Data processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a data processing method and device, a storage medium and electronic equipment, and relates to the technical field of light storage source load interaction. The method comprises the following steps: acquiring a historical data set and weather information in a preset time period; inputting the historical data set and the weather information in a preset time period into a target prediction model for processing, and predicting to obtain a target data set; and adjusting operation parameters of the photovoltaic power generation system and the energy storage system according to the target data set. Through the method and the device, the problem that the stability of a power grid is relatively low due to the fact that the operation parameters of the photovoltaic power generation system and the energy storage system are difficult to accurately adjust in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of optical storage source-load interaction technology, and in particular to a data processing method and device, a storage medium and an electronic device. Background Art

[0002] The research in the field of photovoltaic-storage-source-load interaction aims to achieve efficient integration of solar photovoltaic power generation and power storage systems through the development and application of an integrated photovoltaic-storage-source-load interaction system, so as to optimize energy allocation, improve power generation efficiency and grid stability. This interactive method can not only effectively alleviate the impact of the intermittent and undispatched nature of photovoltaic power generation on the power grid, but also promote the widespread access and in-depth utilization of renewable energy, and provide key technical support for building a clean, safe and efficient modern energy system. It is also of great significance in responding to climate change, promoting energy transformation, and ensuring energy security.

[0003] However, the source-load interaction methods and systems of photovoltaic and energy storage integration in related technologies often do not consider the intelligent adjustment process, and usually directly adjust the power of photovoltaic and energy storage, and cannot be adjusted in real time according to the operating status. Therefore, it is difficult to accurately adjust the operating parameters of the photovoltaic power generation system and the energy storage system in related technologies, resulting in low stability of the power grid.

[0004] Currently, no effective solution has been proposed to address the problem that it is difficult to accurately adjust the operating parameters of photovoltaic power generation systems and energy storage systems in related technologies, resulting in low stability of the power grid. Summary of the invention

[0005] The main purpose of this application is to provide a data processing method and device, a storage medium and an electronic device to solve the problem in the related art that it is difficult to accurately adjust the operating parameters of the photovoltaic power generation system and the energy storage system, resulting in low stability of the power grid.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a data processing method is provided. The method comprises: obtaining a historical data set and weather information within a preset time period, wherein the historical data set at least includes: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharge data of the energy storage system and historical load demand data of the target power grid, and the photovoltaic power generation system and the energy storage system supply power to the target power grid; inputting the historical data set and the weather information within the preset time period into the target prediction model for processing, and predicting to obtain a target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period and the target discharge data of the energy storage system within the preset time period; adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set.

[0007] Furthermore, according to the target data set, adjusting the operating parameters of the photovoltaic power generation system and the energy storage system includes: determining a plurality of buses corresponding to different voltage levels in the target power grid, and calculating the change data of the charging power corresponding to each bus according to the target data set; adjusting the charging power corresponding to each bus according to the change data of the charging power corresponding to each bus to obtain the adjusted charging power corresponding to each bus; and adjusting the operating parameters of the photovoltaic power generation system and the energy storage system based on the adjusted charging power corresponding to each bus.

[0008] Furthermore, according to the target data set, calculating the change data of the charging power corresponding to each bus includes: calculating the reverse feed amount corresponding to each voltage level according to the target data set, wherein the reverse feed amount is used to represent the electric quantity information provided by the photovoltaic power generation system and the energy storage system to the target power grid, and each voltage level is the voltage level corresponding to each bus; calculating the sum of the maximum electric energy capacity of the target power grid at each voltage level, and obtaining the target value at each voltage level; calculating the ratio between the reverse feed amount corresponding to each voltage level and the target value at each voltage level, and obtaining the target ratio corresponding to each voltage level; calculating the change data of the charging power corresponding to each bus according to the target ratio corresponding to each voltage level.

[0009] Furthermore, the historical data set and the weather information within the preset time period are input into a target prediction model for processing, and the target data set is predicted to be obtained, including: inputting the historical data set and the weather information within the preset time period into the target prediction model, and using a time series analysis method to analyze and process the data in the historical data set and the weather information within the preset time period to obtain an analysis result, wherein the target prediction model is a model constructed based on a long short-term memory network; based on the analysis result, the target data set is predicted to be obtained.

[0010] Furthermore, after adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, the method also includes: obtaining actual power generation data of the photovoltaic power generation system within the preset time period, actual charging data of the energy storage system within the preset time period, and actual discharging data of the energy storage system within the preset time period to obtain an actual data set; based on the actual data set and the target data set, determining whether the target system is in an abnormal state, wherein the target system is a system based on the photovoltaic power generation system and the energy storage system.

[0011] Further, judging whether the target system is in an abnormal state based on the actual data set and the target data set includes: calculating a first difference between the actual power generation data of the photovoltaic power generation system in the preset time period and the target power generation data of the photovoltaic power generation system in the preset time period based on the actual data set and the target data set, and calculating a second difference between the actual charge data of the energy storage system in the preset time period and the target charge data of the energy storage system in the preset time period, and calculating a third difference between the actual discharge data of the energy storage system in the preset time period and the target discharge data of the energy storage system in the preset time period; judging whether the first difference is greater than a first preset threshold, judging whether the second difference is greater than a second preset threshold, and judging whether the third difference is greater than a third preset threshold; if the first difference is not greater than the first preset threshold, and the second difference is not greater than the second preset threshold, and the third difference is not greater than the third preset threshold, then determining that the target system is in a normal state; if the first difference is greater than the first preset threshold, and / or the second difference is greater than the second preset threshold, and / or the third difference is greater than the third preset threshold, then determining that the target system is in an abnormal state.

[0012] Furthermore, after determining whether the target system is in an abnormal state based on the actual data set and the target data set, the method also includes: if the target system is in a normal state, prohibiting adjustment of the operating parameters of the photovoltaic power generation system and the energy storage system; if the target system is in an abnormal state, determining an adjustment strategy based on the actual data set, the weather information at the current moment and the operating status information of the target system; and adjusting the operating parameters of the photovoltaic power generation system and the energy storage system again based on the adjustment strategy.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a data processing device is provided. The device comprises: a first acquisition unit, used to acquire a historical data set and weather information within a preset time period, wherein the historical data set at least includes: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharge data of the energy storage system and historical load demand data of the target power grid, and the photovoltaic power generation system and the energy storage system supply power to the target power grid; a first prediction unit, used to input the historical data set and the weather information within the preset time period into a target prediction model for processing, and predict to obtain a target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period and the target discharge data of the energy storage system within the preset time period; a first adjustment unit, used to adjust the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set.

[0014] Furthermore, the first adjustment unit includes: a first processing module, used to determine the multiple buses corresponding to different voltage levels in the target power grid, and calculate the change data of the charging power corresponding to each bus according to the target data set; a second processing module, used to adjust the charging power corresponding to each bus according to the change data of the charging power corresponding to each bus, and obtain the adjusted charging power corresponding to each bus; the first adjustment module is used to adjust the operating parameters of the photovoltaic power generation system and the energy storage system based on the adjusted charging power corresponding to each bus.

[0015] Furthermore, the first processing module includes: a first calculation submodule, used to calculate the reverse transmission amount corresponding to each voltage level according to the target data set, wherein the reverse transmission amount is used to represent the power information provided by the photovoltaic power generation system and the energy storage system to the target power grid, and each voltage level is the voltage level corresponding to each bus; a second calculation submodule, used to calculate the sum of the maximum power capacity of the target power grid at each voltage level, and obtain the target value at each voltage level; a third calculation submodule, used to calculate the ratio between the reverse transmission amount corresponding to each voltage level and the target value at each voltage level, and obtain the target ratio corresponding to each voltage level; a fourth calculation submodule, used to calculate the change data of the charging power corresponding to each bus according to the target ratio corresponding to each voltage level.

[0016] Furthermore, the first prediction unit includes: a third processing module, used to input the historical data set and the weather information within the preset time period into the target prediction model, and use a time series analysis method to analyze and process the data in the historical data set and the weather information within the preset time period to obtain an analysis result, wherein the target prediction model is a model constructed based on a long short-term memory network; a first prediction module, used to predict the target data set based on the analysis result.

[0017] Furthermore, the device also includes: a second acquisition unit, used to obtain the actual power generation data of the photovoltaic power generation system within the preset time period, the actual charging data of the energy storage system within the preset time period, and the actual discharging data of the energy storage system within the preset time period after adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, so as to obtain an actual data set; a first judgment unit, used to judge whether the target system is in an abnormal state based on the actual data set and the target data set, wherein the target system is a system composed of the photovoltaic power generation system and the energy storage system.

[0018] Further, the first judgment unit includes: a first calculation module, which is used to calculate the first difference between the actual power generation data of the photovoltaic power generation system in the preset time period and the target power generation data of the photovoltaic power generation system in the preset time period according to the actual data set and the target data set, and calculate the second difference between the actual charge data of the energy storage system in the preset time period and the target charge data of the energy storage system in the preset time period, and calculate the third difference between the actual discharge data of the energy storage system in the preset time period and the target discharge data of the energy storage system in the preset time period; the first judgment module A block is used to determine whether the first difference is greater than a first preset threshold, and to determine whether the second difference is greater than a second preset threshold, and to determine whether the third difference is greater than a third preset threshold; a first determination module is used to determine that the target system is in a normal state if the first difference is not greater than the first preset threshold, and the second difference is not greater than the second preset threshold, and the third difference is not greater than the third preset threshold; a second determination module is used to determine that the target system is in an abnormal state if the first difference is greater than the first preset threshold, and / or the second difference is greater than the second preset threshold, and / or the third difference is greater than the third preset threshold.

[0019] Furthermore, the device also includes: a first processing unit, which is used to determine whether the target system is in an abnormal state based on the actual data set and the target data set, and if the target system is in a normal state, prohibit adjustment of the operating parameters of the photovoltaic power generation system and the energy storage system; a first determination unit, which is used to determine an adjustment strategy based on the actual data set, weather information at the current moment and operating status information of the target system if the target system is in an abnormal state; and a second adjustment unit, which is used to readjust the operating parameters of the photovoltaic power generation system and the energy storage system based on the adjustment strategy.

[0020] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a program, wherein the program executes any one of the above-mentioned data processing methods.

[0021] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is provided, which includes one or more processors and a memory, and the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the data processing methods described above.

[0022] Through the present application, the following steps are adopted: obtaining a historical data set and weather information within a preset time period, wherein the historical data set includes at least: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharging data of the energy storage system and historical load demand data of the target power grid, wherein the photovoltaic power generation system and the energy storage system supply power to the target power grid; inputting the historical data set and the weather information within the preset time period into a target prediction model for processing, and predicting a target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period and the target discharging data of the energy storage system within the preset time period; adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, thereby solving the problem in the related art that it is difficult to accurately adjust the operating parameters of the photovoltaic power generation system and the energy storage system, resulting in low stability of the power grid. By collecting the real-time power generation data of the photovoltaic power generation system, the real-time charging and discharging status of the energy storage system and the real-time load demand of the power grid, and calculating the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system based on the real-time power generation data, charging and discharging status and load demand, the operating parameters of the photovoltaic power generation system and the energy storage system can be accurately adjusted based on the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system, thereby reducing the impact of photovoltaic power generation on the power grid, thereby achieving the effect of improving the stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 is a flow chart of a data processing method provided according to an embodiment of the present application;

[0025] Figure 2 It is a flow chart of a photovoltaic-storage integrated source-load interaction method provided according to an embodiment of the present application;

[0026] Figure 3 This is a flow chart of collecting the power generation of the photovoltaic power generation system, the charging and discharging status of the energy storage system and the real-time load demand of the power grid in real time through sensors and intelligent meters in this embodiment;

[0027] Figure 4 This is a flow chart of obtaining the optimal photovoltaic power generation and the optimal energy storage charge and discharge according to historical data and weather forecast in this embodiment;

[0028] Figure 5It is a flowchart of adjusting the charging power variables corresponding to 10kV, 35kV, 110kV and 220kV buses according to the optimal output and charging and discharging strategies in this embodiment, which is sent to the photovoltaic power generation system and the energy storage system through the automatic control system;

[0029] Figure 6 It is a flowchart of real-time monitoring of photovoltaic output, energy storage status and grid load in this embodiment, comparing them according to a predetermined strategy, and finding any deviation or abnormality;

[0030] Figure 7 This is a flow chart of the embodiment in which, when a deviation or abnormality is detected, the central processing unit will recalculate the strategy based on the new data and quickly adjust the operating parameters of the energy storage system;

[0031] Figure 8 is a flow chart of the setting user interface in this embodiment, in which the operator monitors the system status online and manually adjusts the strategy;

[0032] Fig. 9 is a schematic diagram of a photovoltaic-storage integrated source-load interactive system provided according to an embodiment of the present application;

[0033] Fig.10 is a schematic diagram of a data processing device provided according to an embodiment of the present application;

[0034] Fig.11 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0039] The present invention is described below in conjunction with preferred implementation steps. Figure 1 is a flow chart of a data processing method provided according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0040] Step S101, obtaining a historical data set and weather information within a preset time period, wherein the historical data set includes at least: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharging data of the energy storage system and historical load demand data of the target power grid, and the photovoltaic power generation system and the energy storage system provide power for the target power grid.

[0041] For example, the above historical data set may include but is not limited to the power generation of photovoltaic panels, weather conditions (such as temperature, sunshine intensity, etc.), the charging and discharging amount of the energy storage system, the load demand of the power grid, etc. The weather information within the above preset time period may be information obtained through weather forecast.

[0042] Step S102, inputting the historical data set and the weather information within the preset time period into the target prediction model for processing, and predicting to obtain the target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period, and the target discharging data of the energy storage system within the preset time period.

[0043] For example, based on the data in the historical data set and the weather forecast information (corresponding to the weather information within the above-mentioned preset time period), a machine learning algorithm (corresponding to the above-mentioned target prediction model) is used to predict the optimal photovoltaic power generation (corresponding to the above-mentioned target power generation data) and the optimal energy storage charge and discharge (corresponding to the above-mentioned target charge data and target discharge data) in a future period of time (corresponding to the above-mentioned preset time period).

[0044] Step S103: adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set.

[0045] For example, based on the optimal output (corresponding to the above-mentioned target power generation data) and the charging and discharging strategy (corresponding to the above-mentioned target charging data and target discharging data), it is issued to the photovoltaic power generation system and the energy storage system through the automatic control system, and the operating parameters of the photovoltaic power generation system and the energy storage system are adjusted.

[0046] Through the above steps S101 to S103, by collecting the real-time power generation data of the photovoltaic power generation system, the real-time charging and discharging status of the energy storage system and the real-time load demand of the power grid, and calculating the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system according to the real-time power generation data, the charging and discharging status and the load demand, and then according to the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system, the operating parameters of the photovoltaic power generation system and the energy storage system can be accurately adjusted, thereby reducing the impact of photovoltaic power generation on the power grid, thereby achieving the effect of improving the stability of the power grid.

[0047] Optionally, in the data processing method provided in the embodiment of the present application, the historical data set and the weather information within a preset time period are input into the target prediction model for processing, and the target data set is predicted to be obtained, including: inputting the historical data set and the weather information within the preset time period into the target prediction model, and using a time series analysis method to analyze and process the data in the historical data set and the weather information within the preset time period to obtain an analysis result, wherein the target prediction model is a model constructed based on a long short-term memory network; based on the analysis result, the target data set is predicted to be obtained.

[0048] For example, time series analysis and prediction models (such as long short-term memory networks, LSTM) (corresponding to the target prediction model mentioned above) can be used to comprehensively consider multiple factors such as seasonal changes, weather forecasts, historical power generation, and historical energy storage usage patterns, so as to output accurate prediction results (corresponding to the target data set mentioned above).

[0049] For example, if the weather forecast shows that it will continue to be sunny in the next few days, combined with historical data analysis, it is found that photovoltaic panels have a higher power generation efficiency under similar weather conditions, then the system will predict that photovoltaic power generation will increase during this period. Similarly, if it is predicted that the peak load of the power grid will increase in the future, the system may instruct the energy storage system to store more electricity in advance so that it can be released when needed, balancing the load on the power grid and improving the system's responsiveness.

[0050] In summary, the prediction of the optimal photovoltaic power generation and the optimal charging and discharging of energy storage is not only based on the statistical analysis of historical data, but also combined with real-time information of weather forecasts, making the prediction more accurate and timely.

[0051] Optionally, in the data processing method provided in the embodiment of the present application, adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set includes: determining multiple buses corresponding to different voltage levels in the target power grid, and calculating the change data of the charging power corresponding to each bus according to the target data set; adjusting the charging power corresponding to each bus according to the change data of the charging power corresponding to each bus to obtain the adjusted charging power corresponding to each bus; and adjusting the operating parameters of the photovoltaic power generation system and the energy storage system based on the adjusted charging power corresponding to each bus.

[0052] For example, the various buses corresponding to the above-mentioned different voltage levels may be 10kV, 35kV, 110kV and 220kV buses. And according to the optimal output and charging and discharging strategy (corresponding to the above-mentioned target data set), the charging power variables (corresponding to the above-mentioned charging power change data) corresponding to the 10kV, 35kV, 110kV and 220kV buses (corresponding to the above-mentioned various buses) can be adjusted through the automatic control system to the photovoltaic power generation system and the energy storage system, and then the operating parameters of the photovoltaic power generation system and the energy storage system can be adjusted according to the charging power corresponding to the adjusted 10kV, 35kV, 110kV and 220kV buses (corresponding to the above-mentioned various buses).

[0053] In summary, through hierarchical monitoring and adjustment strategies, the operating status of photovoltaic power generation systems and energy storage systems can be more accurately controlled and optimized, ensuring the effective use of energy and improving the economy and reliability of the entire energy system.

[0054] Optionally, in the data processing method provided in the embodiment of the present application, calculating the change data of the charging power corresponding to each bus according to the target data set includes: calculating the reverse feed amount corresponding to each voltage level according to the target data set, wherein the reverse feed amount is used to represent the power information provided by the photovoltaic power generation system and the energy storage system to the target power grid, and each voltage level is the voltage level corresponding to each bus; calculating the sum of the maximum power capacity of the target power grid at each voltage level to obtain the target value at each voltage level; calculating the ratio between the reverse feed amount corresponding to each voltage level and the target value at each voltage level to obtain the target ratio corresponding to each voltage level; and calculating the change data of the charging power corresponding to each bus based on the target ratio corresponding to each voltage level.

[0055] For example, the system sets 10kV, 35kV, 110kV and 220kV buses (corresponding to the above-mentioned multiple buses) as units, corresponding to the fourth, third, second and first power monitoring levels, respectively, to achieve power management and analysis at different levels. First, the reverse transmission amount (F) of each power monitoring level is calculated using the first calculation formula, which is: F = GZ-CZ-LZ, where F is the reverse transmission amount, GZ is the power generation, CZ is the charging power, and LZ is the total load power. The purpose of this step is to determine the net amount of electricity sent by the power generation system to the grid at each monitoring level. Next, the second calculation formula is used to calculate the reverse transmission ratio (BL) of each level (corresponding to the above-mentioned target ratio) in order to evaluate the power generation efficiency and contribution of each level: BL = F÷ZL, where BL is the reverse transmission ratio, F is the reverse transmission amount, and ZL is the total capacity of the power monitoring level where the photovoltaic power is located (corresponding to the above-mentioned target value). Afterwards, based on the ranking of the reverse transmission ratio, the system applies different calculation formulas to the top three and bottom three reverse transmission ratios in the first to fourth power monitoring levels to adjust the charging power variable (corresponding to the above-mentioned charging power change data).

[0056] Through the above scheme, the charging power variable corresponding to each bus can be calculated quickly and accurately according to the reverse transmission amount and reverse transmission ratio.

[0057] Optionally, in the data processing method provided in the embodiment of the present application, after adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, the method also includes: obtaining the actual power generation data of the photovoltaic power generation system within a preset time period, the actual charging data of the energy storage system within a preset time period, and the actual discharging data of the energy storage system within a preset time period to obtain an actual data set; based on the actual data set and the target data set, determining whether the target system is in an abnormal state, wherein the target system is a system composed of a photovoltaic power generation system and an energy storage system.

[0058] For example, real-time monitoring of photovoltaic output (corresponding to the actual power generation data mentioned above), energy storage status (corresponding to the actual charging data and actual discharging data mentioned above) and grid load are performed, and compared according to predetermined strategies to determine whether any deviation or abnormality occurs in the photovoltaic storage integrated system (corresponding to the target system mentioned above).

[0059] Through the above scheme, it is possible to conveniently detect whether an abnormality occurs in the integrated photovoltaic and storage system based on the real-time monitored data.

[0060] Optionally, in the data processing method provided in the embodiment of the present application, judging whether the target system is in an abnormal state based on the actual data set and the target data set includes: calculating the first difference between the actual power generation data of the photovoltaic power generation system in a preset time period and the target power generation data of the photovoltaic power generation system in the preset time period based on the actual data set and the target data set, and calculating the second difference between the actual charge data of the energy storage system in the preset time period and the target charge data of the energy storage system in the preset time period, and calculating the third difference between the actual discharge data of the energy storage system in the preset time period and the target discharge data of the energy storage system in the preset time period; judging whether the first difference is greater than a first preset threshold, judging whether the second difference is greater than the second preset threshold, and judging whether the third difference is greater than a third preset threshold; if the first difference is not greater than the first preset threshold, and the second difference is not greater than the second preset threshold, and the third difference is not greater than the third preset threshold, then determining that the target system is in a normal state; if the first difference is greater than the first preset threshold, and / or the second difference is greater than the second preset threshold, and / or the third difference is greater than the third preset threshold, then determining that the target system is in an abnormal state.

[0061] For example, the system continuously collects real-time data on key parameters such as photovoltaic output (corresponding to the actual power generation data mentioned above), energy storage status (corresponding to the actual charging data and actual discharging data mentioned above), and grid load. These data are the basis for subsequent analysis and judgment, ensuring the real-time and accuracy of monitoring. Next, the system uses a predetermined strategy for comparative analysis based on the real-time monitoring data. This includes evaluating whether each indicator is within the normal operating range and whether there is an obvious trend change, so that a preliminary judgment can be made as to whether there is any deviation or abnormality in the photovoltaic storage integrated system (corresponding to the target system mentioned above).

[0062] In summary, by comparing the real-time monitoring data and the predicted data, it is possible to quickly and accurately determine whether an abnormality occurs in the integrated photovoltaic and energy storage system.

[0063] Optionally, in the data processing method provided in the embodiment of the present application, after determining whether the target system is in an abnormal state based on the actual data set and the target data set, the method also includes: if the target system is in a normal state, prohibiting adjustment of the operating parameters of the photovoltaic power generation system and the energy storage system; if the target system is in an abnormal state, determining an adjustment strategy based on the actual data set, the weather information at the current moment and the operating status information of the target system; and adjusting the operating parameters of the photovoltaic power generation system and the energy storage system again based on the adjustment strategy.

[0064] For example, if it is monitored that the integrated photovoltaic and storage system (corresponding to the target system mentioned above) has not deviated and is normal, the operating parameters of the photovoltaic power generation system and the energy storage system may not be adjusted; if it is monitored that the integrated photovoltaic and storage system (corresponding to the target system mentioned above) has deviated or is abnormal, the central processing unit will recalculate the strategy based on the new data (corresponding to the actual data set mentioned above) and quickly adjust the operating parameters of the photovoltaic power generation system and the energy storage system.

[0065] The above solution ensures that the integrated photovoltaic and storage system can dynamically adapt to environmental changes and market demands and optimize overall performance.

[0066] For example, Figure 2 is a flow chart of a photovoltaic-storage integrated source-load interaction method provided according to an embodiment of the present application, such as Figure 2 As shown, a photovoltaic-storage integrated source-load interaction method includes:

[0067] Step S201, collecting the power generation of the photovoltaic power generation system, the charging and discharging status of the energy storage system and the real-time load demand of the power grid in real time through sensors and smart meters;

[0068] Step S202, obtaining the optimal photovoltaic power generation and the optimal energy storage charge and discharge according to historical data and weather forecast;

[0069] Step S203, according to the optimal output and charging and discharging strategy, the automatic control system is sent to the photovoltaic power generation system and the energy storage system to adjust the charging power variables corresponding to the 10kV, 35kV, 110kV and 220kV buses;

[0070] Step S204, real-time monitoring of photovoltaic output, energy storage status and grid load, comparison according to a predetermined strategy, and finding any deviation or abnormality;

[0071] Step S205, if deviation or abnormality is detected, the central processing unit will recalculate the strategy based on the new data and quickly adjust the operating parameters of the energy storage system;

[0072] Step S206, setting a user interface, the operator monitors the system status online, and manually adjusts the strategy.

[0073] In this embodiment, in order to achieve an effective combination of photovoltaic power generation and energy storage systems, improve the utilization rate of photovoltaic power generation and the stability of the power grid, the real-time power generation data of the photovoltaic power generation system, the real-time charging status of the energy storage system and the real-time load demand of the power grid are collected, and the optimal output of the photovoltaic power generation system and the optimal charging and discharging strategy of the energy storage system are calculated based on the real-time power generation data, charging status and load demand, so as to reduce the impact of photovoltaic power generation on the power grid, which is conducive to promoting the development of photovoltaic power generation and the widespread application of renewable energy.

[0074] For example, Figure 3 is a flow chart of collecting the power generation of the photovoltaic power generation system, the charging and discharging status of the energy storage system and the real-time load demand of the power grid in real time through sensors and intelligent meters in this embodiment, such as Figure 3 As shown, sensors and smart meters are used to collect the power generation of the photovoltaic power generation system, the charging and discharging status of the energy storage system, and the real-time load demand of the power grid in real time, including:

[0075] Step S301, installing sensors and smart meters at preset nodes of the photovoltaic power generation system, energy storage system and power grid for real-time monitoring of various parameters;

[0076] Step S302, configure a central data processing system, connect it to all sensors and smart meters, and ensure that data can be received in real time.

[0077] In this embodiment, first, dedicated sensors are installed on key components of the photovoltaic power generation system, such as the output end of the photovoltaic panel array, the inverter, and the node connected to the power grid. These sensors can accurately capture the current and voltage information of the photovoltaic panel, so as to calculate the amount of electricity generated in real time. Similarly, a series of sensors are installed on energy storage systems such as battery packs and power conversion systems to monitor their charging status and discharge rate. For the real-time load demand of the power grid, smart meters are installed at the key access points of the power grid to monitor the real-time load and voltage parameters of the power grid. These sensors and smart meters are equipped with high-speed data transmission capabilities, and can send the collected data to the central data processing system in real time. The central data processing system is a high-performance computer system equipped with powerful data processing and analysis software. The system is connected to all sensors and smart meters through a high-speed data communication network to ensure the real-time transmission and processing of data. The main tasks of the central data processing system include data collection, data storage, data analysis, and the generation of optimization strategies. It uses advanced data analysis algorithms, combined with weather forecasts, historical data and other information, to conduct a comprehensive analysis of the power generation of the photovoltaic system, the charging and discharging status of the energy storage system and the load demand of the power grid, so as to dynamically adjust the operation strategy of the photovoltaic power generation and energy storage system to optimize the energy efficiency and economy of the entire system. For example, if the weather forecast predicts that there will be sufficient sunshine in the next few hours, the central data processing system may instruct the energy storage system to store energy in advance to supplement the power grid demand when the photovoltaic power generation decreases; on the contrary, if it is predicted to be cloudy, the system may reduce the discharge rate of the energy storage to ensure that sufficient power support can be provided in the event of energy shortage. In this way, this embodiment not only realizes real-time monitoring and management of the photovoltaic power generation system, the energy storage system and the power grid, but also significantly improves the energy utilization rate and the economy of the system through the efficient scheduling of the central data processing system, and also provides strong technical support for the stability of the power grid and the effective integration of renewable energy.

[0078] For example, Figure 4 is a flow chart of obtaining the optimal photovoltaic power generation and the optimal energy storage charge and discharge according to historical data and weather forecast in this embodiment, such as Figure 4 As shown, the optimal photovoltaic power generation and optimal energy storage charging and discharging are obtained based on historical data and weather forecasts, including:

[0079] Step S401, collecting historical photovoltaic power generation data, energy storage charging and discharging records, and weather forecast information, and integrating these data into a central database;

[0080] Step S402, based on the integrated data, use a machine learning algorithm to predict the optimal photovoltaic power generation and the optimal energy storage charge and discharge in the future.

[0081] In this embodiment, first, a central database is constructed to store historical photovoltaic power generation data, energy storage charging and discharging records, and weather forecast information. These data include but are not limited to the power generation of photovoltaic panels, weather conditions (such as temperature, sunshine intensity, etc.), the charging and discharging of energy storage systems, and the load demand of the power grid. All of these data are regularly updated and integrated into the central database, providing rich data support for subsequent analysis. Next, based on this comprehensive data set, a machine learning algorithm is applied to predict the optimal photovoltaic power generation and the optimal charging and discharging of energy storage in the future. Specifically, time series analysis and prediction models (such as long short-term memory networks, LSTM) are used to comprehensively consider multiple factors such as seasonal changes, weather forecasts, historical power generation, and historical energy storage usage patterns, thereby outputting accurate prediction results. This prediction is not only based on statistical analysis of historical data, but also combined with real-time information from weather forecasts, making the prediction more accurate and timely. For example, if the weather forecast shows that it will continue to be sunny in the next few days, combined with historical data analysis, it is found that photovoltaic panels have a higher power generation efficiency under similar weather conditions, then the system will predict that photovoltaic power generation will increase during this period. Similarly, if it is predicted that the peak load of the power grid will increase in the future, the system may instruct the energy storage system to store more electricity in advance so that it can be released when needed, balancing the grid load and improving the system's responsiveness.

[0082] For example, Figure 5 This is a flowchart of adjusting the charging power variables corresponding to the 10kV, 35kV, 110kV and 220kV buses according to the optimal output and charging and discharging strategies in this embodiment, which is sent to the photovoltaic power generation system and the energy storage system through the automatic control system, such as Figure 5 As shown, according to the optimal output and charging and discharging strategy, the automatic control system is used to send the power to the photovoltaic power generation system and energy storage system to adjust the charging power variables corresponding to the 10kV, 35kV, 110kV and 220kV buses, including:

[0083] Step S501, setting 10kV, 35kV, 110kV and 220kV buses as units, corresponding to the fourth, third, second and first level power monitoring levels respectively;

[0084] Step S502, calculating the reverse transmission amount for each power monitoring level using the first calculation formula;

[0085] Step S503, at each power monitoring level, using a second calculation formula to calculate the reverse transmission ratio;

[0086] Step S504, at the first level power monitoring level, the top three reverse transmission ratios use the third calculation formula to calculate the first level charging power variable; at the second level power monitoring level, the top three reverse transmission ratios use the fourth calculation formula to calculate the second level charging power variable; at the third level power monitoring level, the top three reverse transmission ratios use the fifth calculation formula to calculate the third level charging power variable; at the fourth level power monitoring level, the top three reverse transmission ratios use the sixth calculation formula to calculate the fourth level charging power variable;

[0087] Step S505, at the first level power monitoring level, the last three reverse transmission ratios use the seventh calculation formula to calculate the first level charging power variable; at the second level power monitoring level, the last three reverse transmission ratios use the eighth calculation formula to calculate the second level charging power variable; at the third level power monitoring level, the last three reverse transmission ratios use the ninth calculation formula to calculate the third level charging power variable; at the fourth level power monitoring level, the last three reverse transmission ratios use the tenth calculation formula to calculate the fourth level charging power variable;

[0088] Moreover, the first calculation formula is: F = GZ-CZ-LZ, where F is the reverse transmission amount, GZ is the power generation, CZ is the charging power, and LZ is the total load power; the second calculation formula is: BL = F÷ZL, where BL is the reverse transmission ratio, F is the reverse transmission amount, and ZL is the total capacity of the power monitoring level where the photovoltaic power plant is located; the third calculation formula is: CD1 = k1×F1, where CD1 is the first-level charging power variable, k1 is the preset first-level charging coefficient, and F1 is the first-level power monitoring level where the photovoltaic power plant is located. The fourth calculation formula is: CD2 = k2 × F2, where CD2 is the second-level charging power variable, k2 is the preset second-level charging coefficient, and F2 is the reverse transmission corresponding to the second-level power monitoring level where the photovoltaic is located; the fifth calculation formula is: CD3 = k3 × F3, where CD3 is the third-level charging power variable = k3 is the preset third-level charging coefficient × F3 is the reverse transmission corresponding to the third-level power monitoring level where the photovoltaic is located; the sixth calculation formula is: CD4 = k4 × F4, where In the formula, CD4 is the fourth-level charging power variable = k4 is the preset fourth-level charging coefficient × F4 is the reverse transmission amount corresponding to the fourth-level power monitoring level where the photovoltaic is located; the seventh calculation formula is: CD1 = k5 × F5, wherein CD1 is the first-level charging power variable = k5 is the preset first-level discharge coefficient × F5 is the reverse transmission amount corresponding to the first-level power monitoring level where the photovoltaic is located; the eighth calculation formula is: CD2 = k6 × F6, wherein CD2 is the second-level charging power variable = k6 is the preset second-level discharge coefficient × F6 is the reverse transmission amount corresponding to the second-level power monitoring level where the photovoltaic is located; the ninth calculation formula is: CD3 = k7 × F7, wherein CD3 is the third-level charging power variable = k7 is the preset third-level discharge coefficient × F7 is the reverse transmission amount corresponding to the third-level power monitoring level where the photovoltaic is located; the tenth calculation formula is: CD4 = k8 × F8, wherein CD4 is the fourth-level charging power variable = k8 is the preset fourth-level discharge coefficient × F8 is the reverse transmission amount corresponding to the fourth-level power monitoring level where the photovoltaic is located.

[0089] In this embodiment, the system sets 10kV, 35kV, 110kV and 220kV buses as units, corresponding to the fourth, third, second and first power monitoring levels, respectively, to achieve power management and analysis at different levels. First, the reverse transmission amount (F) of each power monitoring level is calculated using the first calculation formula, which is: F = GZ-CZ-LZ, where F is the reverse transmission amount, GZ is the power generation, CZ is the charging power, and LZ is the total load power. The purpose of this step is to determine the net amount of electricity sent by the power generation system to the grid at each monitoring level. Next, the second calculation formula is used to calculate the reverse transmission ratio (BL) of each level in order to evaluate the power generation efficiency and contribution of each level: BL = F÷ZL, where BL is the reverse transmission ratio, F is the reverse transmission amount, and ZL is the total capacity of the power monitoring level where the photovoltaic is located. Afterwards, according to the ranking of the reverse transmission ratio, the system applies different calculation formulas to the top three and bottom three reverse transmission ratios in the first to fourth power monitoring levels to adjust the charging power variables. These formulas are as follows: For the top three levels of reverse transmission ratio, the third to sixth calculation formulas are used to calculate the charging power variables of each level (CD1, CD2, CD3, CD4), and the formulas are: CD1 = k1 × F1, CD2 = k2 × F2, CD3 = k3 × F3, CD4 = k4 × F4, where k1, k2, k3, k4 are the preset first to fourth level charging coefficients, and F1, F2, F3, F4 are the corresponding reverse transmission amounts of each level. For the last three levels of the reverse transmission ratio, the seventh to tenth calculation formulas are used to calculate the charging power variables of each level (CD1, CD2, CD3, CD4), and the formulas are: CD1 = k5 × F5, CD2 = k6 × F6, CD3 = k7 × F7, CD4 = k8 × F8, where k5, k6, k7, k8 are the preset first to fourth level discharge coefficients, and F5, F6, F7, F8 are the corresponding reverse transmission amounts of each level. Through this hierarchical monitoring and adjustment strategy, the operating status of the photovoltaic power generation system and the energy storage system can be more accurately controlled and optimized, ensuring the effective use of energy and improving the economy and reliability of the entire energy system.

[0090] For example, Figure 6 This is a flowchart of real-time monitoring of photovoltaic output, energy storage status and grid load in this embodiment, comparing them according to a predetermined strategy, and finding any deviation or abnormality, such as Figure 6 As shown, the PV output, energy storage status and grid load are monitored in real time, and compared according to the predetermined strategy to find any deviation or abnormality, including:

[0091] Step S601, determining whether the eleventh calculation formula is satisfied, if satisfied, it is considered that a deviation or abnormality is found;

[0092] Step S602: if the eleventh calculation formula is not satisfied, it is considered that there is no abnormality and no additional processing is performed;

[0093] Furthermore, the eleventh calculation formula is:

[0094] |CD1|>1%×ZGR

[0095] |CD2|>1%×ZGR

[0096] |CD3|>1%×ZGR

[0097] |CD4|>1%×ZGR

[0098] Among them, ZGR is the total capacity of the power monitoring level where the photovoltaic power station is located.

[0099] In this embodiment, first, the system continuously collects real-time data of key parameters such as photovoltaic output, energy storage status and grid load. These data are the basis for subsequent analysis and judgment, ensuring the real-time and accuracy of monitoring. Next, the system uses a predetermined strategy for comparative analysis based on the real-time monitoring data. This includes evaluating whether each indicator is within the normal operating range and whether there is an obvious trend change, so as to preliminarily determine whether there is a deviation or abnormality. Apply the eleventh calculation formula for further judgment: When the preliminary judgment finds possible deviation or abnormality, the system further applies the eleventh calculation formula for verification: |CD1|>1%×ZGR, |CD2|>1%×ZGR, |CD3|>1%×ZGR, |CD4|>1%×ZGR, where CD1, CD2, CD3, CD4 represent the charging power variables of different power monitoring levels (first to fourth levels), respectively, and ZGR is the total capacity of the power monitoring level where photovoltaics are located. This formula is used to evaluate whether the charging power variables of each level exceed 1% of the total capacity, which is a key threshold for judging deviation or abnormality. If the charging power variables at any level meet the above conditions, it is considered that a deviation or abnormality has been found, and the system will take corresponding response measures accordingly, such as adjusting the photovoltaic power generation strategy, optimizing the energy storage charging and discharging plan, or notifying the operation and maintenance personnel to conduct inspections. If the charging power variables at all levels do not meet the above conditions, it is considered that there is no abnormality, and the system will not perform additional processing and continue to perform regular monitoring.

[0100] For example, Figure 7 This is a flowchart of the embodiment in which the central processing unit recalculates the strategy according to the new data and quickly adjusts the operating parameters of the energy storage system when a deviation or abnormality is detected, such as Figure 7 As shown in the figure, if deviation or abnormality is detected, the central processing unit will recalculate the strategy based on the new data and quickly adjust the operating parameters of the energy storage system, including:

[0101] Step S701, when a deviation or anomaly is detected, the central processing unit will calculate the adjusted charging power variable using the twelfth calculation formula;

[0102] Step S702, adjusting the operating parameters of the energy storage system according to the adjusted charging power variable;

[0103] Moreover, the twelfth calculation formula is: TH=TQ-1%×(GZ-CZ-LZ), wherein TH is the charging power variable after adjustment, and TQ is the charging power variable before adjustment.

[0104] In this embodiment, once a deviation is detected, the central processing unit will recalculate the strategy based on the new data and quickly adjust the operating parameters of the power generation and energy storage systems. This feedback loop ensures that the system can dynamically adapt to environmental changes and market demands and optimize overall performance.

[0105] For example, Figure 8 is a flow chart of the setting user interface in this embodiment, in which the operator monitors the system status online and manually adjusts the strategy, as shown in FIG. Figure 8 As shown, a user interface is set up, and the operator monitors the system status online and manually adjusts the strategy, including:

[0106] Step S801, developing a user interface to enable an operator to monitor the real-time status of the system, including photovoltaic power generation, energy storage charging and discharging status, and grid load demand;

[0107] Step S802, accessing the central control system through a secure network connection, monitoring the system status online, and providing real-time data updates;

[0108] Step S803, integrating control elements in the user interface, allowing the operator to manually adjust the photovoltaic power generation and energy storage strategies according to the monitored data to adapt to special situations or optimize system performance.

[0109] In this embodiment, once a deviation or anomaly is detected, the central processing unit will first use the twelfth calculation formula to calculate the adjusted charging power variable based on the new data. The formula is as follows: TH = TQ-1% × (GZ-CZ-LZ), where TH represents the adjusted charging power variable, TQ is the charging power variable before adjustment, GZ is the power generation, CZ is the charging power, and LZ is the total load power. This calculation process takes into account the current power generation, charging power and load to ensure that the adjusted charging power variable can adapt to the current operating state and correct previous deviations; based on the adjusted charging power variable TH calculated above, the central processing unit will adjust the operating parameters of the energy storage system accordingly. This may include changing the charging or discharging rate of the energy storage system to ensure that the behavior of the energy storage system matches the adjusted charging power variable. For example, if monitoring finds that photovoltaic power generation is lower than expected, resulting in insufficient charging of the energy storage system, the central processing unit may instruct the energy storage system to increase the charging rate to replenish the insufficient power; after the adjustment is completed, the system will continue to monitor all relevant parameters in real time to ensure that the actual operating status of the energy storage system is consistent with the adjustment strategy, and further observe whether there are any deviations or abnormalities. If the operating status does not achieve the expected effect or new deviations occur, the central processing unit will adjust the strategy again according to the actual situation to optimize the system response.

[0110] For example, Fig. 9 is a schematic diagram of a photovoltaic storage integrated source-load interactive system provided according to an embodiment of the present application, such as Fig. 9 As shown, a photovoltaic-storage integrated source-load interactive system includes:

[0111] The real-time data acquisition module 901 is used to collect the power generation of the photovoltaic power generation system, the charging and discharging status of the energy storage system and the real-time load demand of the power grid in real time through sensors and intelligent meters;

[0112] The optimization strategy calculation module 902 is used to obtain the optimal photovoltaic power generation and the optimal energy storage charge and discharge according to historical data and weather forecasts;

[0113] The command issuing and execution module 903 is used to issue commands to the photovoltaic power generation system and the energy storage system through the automatic control system according to the optimal output and charging and discharging strategy, and adjust the charging power variables corresponding to the 10kV, 35kV, 110kV and 220kV buses;

[0114] The monitoring and adjustment module 904 is used to monitor the photovoltaic output, energy storage status and grid load in real time, compare them according to the predetermined strategy, and find any deviation or abnormality;

[0115] Feedback loop mechanism module 905, used to detect deviations or anomalies, the central processing unit will recalculate the strategy based on the new data and quickly adjust the operating parameters of the energy storage system;

[0116] The user interface and interaction module 906 is used to set the user interface, and the operator monitors the system status online and manually adjusts the strategy.

[0117] In this embodiment, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0118] In addition, in this embodiment, through real-time data collection and monitoring feedback, effective display and timely adjustment of the power grid status are achieved; and through rapid analysis of online photovoltaic and load usage, an effective hierarchical consumption of excessive power generation is ultimately formed, achieving efficient and reliable source-load interaction.

[0119] Moreover, a photovoltaic storage integrated source-load interaction method and system in this embodiment includes obtaining the optimal photovoltaic power generation and the optimal energy storage charge and discharge according to historical data and weather forecasts; according to the optimal output and charge and discharge strategy, the automatic control system is issued to the photovoltaic power generation system and the energy storage system to adjust the charging power variables corresponding to the 10kV, 35kV, 110kV and 220kV busbars; real-time monitoring of photovoltaic output, energy storage status and grid load, and comparison according to the predetermined strategy to find any deviation or abnormality; when deviation or abnormality is detected, the central processing unit will recalculate the strategy according to the new data and quickly adjust the operating parameters of the energy storage system; set a user interface, the operator monitors the system status online, and manually adjusts the strategy. This solution automatically forms automatic control and feedback adjustments through real-time data collection combined with the use of photovoltaics and loads.

[0120] In summary, the data processing method provided in the embodiment of the present application obtains a historical data set and weather information within a preset time period, wherein the historical data set includes at least: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharge data of the energy storage system and historical load demand data of the target power grid, wherein the photovoltaic power generation system and the energy storage system supply power to the target power grid; the historical data set and the weather information within the preset time period are input into a target prediction model for processing, and a target data set is predicted, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period and the target discharge data of the energy storage system within the preset time period; according to the target data set, the operating parameters of the photovoltaic power generation system and the energy storage system are adjusted, thereby solving the problem in the related art that it is difficult to accurately adjust the operating parameters of the photovoltaic power generation system and the energy storage system, resulting in low stability of the power grid. By collecting the real-time power generation data of the photovoltaic power generation system, the real-time charging and discharging status of the energy storage system and the real-time load demand of the power grid, and calculating the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system based on the real-time power generation data, charging and discharging status and load demand, the operating parameters of the photovoltaic power generation system and the energy storage system can be accurately adjusted based on the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system, thereby reducing the impact of photovoltaic power generation on the power grid, thereby achieving the effect of improving the stability of the power grid.

[0121] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0122] The embodiment of the present application also provides a data processing device. It should be noted that the data processing device of the embodiment of the present application can be used to execute the data processing method provided by the embodiment of the present application. The data processing device provided by the embodiment of the present application is introduced below.

[0123] Fig.10 Schematic diagram of a data processing device provided according to an embodiment of the present application. Fig.10 As shown, the device includes: a first acquisition unit 1001, a first prediction unit 1002 and a first adjustment unit 1003.

[0124] Specifically, the first acquisition unit 1001 is used to acquire a historical data set and weather information within a preset time period, wherein the historical data set at least includes: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharging data of the energy storage system, and historical load demand data of the target power grid, and the photovoltaic power generation system and the energy storage system provide power for the target power grid;

[0125] The first prediction unit 1002 is used to input the historical data set and the weather information within the preset time period into the target prediction model for processing, and predict the target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period, and the target discharging data of the energy storage system within the preset time period;

[0126] The first adjustment unit 1003 is used to adjust the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set.

[0127] In summary, the data processing device provided in the embodiment of the present application obtains a historical data set and weather information within a preset time period through a first acquisition unit 1001, wherein the historical data set at least includes: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharging data of the energy storage system and historical load demand data of the target power grid, and the photovoltaic power generation system and the energy storage system supply power to the target power grid; the first prediction unit 1002 inputs the historical data set and the weather information within the preset time period into a target prediction model for processing, and predicts a target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period and the target discharging data of the energy storage system within the preset time period; the first adjustment unit 1003 adjusts the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, thereby solving the problem in the related art that it is difficult to accurately adjust the operating parameters of the photovoltaic power generation system and the energy storage system, resulting in low stability of the power grid. By collecting the real-time power generation data of the photovoltaic power generation system, the real-time charging and discharging status of the energy storage system and the real-time load demand of the power grid, and calculating the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system based on the real-time power generation data, charging and discharging status and load demand, the operating parameters of the photovoltaic power generation system and the energy storage system can be accurately adjusted based on the optimal power generation of the photovoltaic power generation system and the optimal charging and discharging amount of the energy storage system, thereby reducing the impact of photovoltaic power generation on the power grid, thereby achieving the effect of improving the stability of the power grid.

[0128] Optionally, in the data processing device provided in the embodiment of the present application, the first adjustment unit includes: a first processing module, used to determine the multiple buses corresponding to different voltage levels in the target power grid, and calculate the change data of the charging power corresponding to each bus according to the target data set; a second processing module, used to adjust the charging power corresponding to each bus according to the change data of the charging power corresponding to each bus, and obtain the adjusted charging power corresponding to each bus; the first adjustment module is used to adjust the operating parameters of the photovoltaic power generation system and the energy storage system based on the adjusted charging power corresponding to each bus.

[0129] Optionally, in the data processing device provided in the embodiment of the present application, the first processing module includes: a first calculation submodule, used to calculate the reverse transmission amount corresponding to each voltage level according to the target data set, wherein the reverse transmission amount is used to represent the power information provided by the photovoltaic power generation system and the energy storage system to the target power grid, and each voltage level is the voltage level corresponding to each bus; a second calculation submodule, used to calculate the sum of the maximum power capacity of the target power grid at each voltage level, and obtain the target value at each voltage level; a third calculation submodule, used to calculate the ratio between the reverse transmission amount corresponding to each voltage level and the target value at each voltage level, and obtain the target ratio corresponding to each voltage level; a fourth calculation submodule, used to calculate the change data of the charging power corresponding to each bus based on the target ratio corresponding to each voltage level.

[0130] Optionally, in the data processing device provided in the embodiment of the present application, the first prediction unit includes: a third processing module, used to input the historical data set and the weather information within a preset time period into the target prediction model, and use a time series analysis method to analyze and process the data in the historical data set and the weather information within the preset time period to obtain an analysis result, wherein the target prediction model is a model constructed based on a long short-term memory network; and a first prediction module, used to predict the target data set based on the analysis result.

[0131] Optionally, in the data processing device provided in the embodiment of the present application, the device also includes: a second acquisition unit, used to obtain the actual power generation data of the photovoltaic power generation system within a preset time period, the actual charging data of the energy storage system within a preset time period, and the actual discharging data of the energy storage system within a preset time period after adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, so as to obtain an actual data set; a first judgment unit, used to judge whether the target system is in an abnormal state based on the actual data set and the target data set, wherein the target system is a system composed of a photovoltaic power generation system and an energy storage system.

[0132] Optionally, in the data processing device provided in the embodiment of the present application, the first judgment unit includes: a first calculation module, which is used to calculate the first difference between the actual power generation data of the photovoltaic power generation system in the preset time period and the target power generation data of the photovoltaic power generation system in the preset time period based on the actual data set and the target data set, and calculate the second difference between the actual charge data of the energy storage system in the preset time period and the target charge data of the energy storage system in the preset time period, and calculate the third difference between the actual discharge data of the energy storage system in the preset time period and the target discharge data of the energy storage system in the preset time period; a first judgment module, which is used to judge whether the first difference is greater than a first preset threshold, and judge whether the second difference is greater than the second preset threshold, and judge whether the third difference is greater than a third preset threshold; a first determination module, which is used to determine that the target system is in a normal state if the first difference is not greater than the first preset threshold, and the second difference is not greater than the second preset threshold, and the third difference is not greater than the third preset threshold; a second determination module, which is used to determine that the target system is in an abnormal state if the first difference is greater than the first preset threshold, and / or the second difference is greater than the second preset threshold, and / or the third difference is greater than the third preset threshold.

[0133] Optionally, in the data processing device provided in the embodiment of the present application, the device also includes: a first processing unit, used to determine whether the target system is in an abnormal state based on the actual data set and the target data set, and if the target system is in a normal state, prohibiting adjustment of the operating parameters of the photovoltaic power generation system and the energy storage system; a first determination unit, used to determine an adjustment strategy based on the actual data set, weather information at the current moment and operating status information of the target system if the target system is in an abnormal state; a second adjustment unit, used to adjust the operating parameters of the photovoltaic power generation system and the energy storage system again based on the adjustment strategy.

[0134] The data processing device includes a processor and a memory. The first acquisition unit 1001, the first prediction unit 1002 and the first adjustment unit 1003 are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0135] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the stability of the power grid can be improved by adjusting the kernel parameters.

[0136] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0137] An embodiment of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the data processing method is implemented.

[0138] An embodiment of the present invention provides a processor, which is used to run a program, wherein the data processing method is executed when the program is run.

[0139] like Fig.11 As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: obtaining a historical data set and weather information within a preset time period, wherein the historical data set includes at least: historical weather data, historical power generation data of a photovoltaic power generation system, historical charging data of an energy storage system, historical discharging data of an energy storage system and historical load demand data of a target power grid, and the photovoltaic power generation system and the energy storage system supply power to the target power grid; inputting the historical data set and the weather information within the preset time period into a target prediction model for processing, and predicting a target data set, wherein the target data set includes target power generation data of the photovoltaic power generation system within the preset time period, target charging data of the energy storage system within the preset time period and target discharging data of the energy storage system within the preset time period; and adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set.

[0140] When the processor executes the program, the following steps are also implemented: according to the target data set, the operating parameters of the photovoltaic power generation system and the energy storage system are adjusted, including: determining multiple buses corresponding to different voltage levels in the target power grid, and calculating the change data of the charging power corresponding to each bus according to the target data set; adjusting the charging power corresponding to each bus according to the change data of the charging power corresponding to each bus to obtain the adjusted charging power corresponding to each bus; based on the adjusted charging power corresponding to each bus, adjusting the operating parameters of the photovoltaic power generation system and the energy storage system.

[0141] When the processor executes the program, the following steps are also implemented: according to the target data set, the change data of the charging power corresponding to each bus is calculated, including: according to the target data set, the reverse feed amount corresponding to each voltage level is calculated, wherein the reverse feed amount is used to represent the power information provided by the photovoltaic power generation system and the energy storage system to the target power grid, and each voltage level is the voltage level corresponding to each bus; the sum of the maximum power capacity of the target power grid at each voltage level is calculated to obtain the target value at each voltage level; the ratio between the reverse feed amount corresponding to each voltage level and the target value at each voltage level is calculated to obtain the target ratio corresponding to each voltage level; based on the target ratio corresponding to each voltage level, the change data of the charging power corresponding to each bus is calculated.

[0142] When the processor executes the program, the following steps are also implemented: inputting the historical data set and the weather information within a preset time period into the target prediction model for processing, and predicting the target data set includes: inputting the historical data set and the weather information within the preset time period into the target prediction model, and using a time series analysis method to analyze and process the data in the historical data set and the weather information within the preset time period to obtain an analysis result, wherein the target prediction model is a model constructed based on a long short-term memory network; based on the analysis result, the target data set is predicted.

[0143] When the processor executes the program, the following steps are also implemented: after adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, it also includes: obtaining the actual power generation data of the photovoltaic power generation system within a preset time period, the actual charging data of the energy storage system within a preset time period, and the actual discharging data of the energy storage system within a preset time period to obtain an actual data set; based on the actual data set and the target data set, determining whether the target system is in an abnormal state, wherein the target system is a system based on the photovoltaic power generation system and the energy storage system.

[0144] When the processor executes the program, the following steps are also implemented: judging whether the target system is in an abnormal state based on the actual data set and the target data set includes: calculating the first difference between the actual power generation data of the photovoltaic power generation system in a preset time period and the target power generation data of the photovoltaic power generation system in the preset time period based on the actual data set and the target data set, and calculating the second difference between the actual charge data of the energy storage system in the preset time period and the target charge data of the energy storage system in the preset time period, and calculating the third difference between the actual discharge data of the energy storage system in the preset time period and the target discharge data of the energy storage system in the preset time period; judging whether the first difference is greater than a first preset threshold, judging whether the second difference is greater than a second preset threshold, and judging whether the third difference is greater than a third preset threshold; if the first difference is not greater than the first preset threshold, and the second difference is not greater than the second preset threshold, and the third difference is not greater than the third preset threshold, then determining that the target system is in a normal state; if the first difference is greater than the first preset threshold, and / or the second difference is greater than the second preset threshold, and / or the third difference is greater than the third preset threshold, then determining that the target system is in an abnormal state.

[0145] When the processor executes the program, the following steps are also implemented: after judging whether the target system is in an abnormal state based on the actual data set and the target data set, it also includes: if the target system is in a normal state, it is forbidden to adjust the operating parameters of the photovoltaic power generation system and the energy storage system; if the target system is in an abnormal state, the adjustment strategy is determined based on the actual data set, the weather information at the current moment and the operating status information of the target system; according to the adjustment strategy, the operating parameters of the photovoltaic power generation system and the energy storage system are adjusted again. The device in this article can be a server, PC, PAD, mobile phone, etc.

[0146] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: obtaining a historical data set and weather information within a preset time period, wherein the historical data set includes at least: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharge data of the energy storage system, and historical load demand data of the target power grid, and the photovoltaic power generation system and the energy storage system supply power to the target power grid; inputting the historical data set and the weather information within the preset time period into a target prediction model for processing, and predicting a target data set, wherein the target data set includes target power generation data of the photovoltaic power generation system within the preset time period, target charging data of the energy storage system within the preset time period, and target discharge data of the energy storage system within the preset time period; and adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set.

[0147] When executed on a data processing device, it is also suitable for executing a program that is initialized with the following method steps: adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, including: determining multiple buses corresponding to different voltage levels in the target power grid, and calculating the change data of the charging power corresponding to each bus according to the target data set; adjusting the charging power corresponding to each bus according to the change data of the charging power corresponding to each bus to obtain the adjusted charging power corresponding to each bus; adjusting the operating parameters of the photovoltaic power generation system and the energy storage system based on the adjusted charging power corresponding to each bus.

[0148] When executed on a data processing device, it is also suitable for executing a program initialized with the following method steps: according to the target data set, calculating the change data of the charging power corresponding to each bus, including: according to the target data set, calculating the reverse transmission amount corresponding to each voltage level, wherein the reverse transmission amount is used to represent the power information provided by the photovoltaic power generation system and the energy storage system to the target power grid, and each voltage level is the voltage level corresponding to each bus; calculating the sum of the maximum power capacity of the target power grid at each voltage level, and obtaining the target value at each voltage level; calculating the ratio between the reverse transmission amount corresponding to each voltage level and the target value at each voltage level, and obtaining the target ratio corresponding to each voltage level; calculating the change data of the charging power corresponding to each bus according to the target ratio corresponding to each voltage level.

[0149] When executed on a data processing device, it is also suitable for executing a program initialized with the following method steps: inputting a historical data set and weather information within a preset time period into a target prediction model for processing, and predicting a target data set includes: inputting a historical data set and weather information within a preset time period into a target prediction model, and using a time series analysis method to analyze and process the data in the historical data set and the weather information within the preset time period to obtain an analysis result, wherein the target prediction model is a model constructed based on a long short-term memory network; based on the analysis result, the target data set is predicted.

[0150] When executed on a data processing device, it is also suitable for executing a program initialized with the following method steps: after adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, it also includes: obtaining the actual power generation data of the photovoltaic power generation system within a preset time period, the actual charging data of the energy storage system within a preset time period, and the actual discharging data of the energy storage system within a preset time period to obtain an actual data set; based on the actual data set and the target data set, judging whether the target system is in an abnormal state, wherein the target system is a system based on the photovoltaic power generation system and the energy storage system.

[0151] When executed on a data processing device, it is also suitable for executing a program initialized with the following method steps: judging whether the target system is in an abnormal state based on the actual data set and the target data set includes: calculating the first difference between the actual power generation data of the photovoltaic power generation system in a preset time period and the target power generation data of the photovoltaic power generation system in the preset time period based on the actual data set and the target data set, and calculating the second difference between the actual charge data of the energy storage system in the preset time period and the target charge data of the energy storage system in the preset time period, and calculating the third difference between the actual discharge data of the energy storage system in the preset time period and the target discharge data of the energy storage system in the preset time period; judging whether the first difference is greater than a first preset threshold, judging whether the second difference is greater than a second preset threshold, and judging whether the third difference is greater than a third preset threshold; if the first difference is not greater than the first preset threshold, and the second difference is not greater than the second preset threshold, and the third difference is not greater than the third preset threshold, then determining that the target system is in a normal state; if the first difference is greater than the first preset threshold, and / or the second difference is greater than the second preset threshold, and / or the third difference is greater than the third preset threshold, then determining that the target system is in an abnormal state.

[0152] When executed on a data processing device, it is also suitable for executing an initialization program having the following method steps: after determining whether the target system is in an abnormal state based on the actual data set and the target data set, it also includes: if the target system is in a normal state, it is prohibited to adjust the operating parameters of the photovoltaic power generation system and the energy storage system; if the target system is in an abnormal state, the adjustment strategy is determined based on the actual data set, the weather information at the current moment and the operating status information of the target system; according to the adjustment strategy, the operating parameters of the photovoltaic power generation system and the energy storage system are adjusted again.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0158] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0159] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0160] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0161] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0162] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A data processing method, characterized in that: include: Acquire a historical data set and weather information within a preset time period, wherein the historical data set includes at least: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharge data of the energy storage system, and historical load demand data of a target power grid, the photovoltaic power generation system and the energy storage system supplying power to the target power grid; Input the historical data set and the weather information within the preset time period into the target prediction model for processing, and predict a target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period, and the target discharging data of the energy storage system within the preset time period; According to the target data set, operating parameters of the photovoltaic power generation system and the energy storage system are adjusted.

2. The method according to claim 1, characterized in that According to the target data set, adjusting the operating parameters of the photovoltaic power generation system and the energy storage system includes: Determine multiple busbars corresponding to different voltage levels in the target power grid, and calculate charging power change data corresponding to each busbar according to the target data set; According to the change data of the charging power corresponding to each bus, the charging power corresponding to each bus is adjusted to obtain the adjusted charging power corresponding to each bus; Based on the adjusted charging power corresponding to each bus, the operating parameters of the photovoltaic power generation system and the energy storage system are adjusted.

3. The method according to claim 2, characterized in that According to the target data set, calculating the change data of the charging power corresponding to each bus includes: According to the target data set, the reverse transmission amount corresponding to each voltage level is calculated, wherein the reverse transmission amount is used to represent the power information provided by the photovoltaic power generation system and the energy storage system to the target power grid, and each voltage level is the voltage level corresponding to each bus; Calculating the sum of the maximum electric energy capacity of the target power grid at each voltage level to obtain a target value at each voltage level; Calculate the ratio between the reverse transmission amount corresponding to each voltage level and the target value under each voltage level to obtain the target ratio corresponding to each voltage level; According to the target ratio corresponding to each voltage level, the change data of the charging power corresponding to each bus is calculated.

4. The method according to claim 1, characterized in that: The historical data set and the weather information within the preset time period are input into the target prediction model for processing, and the target data set obtained by prediction includes: Inputting the historical data set and the weather information within the preset time period into the target prediction model, and using a time series analysis method to analyze and process the data in the historical data set and the weather information within the preset time period to obtain an analysis result, wherein the target prediction model is a model constructed based on a long short-term memory network; Based on the analysis result, the target data set is predicted.

5. The method according to claim 1, characterized in that After adjusting the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set, the method further includes: Acquire actual power generation data of the photovoltaic power generation system within the preset time period, actual charge data of the energy storage system within the preset time period, and actual discharge data of the energy storage system within the preset time period to obtain an actual data set; Based on the actual data set and the target data set, it is determined whether a target system is in an abnormal state, wherein the target system is a system composed of the photovoltaic power generation system and the energy storage system.

6. The method according to claim 5, characterized in that Judging whether the target system is in an abnormal state according to the actual data set and the target data set includes: According to the actual data set and the target data set, a first difference between the actual power generation data of the photovoltaic power generation system in the preset time period and the target power generation data of the photovoltaic power generation system in the preset time period is calculated, and a second difference between the actual charge data of the energy storage system in the preset time period and the target charge data of the energy storage system in the preset time period is calculated, and a third difference between the actual discharge data of the energy storage system in the preset time period and the target discharge data of the energy storage system in the preset time period is calculated; Determine whether the first difference is greater than a first preset threshold, determine whether the second difference is greater than a second preset threshold, and determine whether the third difference is greater than a third preset threshold; If the first difference is not greater than the first preset threshold, the second difference is not greater than the second preset threshold, and the third difference is not greater than the third preset threshold, it is determined that the target system is in a normal state; If the first difference is greater than the first preset threshold, and / or the second difference is greater than the second preset threshold, and / or the third difference is greater than the third preset threshold, it is determined that the target system is in an abnormal state.

7. The method according to claim 5, characterized in that After determining whether the target system is in an abnormal state based on the actual data set and the target data set, the method further includes: If the target system is in a normal state, it is prohibited to adjust the operating parameters of the photovoltaic power generation system and the energy storage system; If the target system is in an abnormal state, determining an adjustment strategy according to the actual data set, the weather information at the current moment and the operating state information of the target system; According to the adjustment strategy, the operating parameters of the photovoltaic power generation system and the energy storage system are adjusted again.

8. A data processing device, characterized in that: include: A first acquisition unit is used to acquire a historical data set and weather information within a preset time period, wherein the historical data set includes at least: historical weather data, historical power generation data of the photovoltaic power generation system, historical charging data of the energy storage system, historical discharge data of the energy storage system, and historical load demand data of a target power grid, wherein the photovoltaic power generation system and the energy storage system supply power to the target power grid; A first prediction unit is used to input the historical data set and the weather information within the preset time period into a target prediction model for processing, and predict a target data set, wherein the target data set includes the target power generation data of the photovoltaic power generation system within the preset time period, the target charging data of the energy storage system within the preset time period, and the target discharging data of the energy storage system within the preset time period; The first adjustment unit is used to adjust the operating parameters of the photovoltaic power generation system and the energy storage system according to the target data set.

9. A computer-readable storage medium, characterized in that: The storage medium stores a program, wherein the program executes the data processing method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method described in any one of claims 1 to 7.

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