagram of a power data processing device in one embodiment;

[0067] Figure 9 is a structural block diagram of a power dispatching device in one embodiment;

[0068] Figure 10 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation

[0069] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0071] The power data processing method and power dispatching method for renewable energy power generation systems provided in this application embodiment can be applied to the application environment shown in Figure 1. The power supply module 102 communicates with the edge control module 104 and the cloud server 106 via a network. The data storage system can store the data that the edge control module 104 and the cloud server 106 need to process. The data storage system can be integrated into the edge control module 104 and the cloud server 106, or it can be placed in the cloud or on other network servers.

[0072] Specifically, the power supply module 102 includes, but is not limited to, a power generation module and an energy storage module. Sensors in the power supply module 102 can collect real-time operational data from the power generation module, energy storage module, and other modules—that is, the operational data of the renewable energy power generation system—and upload this data to the edge control module 104. Furthermore, the edge control module 104 can obtain environmental data from weather stations, including but not limited to temperature, light intensity, humidity, and wind speed, either in real-time or periodically / irregularly. Further, the edge control module 104 uses the operational data and environmental data as input, calls a pre-stored power dispatch model, predicts changes in load demand and power generation of the renewable energy power generation system, and then determines a power dispatch strategy based on these data. The power dispatch model is trained on a long short-term memory model using historical environmental data and historical operational data of the renewable energy power generation system, and can be pre-stored in a data storage system.

[0073] Then, the edge control module 104 sends the analyzed power dispatch strategy to the cloud server 106, which then dispatches the renewable energy power generation system.

[0074] In an exemplary embodiment, as shown in FIG2, a power data processing method for a renewable energy power generation system is provided. Taking the application of this method to the edge control module 104 in FIG1 as an example, the method includes the following steps:

[0075] S110 acquires environmental data and operational data of the renewable energy power generation system transmitted from the power supply module.

[0076] Environmental data refers to external environmental factors affecting the efficiency of renewable energy power generation, including but not limited to temperature, humidity, wind speed, wind direction, and solar radiation intensity. Environmental data is a crucial factor influencing the power output of renewable energy power generation systems and can be obtained from weather stations. The power supply module is responsible for controlling and distributing the output power of the renewable energy power generation system. It includes, but is not limited to, the power generation module and energy storage module. It is internally equipped with numerous sensors that can upload operational data to the edge control module. Operational data includes, but is not limited to, the power generation capacity, power output fluctuations, and charging / discharging data of the renewable energy power generation system.

[0077] Taking a photovoltaic power generation system as an example, the edge control module includes, but is not limited to, a photovoltaic inverter (used to convert the DC power generated by the photovoltaic panel into AC power, which can monitor the output voltage, current and power data of the photovoltaic panel in real time, and predict the power generation of the photovoltaic panel through an internally deployed power dispatch model), an energy management module (used to manage and control the energy storage module, which can monitor the charging and discharging status, temperature and health status of the battery, and analyze the charging and discharging behavior of the energy storage module through an internally deployed power dispatch model), and an edge computing gateway (which can collect data from the photovoltaic inverter, energy management module and environmental sensors, and generate power dispatch strategies through an internally deployed power dispatch model), etc.

[0078] The renewable energy power generation system also includes a communication module for communication between various modules. To improve data transmission efficiency, in this embodiment, the communication between the power supply module and the edge control module, as well as between the edge control module and the cloud server, uses a low-latency, high-bandwidth communication protocol.

[0079] S120 takes operational data and environmental data as input, calls a preset power dispatch model, and predicts the load demand change data and power generation change data of the renewable energy power generation system.

[0080] The power dispatch model is trained on a Long Short-Term Memory (LSTM) model based on historical environmental data and historical operational data of renewable energy power generation systems. The LSM model is a recurrent neural network suitable for time-series data, effectively capturing long-term and short-term dependencies in time series data and handling the volatility of renewable energy power generation well. Load demand variation data characterizes the changes in user-side electricity demand over time, influenced by various factors such as seasonality, climate, and user-defined electricity consumption patterns. Power generation variation data characterizes the changes in the power generation capacity of renewable energy power generation systems over a future period.

[0081] Taking the renewable energy power generation system as the photovoltaic power generation system, the training process of the power dispatch model can be as follows: First, collect historical environmental data of the renewable energy power generation system (including but not limited to photovoltaic panel output voltage, current, energy storage module battery capacity, charging and discharging status), historical operating data (including but not limited to ambient temperature, humidity, and light intensity), and timestamps of historical environmental data and historical operating data. Then, perform preliminary processing on the collected raw data, such as data cleaning, handling missing values, removing outliers, and denoising. Then, perform feature engineering on the pre-processed data to extract key features, such as the average light intensity in the past 24 hours, the voltage change rate of the photovoltaic panels in the past hour, the current change rate, and the ambient temperature. Remove redundant or irrelevant features, and normalize or standardize the extracted features to make them fall under the same dimensions, such as scaling the voltage and current data to the range of [0, 1] to obtain the processed data.

[0082] Then, the processed data is divided into training, validation, and test sets, for example, 70% training, 20% validation, and 10% test. The Long Short-Term Memory (LSTM) model is trained using the data in the training set, and the model parameters are adjusted to ensure good performance on the validation set. Early stopping can be used during training to reduce the risk of overfitting. The model performance is evaluated using the data in the validation set, and hyperparameters such as learning rate and batch size are adjusted to optimize model performance. The generalization ability of the model is evaluated using the data in the test set, and evaluation metrics such as mean squared error (MSE) and root mean square error (RMSE) are calculated. This yields a trained power dispatch model. The trained power dispatch model is then converted to a format suitable for edge device operation, and finally deployed to the edge control devices.

[0083] It's worth noting that transfer learning algorithms can be introduced during model training. For example, a deep learning model already trained on a similar photovoltaic (PV) power generation system can be transferred to a new environment for adjustment and application. This allows for rapid prediction of power generation and load demand even in new environments with limited data. For instance, in newly deployed PV power plants, a model trained on other power plants can be directly used for power generation prediction, then fine-tuned using local data, thus accelerating model adaptation and improving prediction accuracy. Furthermore, PV power generation systems are often significantly affected by geographical location and climate conditions, but commonalities may exist between different regions. Transfer learning can integrate PV power generation data from different regions to train a more general power dispatch model. Then, in new regions, only minor adjustments with localized data are needed to obtain good prediction results. For example, a model trained in a region with similar climate conditions can be applied to a new PV power plant for power generation and load demand prediction. Through transfer learning, the training cost of the new model can be effectively reduced.

[0084] In this embodiment, before calling the trained power dispatch model, the edge control module can perform a series of preprocessing operations on the received raw data, such as data clearing, noise reduction, and feature engineering. It can also integrate data from multiple sensors or different data sources to form multimodal environmental data and multimodal operational data, which serve as input data for the power dispatch model. This results in more accurate output data on load demand changes and power generation changes in the renewable energy power generation system. Furthermore, in actual operation, the environment of the renewable energy power generation system changes over time, such as seasonal variations and equipment aging. In such cases, the online update mechanism of transfer learning can be used to continuously fine-tune and optimize the power dispatch model using real-time collected data, improving its accuracy and adaptability. Specifically, during real-time prediction tasks, the power dispatch model continuously learns from new environmental and operational data, constantly optimizing its performance. For example, the edge control module can periodically send new environmental and operational data to the cloud server. The cloud server uses this data to continuously update the power dispatch model and then distributes the updated model back to the edge control module.

[0085] S130 determines the power dispatch strategy based on load demand change data and power generation change data.

[0086] Among them, power dispatch strategy refers to an optimization strategy that dynamically adjusts the operation mode of renewable energy power generation system based on the current load demand change data and power generation change data of renewable energy power generation system in order to meet power demand. The optimization objectives of power dispatch strategy include, but are not limited to, minimizing power fluctuations, improving power generation efficiency, reducing energy waste, and ensuring supply and demand balance.

[0087] In photovoltaic (PV) power generation systems, power dispatch strategies include, but are not limited to, optimization strategies for PV power generation modules and optimization strategies for energy storage modules. For example, optimization strategies for PV power generation modules may involve adjusting the operating point of the PV array through maximum power point tracking to maximize power generation, or adjusting the power generation plan of the PV power generation modules based on power generation change data. Optimization strategies for energy storage modules may involve adjusting the charging and discharging plan based on load demand change data, power generation change data, and battery status data of the energy storage modules. For instance, during peak power demand periods, priority is given to discharging from the energy storage modules to meet demand, while during off-peak power demand periods, the excess power generated by the power generation modules is used to charge the batteries of the energy storage modules.

[0088] For example, the edge scheduling module can determine the optimal power output and charging / discharging timing of a renewable energy power generation system based on load demand change data and power generation change data, using optimization algorithms, including but not limited to linear programming, genetic algorithms, and particle swarm optimization algorithms, to generate a power dispatch strategy. Taking a photovoltaic power generation system as an example, for instance, a maximum power point tracking algorithm can be used to ensure that the photovoltaic panels can achieve maximum power output under various lighting conditions. In addition to determining the power dispatch strategy based on load demand change data and power generation change data, the edge control module can also adjust the power dispatch strategy based on changes in the battery health status, remaining lifespan, grid load, and electricity price of the energy storage module. For example, when the battery health status is poor, the charging voltage and charging current can be reduced to reduce battery loss; during peak grid electricity price periods, the energy storage module can be controlled to discharge, and during off-peak electricity price periods, the energy storage module can be charged.

[0089] The aforementioned power data processing method for renewable energy power generation systems differs from traditional solutions that directly upload the system's operational data to a cloud server. This solution deploys an edge control module within the renewable energy power generation system. This edge control module pre-stores a power dispatch model trained on a Long Short-Term Memory (LSTM) model based on historical environmental and operational data. Instead of directly uploading environmental and operational data to the cloud server, the power dispatch model uses these data as input. The model outputs load demand and power generation variation data. The edge control module then determines the power dispatch strategy based on these data. Because the LSM model can capture complex time-series dependencies, it effectively handles the fluctuations in renewable energy power generation over time, resulting in an accurate dispatch strategy. This eliminates the need to upload large amounts of operational data to a cloud server. Instead, the edge control module, closer to the data source, processes the operational data, efficiently and accurately generating dispatch strategies. This reduces data transmission time, improves the renewable energy power generation system's response speed to emergencies, and enhances the system's dispatch efficiency.

[0090] In an exemplary embodiment, as shown in FIG3, the power data processing method further includes:

[0091] S140: Using the operating data as input, the system calls a preset operating status prediction model to predict the operating status of the renewable energy power generation system.

[0092] The operational status prediction model is trained on a long short-term memory model based on historical operational data and the historical operational status of the renewable energy power generation system. This model can predict the future operational status of the renewable energy power generation system based on the input operational data. It can be used to predict potential faults in the power supply modules of the renewable energy power generation system, changes in power generation efficiency, and other related issues. The operational status of renewable energy includes, but is not limited to, normal operation, low power output, fault, maintenance, and start-up or shutdown states.

[0093] For example, operational data is input into the operational status prediction model. Based on the patterns learned during training, the model predicts the operational status of the renewable energy power generation system over a future period, including but not limited to the operational status of each electrical device within the system. Furthermore, the edge control module can adaptively adjust each electrical device based on the operational status, such as adjusting the blade angle of wind turbines and the angle of photovoltaic panels according to the predicted operational status.

[0094] It should be noted that during the training process of the operational status prediction model, transfer learning algorithms can also be used. For example, an operational status prediction model trained on other renewable energy power generation systems can be transferred and fine-tuned with a small amount of local data to obtain a trained operational status prediction model.

[0095] In this embodiment, the operational status prediction model deployed on the edge control module can assess the future operational status of the renewable energy power generation system based on the operational data sent by the power supply module. Furthermore, since the edge control module is relatively close to the power supply module, it can quickly and efficiently take countermeasures in the event of anomalies in the future operational status of the renewable energy power generation system, thereby improving the stability of the renewable energy power generation system.

[0096] In an exemplary embodiment, as shown in FIG3, the power data processing method further includes:

[0097] S150 assesses the health status of the renewable energy power generation system based on its operating status, and obtains the health status assessment results.

[0098] S160, if the health status assessment results indicate that the renewable energy power generation system is operating abnormally, a preset alarm mechanism is triggered to generate an operation status adjustment command, which is then sent to the power supply module in the renewable energy power generation system to adjust the operation status of the power supply module.

[0099] Health status assessment refers to determining the health status or operating condition of a renewable energy power generation system by analyzing data such as its operational status. This assessment assesses whether the system is within a normal range and whether there are potential faults or performance degradation. The results can characterize whether the renewable energy power generation system is in a normal, sub-healthy, or abnormal state. These results may include, but are not limited to, quantitative indicators or status descriptions of various electrical devices within the renewable energy power generation system, such as health scores, failure probability, and remaining service life.

[0100] For example, taking a photovoltaic power generation system as an example, abnormal operating conditions include, but are not limited to, abnormal power generation modules, abnormal energy storage modules, and abnormal communication modules. Specifically, abnormal power generation modules may experience abnormally low power generation (possibly due to accumulated dust or leaves obstructing the photovoltaic modules, photovoltaic module malfunction, or inverter malfunction). The edge control module can identify the cause of the abnormally low power generation based on the operating data of the power generation modules and send maintenance notifications to maintenance personnel. If the problem is caused by obstructed photovoltaic modules, maintenance personnel will be arranged for on-site inspection and cleaning; if it is due to equipment malfunction, maintenance personnel will be arranged to repair the faulty component. Abnormal fluctuations in the voltage or current of the power generation modules may also occur. In this case, the edge control module will issue an alarm (audio-visual alarm, send an alarm message to the cloud server, etc.) and send an operating status adjustment command to the power generation modules to reduce the output power of the photovoltaic power generation. It can also notify maintenance personnel to inspect and repair the power generation modules.

[0101] An abnormal situation in the energy storage module could be battery overcharging or over-discharging (possibly due to a charging controller malfunction or a battery management system anomaly). In this case, the edge control module sends an operating status adjustment command to the energy storage module to cut off the charging or discharging circuit and protect the battery. Another abnormal situation could be excessively high battery temperature (possibly caused by an internal short circuit, excessively high ambient temperature, or a cooling system malfunction). In this case, the edge control module sends an operating status adjustment command to the energy storage module to activate the backup cooling system or reduce the battery discharge rate to lower the battery temperature. If the temperature cannot be effectively reduced, the edge control module immediately controls the energy storage module to stop the battery charging and discharging operation and notifies technicians for inspection.

[0102] Abnormal conditions of the communication module may include data transmission interruption (possibly due to communication module failure or network connection problems) or data transmission errors (possibly due to signal interference or module failure). The edge control module sends an operation status adjustment command to the communication module to switch to the backup communication channel or attempt to switch networks, and alarms to remind technicians to check the data transmission path, transmission equipment, etc.

[0103] In addition, the abnormal grid status of the public power grid connected to the photovoltaic power generation system may also occur, such as excessively high or low grid voltage (which may be caused by grid instability, sudden changes in system load, etc.) or abnormal grid frequency (caused by grid faults, unstable inverter output, etc.). The edge control module sends operating status adjustment commands to the power generation module and energy storage module to adjust the output power of the power generation module and the charging and discharging power of the energy storage module to help stabilize the grid voltage. If the grid frequency exceeds the safe range, the edge control module controls the power generation module and energy storage module to disconnect from the grid. After the grid returns to normal, they are reconnected. The edge control module also checks the working status of the power equipment in the photovoltaic power generation system. If the abnormal grid status cannot be resolved by the above methods, the edge control module sends an alarm message to the cloud server, which then notifies the grid operator and takes further measures, such as controlling the photovoltaic power generation system to operate off-grid or starting the backup power supply.

[0104] Following the above embodiments, the edge control module assesses the health status of the renewable energy power generation system based on the predicted operating status. If the health assessment indicates that the renewable energy power generation system is in an abnormal state, such as a short remaining service life for some power equipment or a high probability of future failures, the edge control module will trigger a preset alarm mechanism, such as an audible and visual alarm, sending an alarm message to the cloud server, and alerting relevant maintenance personnel through the cloud server. Furthermore, the edge control module will also generate an operating status adjustment command. For example, if the remaining service life of a generator A is less than three months, the operating status adjustment command could be "shut down generator A" and send an alarm message to the cloud server to replace generator A. The cloud server will then forward the alarm message to the terminals of relevant maintenance personnel.

[0105] In this embodiment, by predicting the operating status of the renewable energy power generation system and assessing its health status, the edge control module can automatically generate and execute operating status adjustment instructions, taking swift action without needing to process them through a cloud server, thus improving the response speed and stability of the renewable energy power generation system.

[0106] In an exemplary embodiment, as shown in FIG3, the power data processing method further includes:

[0107] S170, based on operating data, determines the power generation efficiency of the power supply module in the renewable energy power generation system.

[0108] S180, if the power supply module is determined to be in an abnormal working state based on the power generation efficiency, pushes a device maintenance message.

[0109] Among them, power generation efficiency refers to the ratio between the electrical energy that a power generation module can convert under specific conditions and the energy it consumes. It can be expressed as a percentage. The higher the power generation efficiency, the higher the energy conversion rate of the power generation module.

[0110] For example, sensors are deployed within the power supply module to collect operational data from the power generation module. Taking the power generation module in a photovoltaic power generation system as an example, the sensors on the power generation module can collect environmental data such as solar radiation intensity. This environmental data reflects the solar power absorbed by the photovoltaic panels. In addition, the operational data also includes the power generation capacity of the power generation module. Based on the solar power absorbed by the power generation module and its power generation capacity, the edge control module can determine the power generation efficiency of the power supply module.

[0111] Furthermore, when the power supply module's generation efficiency is significantly lower than the normal range, it can be considered that the power supply module is in an abnormal operating state, indicating that the power supply module may have a fault, performance degradation, or maintenance requirements. It should be noted that the normal range of the power supply module's generation efficiency can be dynamically adjusted, for example, based on different seasons, weather conditions, and equipment usage time. To improve response speed, the edge control module can directly or indirectly push equipment maintenance messages to maintenance personnel via a cloud server. These equipment maintenance messages include, but are not limited to, the power supply module's operating status, anomaly level, and maintenance measures. In addition, the edge control module also includes a storage unit that can store operational data and environmental data acquired by the edge control module, as well as the operating status, power dispatch strategy, health status, and maintenance records of the renewable energy power generation system obtained based on the operational and environmental data analysis, and communication records between the edge control module and the cloud server.

[0112] In this embodiment, the power generation efficiency of the power supply module in the renewable energy power generation system can be quickly determined based on the operating data of the renewable energy power generation system. Furthermore, when the power supply module is in an abnormal working state, the edge control module can automatically push equipment maintenance messages, which significantly improves the response speed of the renewable energy power generation system and reduces the failure probability of the renewable energy power generation system.

[0113] To provide a clearer explanation of the power data processing method for the renewable energy power generation system proposed in this scheme, the following detailed explanation will be provided in conjunction with Figure 3 and a specific embodiment, which includes the following steps:

[0114] S110 acquires environmental data and operational data of the renewable energy power generation system transmitted from the power supply module.

[0115] S120 takes operational data and environmental data as input, calls a preset power dispatch model, and predicts the load demand change data and power generation change data of the renewable energy power generation system.

[0116] S130 determines the power dispatch strategy based on load demand change data and power generation change data.

[0117] S140: Using the operating data as input, the system calls a preset operating status prediction model to predict the operating status of the renewable energy power generation system.

[0118] S150 assesses the health status of the renewable energy power generation system based on its operating status, and obtains the health status assessment results.

[0119] S160, if the health status assessment results indicate that the renewable energy power generation system is operating abnormally, a preset alarm mechanism is triggered to generate an operation status adjustment command, which is then sent to the power supply module S170 in the renewable energy power generation system. Based on the operation data, the power generation efficiency of the power supply module in the renewable energy power generation system is determined.

[0120] S180, if the power supply module is determined to be in an abnormal working state based on the power generation efficiency, pushes a device maintenance message.

[0121] In one embodiment, a power dispatching method for a renewable energy power generation system is provided. The system includes interconnected edge control modules and a cloud server. The method is applied to the cloud server, as shown in Figure 4, and includes the following steps:

[0122] S210 receives the power dispatching strategy sent by the edge control module.

[0123] S220 is a system that dispatches renewable energy power generation systems based on power dispatch strategies.

[0124] The power dispatch strategy is obtained by the edge control module using the steps in any of the above embodiments of the power data processing method for renewable energy power generation systems. The cloud server is a central computing unit that processes and stores large amounts of data, capable of unified management of data such as the power dispatch strategy uploaded by the edge control module, and responsible for global dispatch optimization and strategy execution.

[0125] Specifically, the power dispatch strategies generated through the aforementioned power data methods include, but are not limited to: adjusting the speed of wind turbines, adjusting the tilt angle of solar panels, coordinating the output plans of different power generation devices, and formulating charging and discharging plans for energy storage modules. The edge control module and the cloud server can communicate via a network. To improve timeliness, lightweight and efficient protocols should be selected as much as possible, such as Wi-Fi, BLE (Bluetooth Low Energy), Zigbee, and LoRa (Long Range radio).

[0126] Furthermore, the cloud server acts as a management and control center, responsible for the overall coordination, remote monitoring, and management of the system. After receiving the power dispatch strategy, the cloud server can further adjust the strategy appropriately. For example, based on real-time changes in environmental data (such as wind speed and solar radiation intensity), and combined with deep learning technology, the cloud server can optimize and update the power dispatch strategy in real time. Then, based on the optimized power dispatch strategy, the cloud server generates a series of dispatch instructions and sends them to various power devices in the renewable energy power generation system, thereby coordinating the operating status of different power devices to meet the current power demand of the system.

[0127] In this embodiment, through data transmission between the edge control module and the cloud server, the edge control module acts as a relay station between the cloud server and the renewable energy power generation system. Operational data undergoes preliminary processing by the edge control module, with only necessary information transmitted to the cloud server, thus reducing the cloud server's workload. Furthermore, the power dispatching strategy generated by the edge control module can provide a basis for cloud server scheduling. This approach reduces data transmission over the network, thereby saving bandwidth and reducing network congestion, and also lowers the risk of data leakage.

[0128] In one exemplary embodiment, the number of edge control modules is multiple, as shown in FIG5, S210 includes:

[0129] S211 receives multiple power dispatch strategies sent by multiple edge control modules.

[0130] S220 includes:

[0131] S221, based on multiple power dispatch strategies, determines the global power dispatch strategy for the renewable energy power generation system.

[0132] S222 generates dispatch instructions based on the global power dispatch strategy.

[0133] S223, send a dispatch instruction to the power supply module of the renewable energy power generation system so that the power supply module can execute the global power dispatch strategy.

[0134] In complex renewable energy power generation systems, there may be multiple distributed edge control modules. These modules are deployed in different geographical areas or different types of power generation equipment (such as wind power and photovoltaic power generation) to collect and process local environmental and operational data. Each edge control module determines a different power dispatch strategy based on its local environmental and operational data. Dispatch instructions are specific execution commands generated by the cloud server based on the global dispatch strategy, used to instruct each power supply module in the renewable energy power generation system to adjust its operating status or output power, etc.

[0135] For example, when the area covered by a renewable energy power generation system is large, it may include multiple energy sources (wind power, solar power, etc.). To achieve global scheduling of the renewable energy power generation system, independent edge control modules need to be deployed on each distributed power station or subsystem. Each edge control module generates a local power dispatch strategy based on local environmental and operational data, and different edge control modules maintain communication connections with the cloud server. It should be noted that, in addition to each edge control module communicating separately with the cloud server, different edge control modules can also communicate and share data appropriately, thereby adjusting the local power dispatch strategy based on data from other edge control modules, and achieving coordinated optimization of the renewable energy power generation system.

[0136] The cloud server receives multiple power dispatch strategies from various edge control modules, then aggregates and optimizes these strategies. For example, it considers the power generation status and efficiency of each power plant or subsystem while optimizing the overall power generation efficiency and resource utilization of the renewable energy power generation system. For instance, since different power dispatch strategies may have different optimization objectives—some focusing on power generation efficiency, others on optimizing the lifespan of power equipment—after receiving multiple power dispatch strategies, the cloud server first performs a fusion analysis. Based on the overall power supply and demand situation of the renewable energy power generation system, it assigns weights to each power dispatch strategy to determine the final global dispatch strategy. For example, in a renewable energy power generation system, some areas may have high load demand but insufficient local power generation capacity. The cloud server can use dispatch strategies to allocate surplus power from other areas to the high-load areas. It should be noted that the global dispatch strategy is not static. The cloud server can directly obtain high-priority operational data from the power supply modules of each power plant or subsystem and dynamically adjust the global dispatch strategy based on this operational data and real-time environmental data.

[0137] Finally, the cloud server generates scheduling instructions based on the global scheduling strategy and sends them to each power supply module. These instructions include global and local scheduling instructions. Global scheduling instructions involve optimizing the power generation of the entire renewable energy generation system, aiming to achieve global load balancing and optimal resource allocation. This includes, for example, coordinating power supply between different power plants or subsystems to address global load fluctuations. Local scheduling instructions, on the other hand, are specific to the power supply module of each power plant or subsystem, aiming to improve the power generation efficiency of a single power plant or subsystem. This includes, for example, fine-tuning the operating parameters of the power supply module.

[0138] After receiving dispatch instructions, the power supply modules will adjust their operating status accordingly. For example, photovoltaic inverters will adjust their output power according to dispatch instructions, and wind turbines will adjust their speed. In addition, the edge control module can monitor the execution of instructions from each power supply module in real time, and will promptly send alarm messages to the cloud server when there are deviations in instruction execution or other abnormalities.

[0139] In this embodiment, considering that there may be multiple edge control modules and that the computing resources of the edge control modules are prioritized, the focus is usually only on local optimization issues. Cloud servers, however, have a broader perspective and can make more complex optimization decisions based on the global state of the renewable energy power generation system. For example, they can perform resource scheduling and allocation among multiple substations to improve the overall power generation efficiency and resource utilization of the renewable energy power generation system.

[0140] In one embodiment, the number of edge control modules is multiple, as shown in Figure 5. The power dispatching method further includes:

[0141] S230 allocates resources to the edge control module with the goal of maximizing the resource utilization of the renewable energy power generation system.

[0142] Resource utilization rate refers to the degree to which the resources (such as storage resources, computing resources, communication resources, etc.) of each device in a renewable energy power generation system are utilized in actual operation. Maximizing resource utilization rate means optimizing resource allocation so that each component of the renewable energy power generation system can work efficiently and reduce resource waste.

[0143] For example, in a renewable energy power generation system, each edge control module is responsible for handling local power data processing tasks. To maximize resource utilization, the renewable energy power generation system can dynamically adjust resource allocation based on the load of different edge control modules, including but not limited to storage resources, computing resources, and communication resources. For instance, edge control modules with higher loads can be allocated more computing resources to enable them to complete data processing tasks in a timely manner, while edge control modules with lower loads can be allocated fewer resources to save on the overall resource consumption of the renewable energy power generation system.

[0144] Furthermore, storage, computing, and communication resources can be allocated to edge control modules based on the type and operating status of the power supply modules near them. For example, wind power and photovoltaic power generation require faster response and dispatch due to weather factors; therefore, edge control modules corresponding to wind turbines and photovoltaic generators will be allocated more computing, storage, and communication resources.

[0145] In addition, adaptive algorithms can be introduced into cloud servers to achieve resource allocation. For example, by combining deep learning algorithms, resources can be adaptively allocated to the edge control module according to its operating status, thereby continuously optimizing the resource allocation scheme and maximizing resource utilization.

[0146] In this embodiment, by allocating resources to different edge control modules, the efficiency of the edge control modules in completing data processing tasks can be improved, thereby increasing the overall operating efficiency of the renewable energy power generation system. Especially under conditions of unbalanced system load, this can effectively reduce waste of overall system computing, storage, and communication resources.

[0147] In an exemplary embodiment, as shown in FIG6, the power dispatching method further includes:

[0148] S240 receives the operating data from the edge control module.

[0149] S250 determines the operating status of the edge control module based on operational data.

[0150] S260 takes over the operation of the edge control module when the operating status of the edge control module is abnormal.

[0151] For example, the cloud server in the renewable energy power generation system can receive the operating data of the edge control module in real time and determine the operating status of the edge control module based on the operating data. For example, it can determine whether the device temperature, response time and other indicators of the edge control module are outside the normal range. If it is found that the device temperature of the edge control module is too high or the response time is too long, it is determined that the edge control module is in an abnormal operating state.

[0152] When the cloud server detects an anomaly in the operating status of an edge control module, it immediately takes over the module's tasks and can also activate backup computing resources. This allows the cloud server to quickly replace the edge control module, improving the stability of the renewable energy power generation system. Furthermore, during the cloud server's takeover, it can send maintenance messages to relevant maintenance personnel, including but not limited to the anomaly identifier, the time of the anomaly, and potential causes. Once the edge control module recovers, the cloud server can reassign its operating tasks.

[0153] In this embodiment, by introducing an automatic takeover mechanism, the cloud server can react quickly when the edge control module malfunctions, enabling the renewable energy power generation system to have better fault tolerance and improve the stability and reliability of the renewable energy power generation system.

[0154] To provide a clearer explanation of the power dispatching method for renewable energy power generation systems proposed in this scheme, the following detailed explanation will be provided in conjunction with Figure 6 and a specific embodiment, which includes the following steps:

[0155] S211 receives multiple power dispatch strategies sent by multiple edge control modules.

[0156] S221, based on multiple power dispatch strategies, determines the global power dispatch strategy for the renewable energy power generation system.

[0157] S222 generates dispatch instructions based on the global power dispatch strategy.

[0158] S223, send a dispatch instruction to the power supply module of the renewable energy power generation system so that the power supply module can execute the global power dispatch strategy.

[0159] S230 allocates resources to the edge control module with the goal of maximizing the resource utilization of the renewable energy power generation system.

[0160] S240 receives the operating data from the edge control module.

[0161] S250 determines the operating status of the edge control module based on operational data.

[0162] S260 takes over the operation of the edge control module when the operating status of the edge control module is abnormal.

[0163] In an exemplary embodiment, as shown in FIG7, a renewable energy power generation system 700 is provided, including a power supply module 710, an edge control module 720 deployed on the side of the power supply module 710, and a cloud server 730 connected to the edge control module 720.

[0164] The power supply module 710 is used to send operating data of the renewable energy power generation system to the edge control module.

[0165] The edge control module 720 is used to acquire environmental data and the operation data of the renewable energy power generation system sent by the power supply module 710. Taking the operation data and environmental data as input, it calls the preset power dispatch model to predict the load demand change data and power generation change data of the renewable energy power generation system. Based on the load demand change data and power generation change data, it determines the power dispatch strategy. The power dispatch model is trained on the long short time memory model based on historical environmental data and historical operation data of the renewable energy power generation system.

[0166] The cloud server 730 is used to receive power dispatch strategies and, based on these strategies, to dispatch renewable energy power generation systems.

[0167] It should be noted that the power supply module 710, edge control module 720, and cloud server 730 in this system embodiment have been described in detail in the above method embodiment, and will not be repeated here.

[0168] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0169] Based on the same inventive concept, this application also provides a power data processing device or power dispatching device for a renewable energy power generation system to implement the power data processing method or power dispatching method of the renewable energy power generation system described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the power data processing device or power dispatching device for a renewable energy power generation system provided below can be found in the limitations of the power data processing method or power dispatching method for renewable energy power generation systems described above, and will not be repeated here.

[0170] In an exemplary embodiment, as shown in FIG8, a power data processing device 800 (hereinafter referred to as power data processing device 800) for a renewable energy power generation system is provided. The system includes an edge control module deployed on the power supply module side; the device is applied to the edge control module. The device includes: a data acquisition module 810, a model prediction module 820, and a strategy determination module 830, wherein:

[0171] The data acquisition module 810 is used to acquire environmental data and operating data of the renewable energy power generation system sent by the power supply module.

[0172] The model prediction module 820 is used to take operating data and environmental data as input, call the preset power dispatch model, and predict the load demand change data and power generation change data of the renewable energy power generation system.

[0173] The strategy determination module 830 is used to determine the power dispatch strategy based on load demand change data and power generation change data.

[0174] Among them, the power dispatch model is trained on the long short-term memory model based on historical environmental data and historical operating data of renewable energy power generation systems.

[0175] In an exemplary embodiment, the power data processing device 800 is further configured to take the operating data as input, call a preset operating state prediction model, and predict the operating state of the renewable energy power generation system. The operating state prediction model is obtained by training a long short-term memory model based on historical operating data and the historical operating state of the renewable energy power generation system.

[0176] In an exemplary embodiment, the power data processing device 800 is further configured to assess the health status of the renewable energy power generation system based on the operating status, obtain a health status assessment result, and trigger a preset alarm mechanism when the health status assessment result indicates that the renewable energy power generation system is operating abnormally, generate an operating status adjustment instruction, and send the operating status adjustment instruction to the power supply module in the renewable energy power generation system to adjust the operating status of the power supply module.

[0177] In an exemplary embodiment, the power data processing device 800 is further configured to determine the power generation efficiency of the power supply module in the renewable energy power generation system based on operating data, and push equipment maintenance messages when it is determined that the power supply module is in an abnormal working state based on the power generation efficiency.

[0178] In an exemplary embodiment, as shown in FIG9, a power dispatching device 900 (hereinafter referred to as power dispatching device 900) for a renewable energy power generation system is provided. The system includes an edge control module deployed on the power supply module side; the device is applied to the edge control module. The device includes: a strategy acquisition module 910 and a power dispatching module 920, wherein:

[0179] The strategy acquisition module 910 is used to receive the power dispatch strategy sent by the edge control module. The power dispatch strategy is obtained by the edge control module using the steps in any of the above power data processing method embodiments.

[0180] The power dispatch module 920 is used to dispatch renewable energy power generation systems based on power dispatch strategies.

[0181] In an exemplary embodiment, there are multiple edge control modules. The power dispatching device 900 is further configured to receive multiple power dispatching strategies sent by the multiple edge control modules, determine a global power dispatching strategy for the renewable energy power generation system based on the multiple power dispatching strategies, generate dispatching instructions based on the global power dispatching strategy, and send the dispatching instructions to the power supply module of the renewable energy power generation system so that the power supply module executes the global power dispatching strategy.

[0182] In one exemplary embodiment, there are multiple edge control modules, and the power dispatching device 900 is also used to allocate resources to the edge control modules with the goal of maximizing the resource utilization of the renewable energy power generation system.

[0183] In an exemplary embodiment, the power dispatching device 900 is further configured to receive operating data from the edge control module, determine the operating status of the edge control module based on the operating data, and take over the operating tasks of the edge control module if the operating status of the edge control module is abnormal.

[0184] Each module in the aforementioned power data processing device or power dispatching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0185] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 10. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores environmental data and other data. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a power data processing method or a power dispatching method.

[0186] Those skilled in the art will understand that the structure shown in Figure 10 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0187] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps in any of the above embodiments of the power data processing method for renewable energy power generation systems.

[0188] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in any of the above embodiments of the power data processing method for renewable energy power generation systems.

[0189] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the power data processing method for renewable energy power generation systems.

[0190] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps in any of the above embodiments of the power dispatching method for renewable energy generation systems.

[0191] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in any of the above embodiments of the power dispatching method for renewable energy generation systems.

[0192] In one exemplary embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the power dispatching method for renewable energy generation systems.

[0193] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0194] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0195] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0196] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for processing power data in a renewable energy power generation system, characterized in that, The system includes an edge control module deployed on the power supply module side; The method is applied to the edge control module, including: Acquire environmental data, as well as the operating data of the renewable energy power generation system transmitted by the power supply module; Using the operational data and environmental data as input, a preset power dispatch model is invoked to predict the load demand change data and power generation change data of the renewable energy power generation system. Based on the load demand change data and the power generation change data, a power dispatch strategy is determined; The power dispatch model is trained on a long short-term memory model based on historical environmental data and historical operating data of the renewable energy power generation system.

2. The method according to claim 1, characterized in that, The power data processing method further includes: Using the aforementioned operating data as input, a preset operating state prediction model is invoked to predict the operating state of the renewable energy power generation system. The operational status prediction model is obtained by training a long short-term memory model based on historical operational data and the historical operational status of the renewable energy power generation system.

3. The method according to claim 2, characterized in that, The method further includes: Based on the operating status, the health status of the renewable energy power generation system is assessed to obtain the health status assessment result; If the health status assessment results indicate that the renewable energy power generation system is operating abnormally, a preset alarm mechanism is triggered to generate an operation status adjustment command. The operation status adjustment command is then sent to the power supply module in the renewable energy power generation system to adjust the operation status of the power supply module.

4. The method according to claim 1, characterized in that, The training process of the power dispatch model includes: Acquire historical environmental data, historical operational data, timestamps of the historical environmental data, and timestamps of the historical operational data of the renewable energy power generation system; Data cleaning is performed on the historical environmental data, the historical operational data, the timestamps of the historical environmental data, and the timestamps of the historical operational data. The cleaned historical environment data, historical operation data, timestamps of the historical environment data, and timestamps of the historical operation data are divided into training set, validation set, and test set. Based on the training set, the validation set, and the test set, the long short-term memory model is trained to obtain the power dispatch model.

5. The method according to claim 1, characterized in that, The environmental data includes at least one of temperature, humidity, wind speed, wind direction, and solar radiation intensity.

6. The method according to claim 1, characterized in that, The operational data includes at least one of the following: power generation, power output fluctuation, and charge / discharge data of the renewable energy power generation system.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the operational data, the power generation efficiency of the power supply module in the renewable energy power generation system is determined; If the power supply module is determined to be in an abnormal working state based on the power generation efficiency, a device maintenance message is pushed.

8. A power dispatching method for a renewable energy power generation system, characterized in that, The system includes interconnected edge control modules and cloud servers, and the method is applied to the cloud server, including: The edge control module receives a power dispatching strategy, which is obtained by the edge control module using the power data processing method described in any one of claims 1 to 7. Based on the power dispatch strategy, the renewable energy power generation system is dispatched.

9. The power dispatching method according to claim 8, characterized in that, The number of edge control modules is multiple, and the step of receiving the power dispatching strategy sent by the edge control modules includes: Receive multiple power dispatch strategies sent by multiple edge control modules; The dispatching of the renewable energy generation system based on the power dispatch strategy includes: Based on multiple power dispatch strategies, a global power dispatch strategy for the renewable energy power generation system is determined; Based on the global power dispatching strategy, dispatching instructions are generated; The dispatching instruction is sent to the power supply module of the renewable energy generation system so that the power supply module executes the global power dispatching strategy.

10. The power dispatching method according to claim 8, characterized in that, The number of edge control modules is multiple, and the power dispatching method further includes: Resources are allocated to the edge control module with the goal of maximizing the resource utilization rate of the renewable energy power generation system.

11. The power dispatching method according to any one of claims 8 to 10, characterized in that, The power dispatching method also includes: Receive the operating data from the edge control module; Based on the operational data, the operating status of the edge control module is determined; If the operating status of the edge control module is abnormal, take over the operation tasks of the edge control module.

12. The power dispatching method according to claim 11, characterized in that, The operating status of the edge control module includes one of the following: normal operation status, low power output status, fault status, maintenance status, start-up status, and stop status.

13. The power dispatching method according to claim 11, characterized in that, The method further includes: Based on the aforementioned operating status, adjustments are made to each power device.

14. A renewable energy power generation system, characterized in that, The system includes a power supply module, an edge control module deployed on the side of the power supply module, and a cloud server connected to the edge control module; The power supply module is used to send the operating data of the renewable energy power generation system to the edge control module; The edge control module is used to acquire environmental data and the operating data of the renewable energy power generation system sent by the power supply module. Taking the operating data and the environmental data as input, it calls a preset power dispatch model to predict the load demand change data and power generation change data of the renewable energy power generation system. Based on the load demand change data and the power generation change data, it determines the power dispatch strategy. The power dispatch model is obtained by training a long short-term memory model based on historical environmental data and historical operating data of the renewable energy power generation system. The cloud server is used to receive the power dispatch strategy and, based on the power dispatch strategy, dispatch the renewable energy power generation system.

15. A power data processing device for a renewable energy power generation system, characterized in that, The system includes an edge control module deployed on the power supply module side; The device is applied to the edge control module, and the device includes: The data acquisition module is used to acquire environmental data and the operating data of the renewable energy power generation system sent by the power supply module; The model prediction module is used to take the operating data and the environmental data as input, call the preset power dispatch model, and predict the load demand change data and power generation change data of the renewable energy power generation system. The strategy determination module is used to determine the power dispatch strategy based on the load demand change data and the power generation change data; The power dispatch model is trained on a long short-term memory model based on historical environmental data and historical operating data of the renewable energy power generation system.

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