Control method and system for distributed light storage energy

Abstract

The invention relates to a distributed light storage energy control method, and the method comprises the steps: firstly collecting effective illumination data of all photovoltaic modules, constructing an illumination data set through combining historical data, collecting environment data, and associating the environment data with the illumination data, thereby forming an environment influence data set; through a machine self-learning algorithm, an environment influence data set is analyzed, an environment influence factor is generated, the conversion efficiency of a photovoltaic module is predicted, energy conversion data of an inverter and historical input data of an energy storage device are collected, a light energy conversion data set is constructed, a conversion efficiency factor is generated, and the photovoltaic module is analyzed by utilizing the predicted environment data and the environment influence factor. According to the method, effective illumination data is predicted, expected energy storage data is calculated, whether the expected energy storage data meets the power demand or not is judged according to the power demand data, and a power supply distribution strategy is generated, so that the distributed light storage energy system is regulated and controlled, and the energy effective utilization rate of the distributed light storage energy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a control method and system for distributed photovoltaic energy storage. Background Art

[0002] Distributed photovoltaic energy storage system, as a system that converts solar energy into electrical energy and stores it, has attracted widespread attention because of its ability to improve energy utilization efficiency, reduce dependence on the power grid, and provide a more stable and reliable energy supply.

[0003] Existing distributed photovoltaic energy storage control methods usually rely on basic automation technology and preset rules for energy management, such as using preset rules to decide when to store energy or supply power to the grid. However, these rules are difficult to take into account real-time data and predictive analysis, and are difficult to adapt to the complex and changing energy supply and demand environment, which reduces the energy efficiency of distributed photovoltaic energy storage, and therefore need to be improved. Summary of the invention

[0004] In order to improve the energy efficiency of distributed photovoltaic energy storage, the present application provides a control method and system for distributed photovoltaic energy storage.

[0005] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0006] A control method for distributed photovoltaic energy storage, the method comprising the steps of:

[0007] Acquire effective light data received by all photovoltaic modules, and record the historical effective light data according to a preset common time axis to construct an effective light data set, wherein the effective light data refers to light data received by the photovoltaic modules and used for energy conversion;

[0008] Acquire environmental data corresponding to all photovoltaic modules according to a preset sampling standard and input the data into the effective light data set, and associate the environmental data with the effective light data according to the common time axis to construct an environmental impact data set;

[0009] The preset ambient light impact analysis algorithm model generates environmental impact factors corresponding to different photovoltaic modules based on the environmental impact data set by a machine self-learning algorithm, and the environmental impact factors are used to predict the effective conversion rate of photovoltaic modules to light under different environments;

[0010] Acquire the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associate the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set;

[0011] The preset conversion efficiency analysis algorithm model analyzes the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor, wherein the conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device;

[0012] Acquire predicted environmental data and generate predicted effective lighting data according to the predicted environmental data and the environmental influencing factors;

[0013] Calculating expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used to supply power to the electrical equipment;

[0014] The overall electricity demand data is obtained and whether the overall electricity demand is met is determined based on the expected energy storage data, and a power supply allocation strategy is generated based on the determination result, wherein the power supply allocation strategy is used to regulate distributed photovoltaic energy storage to meet the electricity demand.

[0015] By adopting the above technical solution, the effective illumination data and environmental data of photovoltaic modules are obtained and analyzed, and the machine self-learning algorithm is used to predict the conversion efficiency of photovoltaic modules in different environments, thereby improving the prediction accuracy of energy production. At the same time, the method optimizes the energy conversion efficiency of the inverter through the conversion efficiency analysis algorithm model to reduce energy loss. The intelligent power supply distribution strategy ensures the stability and reliability of energy supply, and the dynamic adaptability of the system improves its flexibility and adaptability, effectively avoiding power shortages due to unreasonable energy distribution, thereby improving the power consumption experience of distributed photovoltaic energy storage. In addition, accurate prediction and optimized power supply distribution strategy effectively utilize the generated electricity, reduce energy waste, improve energy utilization efficiency, and provide scientific decision-making support for energy management.

[0016] In a preferred example, the present application can be further configured as follows: judging whether the overall power demand is met based on the overall power demand data and the expected energy storage data, and generating a power supply allocation strategy based on the judgment result, wherein the power supply allocation strategy is used to regulate the distributed photovoltaic energy storage to meet the power demand, including the following steps:

[0017] Analyze the electricity demand and the status of the photovoltaic storage equipment in each area of ​​the distributed photovoltaic energy storage system based on the overall electricity demand data;

[0018] Comparing the expected energy storage data corresponding to each region in the expected energy storage data with the electricity demand of each region to evaluate the energy supply and demand balance state of each region;

[0019] If the energy supply in a certain area does not meet its demand, a power supply allocation strategy is generated to adjust the balance between energy supply and demand.

[0020] The power distribution strategy includes: adjusting the output of photovoltaic storage equipment in adjacent areas to compensate for the energy gap in areas with imbalanced supply and demand;

[0021] Optimize the charging and discharging strategies of energy storage equipment to support inter-regional dispatch of energy;

[0022] Adjust the operating parameters of photovoltaic modules to improve energy conversion efficiency.

[0023] By adopting the above technical solutions, the distributed photovoltaic energy storage system can be managed in a refined manner, ensuring the stability and reliability of energy supply, while optimizing the energy utilization efficiency and economy of the system. This method is particularly suitable for modern power grids with ever-changing energy demand and supply conditions, and provides an effective solution for realizing smart grids and sustainable energy management.

[0024] In a preferred example, the present application may be further configured as follows: after the step of obtaining the overall power demand data and determining whether the overall power demand is met according to the expected energy storage data, the following steps are included:

[0025] Obtaining historical electricity demand data, and constructing an electricity usage habit profile based on the historical electricity demand data;

[0026] Analyze the electricity usage habit profile to determine the user's electricity usage pattern and preference;

[0027] predicting the user's electricity demand based on the electricity usage pattern and preference;

[0028] According to the expected energy storage data and the predicted electricity demand, a personalized control strategy is generated, and the personalized control strategy is used to adjust the operating status of the photovoltaic power generation system and the energy storage system to meet the user's electricity demand and optimize energy utilization efficiency.

[0029] By adopting the above-mentioned technical solution, this application realizes personalized management of distributed photovoltaic energy storage systems, improves the flexibility of energy supply and user satisfaction. The personalized control strategy can not only meet the user's electricity needs, but also optimize energy utilization efficiency, reduce energy waste, and may reduce the user's electricity bill expenses. In addition, this method also helps to improve the operating efficiency and economy of the entire energy system, and provides an effective solution for realizing smart grids and sustainable energy management.

[0030] In a preferred example, the present application may be further configured as follows: further comprising the following steps: acquiring position distribution data of all photovoltaic components, constructing a photovoltaic position three-dimensional model according to the position distribution data, and performing distribution marking on the photovoltaic position three-dimensional model based on a preset zoning standard;

[0031] Generate regionalized electricity demand data based on the electricity consumption habit portrait, divide the electricity demand into different levels according to a preset electricity demand level threshold, and mark the electricity demand level of the photovoltaic location three-dimensional model based on the regionalized electricity demand data;

[0032] Specifically, different levels of electricity demand correspond to the frequency regulation requirements of photovoltaic modules, which are used by operating personnel to detect and adjust different frequencies of photovoltaic modules according to the frequency regulation requirements.

[0033] By adopting the above technical solution, the operating parameters of the photovoltaic modules are automatically adjusted according to the power demand level. By adjusting the detection and regulation frequency of the photovoltaic modules, operators can more effectively manage the output of the photovoltaic modules, optimize the allocation of power resources, and improve the response speed and operating efficiency of the entire system.

[0034] In a preferred example, the present application can be further configured as follows: after obtaining the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associating the historical energy conversion data and the historical input data of the energy storage device based on the common time axis to construct a light energy conversion data set, the following steps are included:

[0035] Obtain historical output data of the inverter, and calculate corresponding energy storage power deviation data based on historical input data of the energy storage device and historical output data of the inverter;

[0036] Associating the energy storage power deviation data with the environmental data based on the common time axis to construct an environmental energy storage deviation data set;

[0037] The preset energy storage deviation analysis algorithm model analyzes the environmental energy storage deviation data set based on a machine self-learning algorithm to generate a loss factor, which is used to calculate the energy loss when the inverter is transmitted to the energy storage device.

[0038] By adopting the above technical solution, the environmental energy storage deviation data set is analyzed to generate a loss factor. For example, if the analysis results show that the efficiency of the inverter decreases at a specific temperature, the system will adjust the operating parameters of the inverter to reduce energy loss. The loss factor helps to quantify the energy loss of the inverter under different environmental conditions, allowing operators to more accurately calculate the energy loss when the inverter is transmitted to the energy storage device and take corresponding optimization measures.

[0039] In a preferred example, the present application can be further configured as follows: a user interface is provided, which is used to display the predicted photovoltaic power generation output power, the charging and discharging status and control strategy of the energy storage system, and allows the user to adjust the control strategy according to the power usage habits.

[0040] In a preferred example, the present application can be further configured as follows: the machine self-learning algorithm includes a neural network algorithm.

[0041] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0042] A control device for distributed photovoltaic energy storage, the device comprising:

[0043] An effective illumination data set construction unit, used to obtain effective illumination data received by all photovoltaic modules, and record the historical effective illumination data according to a preset common time axis to construct an effective illumination data set, wherein the effective illumination data refers to the illumination data received by the photovoltaic modules and used for energy conversion;

[0044] An environmental impact data set construction unit, used to obtain environmental data corresponding to all photovoltaic modules according to a preset sampling standard and input the environmental data into the effective light data set, and associate the environmental data with the effective light data according to the common time axis to construct an environmental impact data set;

[0045] An environmental impact factor generating unit is used to pre-set an ambient light impact analysis algorithm model to generate environmental impact factors corresponding to different photovoltaic components based on the environmental impact data set based on a machine self-learning algorithm, and the environmental impact factors are used to predict the effective conversion rate of photovoltaic components to light under different environments;

[0046] A light energy conversion data set construction unit, used for acquiring historical energy conversion data of all inverters and historical input data of energy storage devices, and associating the historical energy conversion data and historical input data of energy storage devices based on the common time axis record to construct a light energy conversion data set;

[0047] A conversion efficiency factor generating unit, configured to be pre-set with a conversion efficiency analysis algorithm model to analyze the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor, wherein the conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device;

[0048] A predicted effective illumination data generating unit, used for acquiring predicted environmental data and generating predicted effective illumination data according to the predicted environmental data and the environmental influencing factors;

[0049] An expected energy storage data calculation unit, used to calculate expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used to supply power to the electrical equipment;

[0050] A power supply allocation strategy generation unit is used to obtain overall power demand data and determine whether the overall power demand is met based on the expected energy storage data, and generate a power supply allocation strategy based on the judgment result, wherein the power supply allocation strategy is used to regulate distributed photovoltaic energy storage to meet power demand.

[0051] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0052] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned control method for distributed photovoltaic energy storage when executing the computer program.

[0053] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0054] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned control method for distributed photovoltaic energy storage.

[0055] In summary, the present application includes at least one of the following beneficial technical effects:

[0056] 1. Acquire and analyze the effective illumination data and environmental data of photovoltaic modules, and use machine self-learning algorithms to predict the conversion efficiency of photovoltaic modules in different environments, thereby improving the prediction accuracy of energy output. At the same time, this method optimizes the energy conversion efficiency of the inverter through the conversion efficiency analysis algorithm model to reduce energy loss. The intelligent power supply distribution strategy ensures the stability and reliability of energy supply, while the dynamic adaptability of the system improves its flexibility and adaptability, effectively avoiding power shortages due to unreasonable energy distribution, thereby improving the power consumption experience of distributed photovoltaic energy storage. In addition, accurate prediction and optimized power supply distribution strategies effectively utilize the generated electricity, reduce energy waste, improve energy utilization efficiency, and provide scientific decision-making support for energy management;

[0057] 2. Realize personalized management of distributed photovoltaic energy storage systems, improve the flexibility of energy supply and user satisfaction. Personalized control strategies can not only meet users' electricity needs, but also optimize energy utilization efficiency, reduce energy waste, and possibly reduce users' electricity bills. In addition, this method also helps to improve the operating efficiency and economy of the entire energy system, providing an effective solution for realizing smart grids and sustainable energy management;

[0058] 3. Analyze the environmental energy storage deviation data set to generate a loss factor. For example, if the analysis results show that the efficiency of the inverter decreases at a specific temperature, the system will adjust the inverter's operating parameters to reduce energy loss. The loss factor helps quantify the energy loss of the inverter under different environmental conditions, allowing operators to more accurately calculate the energy loss when the inverter is transmitted to the energy storage device and take corresponding optimization measures;

[0059] 4. Realize the refined management of distributed photovoltaic energy storage systems, ensure the stability and reliability of energy supply, and optimize the energy utilization efficiency and economy of the system. This method is particularly suitable for modern power grids with ever-changing energy demand and supply conditions, and provides an effective solution for realizing smart grids and sustainable energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of a control method for distributed photovoltaic energy storage in one embodiment of the present application;

[0061] Figure 2 This is a principle block diagram of a control device for distributed photovoltaic energy storage in one embodiment of the present application;

[0062] Figure 3 It is a schematic diagram of an electronic device in an embodiment of the present application.

[0063] Figure Number:

[0064] 1. Effective illumination data set construction unit; 2. Environmental impact data set construction unit; 3. Environmental impact factor generation unit; 4. Light energy conversion data set construction unit; 5. Conversion efficiency factor generation unit; 6. Predicted effective illumination data generation unit; 7. Expected energy storage data calculation unit; 8. Power supply distribution strategy generation unit. DETAILED DESCRIPTION

[0065] The present application is further described in detail below in conjunction with the accompanying drawings.

[0066] In one embodiment, if Figure 1 As shown, the present application discloses a control method for distributed photovoltaic energy storage, which specifically includes the following steps:

[0067] S10: Acquire effective illumination data received by all photovoltaic modules, and record the historical effective illumination data according to a preset common time axis to construct an effective illumination data set;

[0068] The effective light data refers to the light data received by the photovoltaic module and used for energy conversion;

[0069] Specifically, in the embodiment of the present application, each photovoltaic module is equipped with a light intensity sensor for measuring the intensity of sunlight in real time. The system records the data once an hour and stores it in a central database. It should be noted that in the embodiment of the present application, the light intensity sensors are installed at the same position of the photovoltaic module to avoid detection errors caused by different installation locations. Through continuous monitoring, the real-time and accuracy of the data can be ensured, providing a reliable basis for subsequent data analysis and prediction.

[0070] S20: acquiring environmental data corresponding to all photovoltaic modules according to a preset sampling standard and inputting the environmental data into the effective illumination data set, and associating the environmental data with the effective illumination data according to the common time axis to construct an environmental impact data set;

[0071] Specifically, environmental data include light data, temperature data, humidity data, wind speed data, visibility data, haze data, etc. These data, together with the light data, are matched along the time axis to form a comprehensive environmental impact data set.

[0072] S30: The preset ambient light impact analysis algorithm model generates environmental impact factors corresponding to different photovoltaic modules based on the environmental impact data set by a machine self-learning algorithm;

[0073] The environmental impact factor is used to predict the effective conversion rate of photovoltaic modules to light under different environments;

[0074] Specifically, machine learning algorithms, including random forests and neural networks, are used to train historical data to determine the impact of environmental factors (such as temperature and humidity) on the efficiency of photovoltaic modules.

[0075] It should be noted that in the embodiment of the present application, a corresponding environmental impact factor is generated for each type of environmental factor in the environmental data, and a weight value is determined according to the actual degree of impact, and a comprehensive environmental impact factor is generated based on the weight value.

[0076] S40: Acquire historical energy conversion data of all inverters and historical input data of energy storage devices, and associate the historical energy conversion data and historical input data of energy storage devices based on the common time axis record to construct a light energy conversion data set;

[0077] S50: The preset conversion efficiency analysis algorithm model analyzes the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor;

[0078] The conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device;

[0079] Specifically, the input power (IIP) and output power (IOP) of the inverter at different positions are obtained and used in the conversion efficiency analysis algorithm model to generate a conversion efficiency factor through a machine self-learning algorithm.

[0080] S60: Acquire predicted environmental data and generate predicted effective lighting data according to the predicted environmental data and the environmental influencing factors;

[0081] Specifically, a time series analysis algorithm is used to combine environmental influencing factors and predicted environmental data to predict effective light data corresponding to future time periods.

[0082] S70: Calculating expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used to supply power to the electrical equipment;

[0083] Specifically, the expected energy storage data (ESD) can be calculated by the formula ESD=PD×E, where PD is the product of the predicted effective illumination data and the area of ​​the photovoltaic module, and E is the conversion efficiency factor.

[0084] S80: Obtaining overall power demand data and judging whether the overall power demand is met according to the expected energy storage data, and generating a power supply allocation strategy according to the judgment result;

[0085] The power supply allocation strategy is used to regulate distributed photovoltaic energy to meet power demand;

[0086] Specifically, a power supply distribution strategy is formulated based on the predicted energy storage data and actual power demand. For example, if it is predicted that a certain area will have high power demand at night, and the photovoltaic power generation capacity is limited, the energy storage equipment will be charged in advance to meet the demand. Through intelligent power supply distribution, the energy supply and demand of each region can be balanced, the operating efficiency of the entire system can be optimized, and a stable supply of energy can be ensured.

[0087] In summary, the effective illumination data and environmental data of photovoltaic modules are obtained and analyzed, and the conversion efficiency of photovoltaic modules in different environments is predicted using machine self-learning algorithms, thereby improving the prediction accuracy of energy production. At the same time, this method optimizes the energy conversion efficiency of the inverter through the conversion efficiency analysis algorithm model to reduce energy loss. The intelligent power supply distribution strategy ensures the stability and reliability of energy supply, while the dynamic adaptability of the system improves its flexibility and adaptability, effectively avoiding power shortages due to unreasonable energy distribution, thereby improving the power consumption experience of distributed photovoltaic energy storage. In addition, accurate prediction and optimized power supply distribution strategies effectively utilize the generated electricity, reduce energy waste, improve energy utilization efficiency, and provide scientific decision-making support for energy management.

[0088] After the step of S40: acquiring the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associating the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set, the following steps are included:

[0089] S41: Obtain historical output data of the inverter, and calculate corresponding energy storage power deviation data according to historical input data of the energy storage device and historical output data of the inverter;

[0090] Specifically, the system collects the historical output data of the inverter, including the output power at different time periods every day, and also collects the historical input data of the energy storage device. By comparing the two, the power deviation data of the energy storage device in a specific time period is calculated. For example, if the output power of the inverter is 5kW at noon, and the input power of the energy storage device is 4.8kW during the same period, then the power deviation is 0.2kW.

[0091] S42: Associating the energy storage power deviation data with the environmental data based on the common time axis to construct an environmental energy storage deviation data set;

[0092] Specifically, the system associates the calculated power deviation data with environmental data (such as temperature and humidity) and records them on a common timeline. For example, if it is found that the power deviation is large in a high temperature environment, this may indicate that the inverter's cooling system needs to be optimized.

[0093] S43: The preset energy storage deviation analysis algorithm model analyzes the environmental energy storage deviation data set based on a machine self-learning algorithm to generate a loss factor, where the loss factor is used to calculate the energy loss when the inverter is transmitted to the energy storage device;

[0094] Specifically, using a machine self-learning algorithm, the system analyzes the environmental energy storage deviation data set to generate a loss factor. For example, if the analysis results show that the efficiency of the inverter decreases at a specific temperature, the system will adjust the inverter's operating parameters to reduce energy loss. The loss factor helps quantify the energy loss of the inverter under different environmental conditions, allowing operators to more accurately calculate the energy loss when the inverter is transmitted to the energy storage device and take corresponding optimization measures.

[0095] In S80: the overall power demand data is used to determine whether the overall power demand is met based on the expected energy storage data, and a power supply allocation strategy is generated based on the determination result. The power supply allocation strategy is used to regulate the distributed photovoltaic energy storage to meet the power demand. The steps include the following:

[0096] S81: Analyze the electricity demand and the status of the photovoltaic storage equipment in each area of ​​the distributed photovoltaic storage energy according to the overall electricity demand data;

[0097] S82: comparing the expected energy storage data corresponding to each region in the expected energy storage data with the electricity demand of each region to evaluate the energy supply and demand balance state of each region;

[0098] S83: If the energy supply in a certain area does not meet its demand, a power supply allocation strategy is generated to adjust the energy supply and demand balance.

[0099] The power distribution strategy includes: adjusting the output of photovoltaic storage equipment in adjacent areas to compensate for the energy gap in areas with imbalanced supply and demand;

[0100] Optimize the charging and discharging strategies of energy storage equipment to support inter-regional dispatch of energy;

[0101] Adjusting the operating parameters of photovoltaic modules to improve energy conversion efficiency;

[0102] It should be noted that a user interface is provided, which is used to display the predicted photovoltaic power generation output power, the charging and discharging status and control strategy of the energy storage system, and allows the user to adjust the control strategy according to the power usage habits.

[0103] A specific example is given for steps S81-S83: Assume that a distributed photovoltaic storage system covers commercial areas, residential areas, and industrial areas. By collecting data, the system finds that commercial areas have higher electricity demand during the day, while residential areas use more electricity at night, and industrial areas have stable electricity demand throughout the day. At the same time, the system also monitors that the efficiency of photovoltaic modules in commercial areas has decreased due to dirt and the remaining power of energy storage equipment is also low. By analyzing the electricity demand and equipment status of each area, the system can identify potential energy supply problems in advance and provide data support for the next step of supply and demand balance assessment and strategy formulation.

[0104] Based on the high electricity demand and low photovoltaic power generation efficiency in the commercial area during the day, the system predicts that the energy storage data in this area during the day cannot meet the electricity demand. However, the expected energy storage data in residential and industrial areas matches the electricity demand. The evaluation of the supply and demand balance helps the system understand which areas need additional energy support, so as to formulate targeted power supply distribution strategies.

[0105] Develop power distribution strategy:

[0106] Adjust the output of solar-storage equipment in neighboring areas: The system decides to deploy excess energy storage in residential areas to commercial areas during the day to compensate for the energy gap in commercial areas.

[0107] Optimize the charging and discharging strategy of energy storage equipment: To support inter-regional dispatch of energy, the system charges energy storage equipment in commercial areas during low-consumption periods and discharges it during peak hours.

[0108] Adjust the operating parameters of photovoltaic modules: The system remotely controls the tilt angle of photovoltaic modules in commercial areas to maximize light reception, and activates the automatic cleaning system to remove dirt and improve energy conversion efficiency.

[0109] In summary, this application can achieve refined management of distributed photovoltaic energy storage systems, ensure the stability and reliability of energy supply, and optimize the energy utilization efficiency and economy of the system. This method is particularly suitable for modern power grids with constantly changing energy demand and supply conditions, and provides an effective solution for realizing smart grids and sustainable energy management.

[0110] After the step of S80: obtaining the overall power demand data and judging whether the overall power demand is met according to the expected energy storage data, the following steps are included:

[0111] S91: Acquire historical electricity demand data, and construct an electricity usage habit profile based on the historical electricity demand data;

[0112] S92: Analyze the power usage habit profile to determine the user's power usage pattern and preference;

[0113] S93: predicting the user's electricity demand based on the electricity consumption pattern and preference;

[0114] S94: Generate a personalized control strategy based on the expected energy storage data and the predicted electricity demand, wherein the personalized control strategy is used to adjust the operating status of the photovoltaic power generation system and the energy storage system to meet the user's electricity demand and optimize energy utilization efficiency.

[0115] Specifically, for steps S91-S94, the embodiment of the present application takes as an example: the system collects the monthly electricity consumption data of a residential user in the past year, including the daily peak electricity consumption period, the power consumption of commonly used electrical appliances, and seasonal electricity consumption changes. Through these data, the system constructs a profile of the user's electricity usage habits, which includes: the user's electricity usage period, the user's electricity usage frequency, the user's electricity equipment type, and the user's electricity usage stability.

[0116] The analysis found that users in a certain commercial district have a high demand for electricity from 9 am to 5 pm on weekdays, mainly for office equipment and air conditioning systems; while at night and on weekends, the demand for electricity drops significantly. By analyzing the electricity usage habit profile, the system can determine the user's electricity usage pattern and preference, providing support for further demand forecasting and power supply strategy adjustment. Based on the electricity usage habit profile of users in the commercial district, the system predicts that the electricity demand in the area will follow a similar pattern on the upcoming weekdays, especially during the daytime. Accurately predicting the user's electricity demand helps the system prepare and adjust resources in advance to ensure sufficient energy supply during peak hours.

[0117] Generate personalized control strategies:

[0118] Adjusting the photovoltaic power generation system: The system predicts upcoming sunny weather and high electricity demand and automatically adjusts the tilt angle of the photovoltaic panels to maximize solar energy capture.

[0119] Adjust the energy storage system: The system anticipates that the energy storage equipment will need to release more energy during peak electricity consumption periods, so the energy storage equipment is charged in advance at night and during off-peak hours.

[0120] Optimize power usage: The system automatically reduces the power consumption of non-essential appliances during peak hours based on user preferences, while ensuring power supply to critical equipment.

[0121] To sum up, this application realizes personalized management of distributed photovoltaic energy storage systems, improves the flexibility of energy supply and user satisfaction. The personalized control strategy can not only meet the user's electricity needs, but also optimize energy utilization efficiency, reduce energy waste, and may reduce the user's electricity bill expenses. In addition, this method also helps to improve the operating efficiency and economy of the entire energy system, and provides an effective solution for realizing smart grids and sustainable energy management.

[0122] Furthermore, the method further includes the following steps: acquiring position distribution data of all photovoltaic modules, constructing a photovoltaic position three-dimensional model according to the position distribution data, and performing distribution marking on the photovoltaic position three-dimensional model based on a preset zoning standard;

[0123] In this step, GIS technology is used to collect the location distribution data of all photovoltaic modules, including latitude and longitude, installation angle, direction and other information. With these data, a detailed photovoltaic location three-dimensional model is constructed, which can accurately reflect the spatial location and characteristics of each photovoltaic module. The photovoltaic location three-dimensional model helps to more accurately predict the power generation of each photovoltaic module, taking into account factors such as terrain and shading, and provides strong spatial data support for subsequent energy management and scheduling.

[0124] Generate regionalized electricity demand data based on the electricity consumption habit portrait, divide the electricity demand into different levels according to a preset electricity demand level threshold, and mark the electricity demand level of the photovoltaic location three-dimensional model based on the regionalized electricity demand data;

[0125] Specifically, different levels of electricity demand correspond to the frequency adjustment requirements of photovoltaic modules, which are used by operators to detect and adjust different frequencies of photovoltaic modules according to the frequency adjustment requirements;

[0126] Specifically, the system generates regionalized electricity demand data by combining electricity consumption habit profiles and historical electricity consumption data. For example, by analyzing the electricity consumption pattern of an industrial park and determining the electricity demand level in different time periods, regionalized electricity demand data can help the system understand the electricity consumption characteristics of each region in more detail and provide a basis for formulating more accurate power supply allocation strategies.

[0127] Based on the preset power demand level threshold, the system divides power demand into high, medium, and low levels. For example, the power demand in residential areas at night may be marked as "high", while the power demand in commercial areas during the day may also be marked as "high". The power demand level marking enables the system to prioritize power resources according to the urgency and importance of power demand, ensuring that the power demand in key areas is met.

[0128] The system automatically adjusts the operating parameters of the PV modules according to the level of power demand. For example, for residential areas marked as "high", the system will increase the monitoring and adjustment frequency of the PV modules in the area to ensure that sufficient power can be provided during peak power demand periods. By adjusting the detection and adjustment frequency of the PV modules, operators can more effectively manage the output of the PV modules, optimize the allocation of power resources, and improve the response speed and operating efficiency of the entire system.

[0129] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0130] In one embodiment, a control device for distributed photovoltaic energy storage is provided, and the control device for distributed photovoltaic energy storage corresponds to the control method for distributed photovoltaic energy storage in the above embodiment. Figure 2 As shown, the control device for distributed photovoltaic energy storage includes an effective illumination data set construction unit 1, which is used to obtain the effective illumination data received by all photovoltaic components, and record the historical effective illumination data according to a preset common time axis to construct an effective illumination data set, wherein the effective illumination data refers to the illumination data received by the photovoltaic components and used for energy conversion;

[0131] An environmental impact data set construction unit 2 is used to obtain environmental data corresponding to all photovoltaic modules according to a preset sampling standard and input the environmental data into the effective light data set, and associate the environmental data with the effective light data according to the common time axis to construct an environmental impact data set;

[0132] An environmental impact factor generating unit 3 is used to pre-set an ambient light impact analysis algorithm model to generate environmental impact factors corresponding to different photovoltaic components based on the environmental impact data set based on a machine self-learning algorithm, and the environmental impact factors are used to predict the effective conversion rate of photovoltaic components to light under different environments;

[0133] A light energy conversion data set construction unit 4 is used to obtain the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associate the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set;

[0134] A conversion efficiency factor generating unit 5, which is pre-set with a conversion efficiency analysis algorithm model to analyze the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor, wherein the conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device;

[0135] A predicted effective illumination data generating unit 6, configured to obtain predicted environmental data and generate predicted effective illumination data according to the predicted environmental data and the environmental influencing factors;

[0136] An expected energy storage data calculation unit 7, used to calculate expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used to supply power to the electrical equipment;

[0137] The power supply allocation strategy generation unit 8 is used to obtain the overall power demand data and determine whether the overall power demand is met based on the expected energy storage data, and generate a power supply allocation strategy based on the judgment result. The power supply allocation strategy is used to regulate distributed photovoltaic energy to meet the power demand.

[0138] For the specific definition of the control device for distributed photovoltaic energy storage, please refer to the definition of the control method for distributed photovoltaic energy storage mentioned above, which will not be repeated here. Each module in the above-mentioned control device for distributed photovoltaic energy storage can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0139] In one embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store a database. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a control method for distributed photovoltaic energy storage is implemented.

[0140] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0141] Acquire effective light data received by all photovoltaic modules, and record the historical effective light data according to a preset common time axis to construct an effective light data set, wherein the effective light data refers to light data received by the photovoltaic modules and used for energy conversion;

[0142] Acquire environmental data corresponding to all photovoltaic modules according to a preset sampling standard and input the data into the effective light data set, and associate the environmental data with the effective light data according to the common time axis to construct an environmental impact data set;

[0143] The preset ambient light impact analysis algorithm model generates environmental impact factors corresponding to different photovoltaic modules based on the environmental impact data set by a machine self-learning algorithm, and the environmental impact factors are used to predict the effective conversion rate of photovoltaic modules to light under different environments;

[0144] Acquire the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associate the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set;

[0145] The preset conversion efficiency analysis algorithm model analyzes the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor, wherein the conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device;

[0146] Acquire predicted environmental data and generate predicted effective lighting data according to the predicted environmental data and the environmental influencing factors;

[0147] Calculating expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used to supply power to the electrical equipment;

[0148] The overall electricity demand data is obtained and whether the overall electricity demand is met is determined based on the expected energy storage data, and a power supply allocation strategy is generated based on the determination result, wherein the power supply allocation strategy is used to regulate distributed photovoltaic energy storage to meet the electricity demand.

[0149] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0150] Acquire effective light data received by all photovoltaic modules, and record the historical effective light data according to a preset common time axis to construct an effective light data set, wherein the effective light data refers to light data received by the photovoltaic modules and used for energy conversion;

[0151] Acquire environmental data corresponding to all photovoltaic modules according to a preset sampling standard and input the data into the effective light data set, and associate the environmental data with the effective light data according to the common time axis to construct an environmental impact data set;

[0152] The preset ambient light impact analysis algorithm model generates environmental impact factors corresponding to different photovoltaic modules based on the environmental impact data set by a machine self-learning algorithm, and the environmental impact factors are used to predict the effective conversion rate of photovoltaic modules to light under different environments;

[0153] Acquire the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associate the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set;

[0154] The preset conversion efficiency analysis algorithm model analyzes the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor, wherein the conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device;

[0155] Acquire predicted environmental data and generate predicted effective lighting data according to the predicted environmental data and the environmental influencing factors;

[0156] Calculating expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used to supply power to the electrical equipment;

[0157] The overall electricity demand data is obtained and whether the overall electricity demand is met is determined based on the expected energy storage data, and a power supply allocation strategy is generated based on the determination result, wherein the power supply allocation strategy is used to regulate distributed photovoltaic energy storage to meet the electricity demand.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0159] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0160] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A control method for distributed photovoltaic energy storage, characterized in that: The method comprises the steps of: acquiring effective illumination data received by all photovoltaic modules, and recording the historical effective illumination data according to a preset common time axis to construct an effective illumination data set, wherein the effective illumination data refers to illumination data received by the photovoltaic modules and used for energy conversion; Acquire environmental data corresponding to all photovoltaic modules according to a preset sampling standard and input the data into the effective light data set, and associate the environmental data with the effective light data according to the common time axis to construct an environmental impact data set; The preset ambient light impact analysis algorithm model generates environmental impact factors corresponding to different photovoltaic modules based on the environmental impact data set by a machine self-learning algorithm, and the environmental impact factors are used to predict the effective conversion rate of photovoltaic modules to light under different environments; Acquire the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associate the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set; The preset conversion efficiency analysis algorithm model analyzes the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor, wherein the conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device; Acquire predicted environmental data and generate predicted effective lighting data according to the predicted environmental data and the environmental influencing factors; Calculating expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used to supply power to the electrical equipment; The overall electricity demand data is obtained and whether the overall electricity demand is met is determined based on the expected energy storage data, and a power supply allocation strategy is generated based on the determination result, wherein the power supply allocation strategy is used to regulate distributed photovoltaic energy storage to meet the electricity demand.

2. A control method for distributed photovoltaic energy storage according to claim 1, characterized in that: In the overall power demand data and according to the expected energy storage data, it is judged whether the overall power demand is met, and a power supply allocation strategy is generated according to the judgment result. The power supply allocation strategy is used to regulate the distributed photovoltaic energy storage to meet the power demand. In this step, the steps include the following: Analyze the electricity demand and the status of the photovoltaic storage equipment in each area of ​​the distributed photovoltaic energy storage system based on the overall electricity demand data; Comparing the expected energy storage data corresponding to each region in the expected energy storage data with the electricity demand of each region to evaluate the energy supply and demand balance state of each region; If the energy supply in a certain area does not meet its demand, a power supply allocation strategy is generated to adjust the balance between energy supply and demand. The power distribution strategy includes: adjusting the output of photovoltaic storage equipment in adjacent areas to compensate for the energy gap in areas with imbalanced supply and demand; Optimize the charging and discharging strategies of energy storage equipment to support inter-regional dispatch of energy; Adjust the operating parameters of photovoltaic modules to improve energy conversion efficiency.

3. A control method for distributed photovoltaic energy storage according to claim 1, characterized in that: After the step of obtaining the overall power demand data and judging whether the overall power demand is met according to the expected energy storage data, the following steps are included: Obtaining historical electricity demand data, and constructing an electricity usage habit profile based on the historical electricity demand data; Analyze the electricity usage habit profile to determine the user's electricity usage pattern and preference; predicting the user's electricity demand based on the electricity usage pattern and preference; According to the expected energy storage data and the predicted electricity demand, a personalized control strategy is generated, and the personalized control strategy is used to adjust the operating status of the photovoltaic power generation system and the energy storage system to meet the user's electricity demand and optimize energy utilization efficiency.

4. A control method for distributed photovoltaic energy storage according to claim 3, characterized in that: The method further includes the following steps: obtaining location distribution data of all photovoltaic components, constructing a photovoltaic location three-dimensional model according to the location distribution data, and performing distribution marking on the photovoltaic location three-dimensional model based on a preset zoning standard; Generate regionalized electricity demand data based on the electricity consumption habit portrait, divide the electricity demand into different levels according to a preset electricity demand level threshold, and mark the electricity demand level of the photovoltaic location three-dimensional model based on the regionalized electricity demand data; Specifically, different levels of electricity demand correspond to the frequency regulation requirements of photovoltaic modules, which are used by operating personnel to detect and adjust different frequencies of photovoltaic modules according to the frequency regulation requirements.

5. A control method for distributed photovoltaic energy storage according to claim 1, characterized in that: After obtaining the historical energy conversion data of all inverters and the historical input data of the energy storage device, and correlating the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set, the following steps are included: Obtain historical output data of the inverter, and calculate corresponding energy storage power deviation data based on historical input data of the energy storage device and historical output data of the inverter; Associating the energy storage power deviation data with the environmental data based on the common time axis to construct an environmental energy storage deviation data set; The preset energy storage deviation analysis algorithm model analyzes the environmental energy storage deviation data set based on a machine self-learning algorithm to generate a loss factor, which is used to calculate the energy loss when the inverter is transmitted to the energy storage device.

6. A control method for distributed photovoltaic energy storage according to claim 1, characterized in that: A user interface is provided, which is used to display the predicted photovoltaic power generation output power, the charging and discharging status of the energy storage system and the control strategy, and allows the user to adjust the control strategy according to the electricity usage habits.

7. A control method for distributed photovoltaic energy storage according to claim 1, characterized in that: The machine self-learning algorithm includes a neural network algorithm.

8. A control device for distributed photovoltaic energy storage, applied to a control method for distributed photovoltaic energy storage as claimed in any one of claims 1 to 7, characterized in that: The device comprises an effective illumination data set construction unit, which is used to obtain effective illumination data received by all photovoltaic modules, and record the historical effective illumination data according to a preset common time axis to construct an effective illumination data set, wherein the effective illumination data refers to the illumination data received by the photovoltaic modules and used for energy conversion; An environmental impact data set construction unit (2) is used to obtain environmental data corresponding to all photovoltaic modules according to a preset sampling standard and input the environmental data into the effective light data set, and associate the environmental data with the effective light data according to the common time axis to construct the environmental impact data set; An environmental impact factor generating unit (3) is used to pre-set an environmental light impact analysis algorithm model to generate environmental impact factors corresponding to different photovoltaic components based on the environmental impact data set based on a machine self-learning algorithm, wherein the environmental impact factors are used to predict the effective conversion rate of photovoltaic components to light under different environments; A light energy conversion data set construction unit (4) is used to obtain the historical energy conversion data of all inverters and the historical input data of the energy storage device, and associate the historical energy conversion data and the historical input data of the energy storage device based on the common time axis record to construct a light energy conversion data set; A conversion efficiency factor generating unit (5) is used to pre-set a conversion efficiency analysis algorithm model to analyze the light energy conversion data set based on a machine self-learning algorithm to generate a conversion efficiency factor, wherein the conversion efficiency factor is used to calculate the effective electric energy input from the inverter to the energy storage device; A predicted effective illumination data generating unit (6), used for acquiring predicted environmental data and generating predicted effective illumination data according to the predicted environmental data and the environmental influencing factors; An expected energy storage data calculation unit (7), used for calculating expected energy storage data according to the predicted effective illumination data and the conversion efficiency factor, wherein the energy storage data is used for supplying power to the electrical equipment; A power supply allocation strategy generating unit (8) is used to obtain overall power demand data and determine whether the overall power demand is met based on the expected energy storage data, and generate a power supply allocation strategy based on the determination result, wherein the power supply allocation strategy is used to regulate distributed photovoltaic energy storage to meet the power demand.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a control method for distributed photovoltaic energy storage as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a control method for distributed photovoltaic energy storage as described in any one of claims 1 to 7 are implemented.

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