A photovoltaic power generation capacity intelligent control method, server, medium and program product

Abstract

The application provides a photovoltaic power generation intelligent control method, a server, a medium and a program product, and relates to the field of photovoltaic power generation control. The method comprises the following steps: acquiring real-time total power generation data of each photovoltaic panel on a roof; if the preset power consumption threshold upper limit of the current period is exceeded, calculating a to-be-reduced power generation value; after acquiring the position information and the current illumination data of each photovoltaic panel, determining the expected power generation efficiency of each photovoltaic panel, sorting the corresponding photovoltaic panels from high to low, and determining the power of each photovoltaic panel according to the sorting result; determining the last photovoltaic panel in the sorting result as the first to-be-closed photovoltaic panel; continuing to calculate the power of the photovoltaic panel from the back of the sorting result to the front multiple times until the to-be-reduced power generation value is met, and determining multiple to-be-closed photovoltaic panels; and controlling the multiple to-be-closed photovoltaic panels to stop working. Through the method, the power generation is accurately regulated and controlled, the energy waste is reduced, and the energy utilization efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic power generation control, and particularly relates to a photovoltaic power generation intelligent control method, a server, a medium and a program product. BACKGROUND

[0002] With the urgent demand for clean energy worldwide, photovoltaic power generation, as an important renewable energy, has ushered in an opportunity for rapid development. Roof photovoltaic power generation systems have been widely used due to their advantages in space utilization and energy supply.

[0003] Currently, the traditional method is usually to arrange special personnel to regularly check the instruments or monitoring equipment on the photovoltaic power generation equipment to obtain the data of the photovoltaic power generation. When these personnel or through monitoring instruments find that the power generation exceeds the current set threshold, the management personnel can go to the location of the photovoltaic panel and adjust the working state of the photovoltaic panel by manually operating the switch or controller.

[0004] However, the existing photovoltaic power generation control method often has difficulty in accurately controlling the power generation, which leads to difficulty in timely adjustment when the power generation exceeds, and thus may cause energy waste or even equipment damage. SUMMARY

[0005] The present application provides a photovoltaic power generation intelligent control method, a server, a medium and a program product, which are used to realize efficient, intelligent and accurate control of photovoltaic power generation in the case that the photovoltaic power generation control in the prior art is not accurate enough and difficult to efficiently adjust according to real-time conditions.

[0006] In a first aspect, the present application provides a photovoltaic power generation intelligent control method applied to a server of a management system, which comprises: acquiring real-time total power generation data of each photovoltaic panel on the roof through a power generation monitoring module; if the real-time total power generation data exceeds the upper limit of the preset power consumption threshold in the current period, calculating a to-be-reduced power generation value; after acquiring the position information and the current illumination data of each photovoltaic panel, determining the predicted power generation efficiency of each photovoltaic panel; sorting the corresponding photovoltaic panels from high to low according to the predicted power generation efficiency to obtain a sorting result; determining the power of each photovoltaic panel from back to front according to the sorting result until the sum of the total power is greater than or equal to the to-be-reduced power generation value; determining the last photovoltaic panel in the sorting result as the first to-be-shut-down photovoltaic panel; continuing to calculate the power of the photovoltaic panel from back to front in the sorting result multiple times until the to-be-reduced power generation value is satisfied, and determining multiple to-be-shut-down photovoltaic panels; and controlling the first to-be-shut-down photovoltaic panel and the multiple to-be-shut-down photovoltaic panels to stop working.

[0007] By adopting the technical scheme, the management system can obtain the total power generation data of each photovoltaic panel on the roof in real time. When the power consumption threshold exceeds the upper limit, the power generation reduction value is calculated, and the expected power generation efficiency is determined according to the position and illumination of the photovoltaic panel to sort the photovoltaic panels. Thus, the photovoltaic panel to be closed can be accurately determined. This method can effectively avoid energy waste caused by excessive power generation and accurately control the power generation of the photovoltaic panel. At the same time, the working state of the photovoltaic panel can be flexibly adjusted according to actual needs, ensuring the stability and rationality of power supply, reducing system operation cost, and improving energy utilization efficiency.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after the step of obtaining the real-time total power generation data of each photovoltaic panel on the roof by the power generation monitoring module, the method further comprises: determining a power generation prediction model according to the historical power generation data and corresponding historical weather information after obtaining the historical power generation data of each photovoltaic panel; inputting the weather forecast information within a future set time from the current time and the power generation of each photovoltaic panel into the power generation prediction model to obtain the predicted power generation of each photovoltaic panel in the future set time; determining the total predicted power generation in the future period of time according to the sum of the predicted power generation of all photovoltaic panels; and adjusting the working state of each photovoltaic panel in advance according to the predicted total power generation and the preset power consumption threshold.

[0009] By adopting the technical scheme, the historical power generation data and historical weather information are used to establish a power generation prediction model. After obtaining the future weather forecast and the power generation of each photovoltaic panel, the predicted power generation in the future set time can be predicted, and then the total predicted power generation is determined. The working state of each photovoltaic panel is adjusted in advance according to the total predicted power generation, which realizes more accurate energy management. The imbalance between power supply and demand is effectively reduced, the reliability of power supply is improved, and the energy distribution strategy is optimized, thereby reducing energy waste and operation cost.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the last photovoltaic panel in the sorting result as the first photovoltaic panel to be closed, the method further comprises: determining the real-time temperature of each photovoltaic panel by a temperature monitoring device; judging whether the real-time temperature of each photovoltaic panel is higher than a preset temperature upper limit; and determining the corresponding photovoltaic panel as the photovoltaic panel to be closed if it is judged that the real-time temperature is higher than the temperature upper limit.

[0011] By adopting the technical scheme, the photovoltaic panel can be effectively prevented from being damaged due to high-temperature operation, and the service life of the photovoltaic panel is prolonged. At the same time, the decline in power generation efficiency and safety hazards caused by overheating are avoided, the stable operation of the photovoltaic power generation system is ensured, and the maintenance cost and failure rate are reduced.

[0012] In some embodiments in combination with the first aspect, in some embodiments, if the real-time total power generation data exceeds the preset power consumption threshold upper limit of the current period, before the step of calculating the to-be-reduced power generation value, the method further comprises: obtaining resident power consumption data of different time periods in a historical setting time period of the building area; determining a predicted power consumption situation of a future time period according to the resident power consumption data; and determining preset power consumption thresholds of different time periods according to the predicted power consumption situation.

[0013] By adopting the above technical solution, the resident power consumption data of different time periods in a historical setting time period of the building area is obtained, so as to determine a predicted power consumption situation of a future time period, and the preset power consumption thresholds of different time periods are set accordingly. This makes the setting of the power consumption threshold more scientific and reasonable, and can better adapt to the changes in actual power consumption demand. The situation of insufficient or excessive power supply is effectively avoided, the efficiency and economy of energy utilization are improved, and the stability and reliability of resident power consumption are ensured.

[0014] In some embodiments in combination with the first aspect, in some embodiments, the management system comprises a wireless sensor for detecting the power generation of the photovoltaic panel. After the step of obtaining, by the power generation monitoring module, the real-time total power generation data of each photovoltaic panel on the roof, if the real-time total power generation data is lower than the preset power consumption threshold lower limit of the current period, the method further comprises: sending an alarm information to the management end to prompt the user that the current power generation is abnormal.

[0015] By adopting the above technical solution, when the real-time total power generation data is lower than the preset power consumption threshold lower limit, an alarm information is sent to the management end to timely prompt the user that the current power generation is abnormal. This helps the user to discover problems in time and take corresponding measures to avoid the impact of insufficient power generation on normal power consumption. The fault monitoring capability of the system is improved, the stability and reliability of the system are enhanced, and the power consumption experience of the user is ensured.

[0016] In some embodiments in combination with the first aspect, in some embodiments, after the step of obtaining, by the power generation monitoring module, the real-time total power generation data of each photovoltaic panel on the roof, if the real-time total power generation data is higher than the preset power consumption threshold upper limit of the current period, the method further comprises: determining the storable power generation of the excess part; and sending a control instruction to the storage battery to make the storable power generation enter the storage battery for charging and storage.

[0017] By adopting the above technical solution, when the real-time total power generation data is higher than the preset power consumption threshold upper limit, the storable power generation is determined and stored in the storage battery. The effective storage and utilization of energy are realized, and the comprehensive utilization efficiency of energy is improved. While ensuring power supply, reserves are provided for coping with sudden power consumption demand, the dependence on external power grid is reduced, and the autonomy and stability of the power system are enhanced.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after controlling the multiple photovoltaic panels to be shut down to stop working, the method further includes: periodically monitoring the real-time power generation efficiency of all photovoltaic panels at preset time intervals; comparing the real-time power generation efficiency of each photovoltaic panel to determine the power generation efficiency ranking result among the photovoltaic panels; determining whether the power generation efficiency ranking result matches the current operating status of each photovoltaic panel; if they do not match, the server determines that the photovoltaic panel with high power generation efficiency and currently not running is the photovoltaic panel to be started, and determines that the photovoltaic panel with low power generation efficiency and currently running is the photovoltaic panel to be stopped; and readjusting the operating status of each photovoltaic panel according to the photovoltaic panel to be stopped and the photovoltaic panel to be started, so that the operating status matches the real-time power generation efficiency ranking result.

[0019] By adopting the above technical solutions, it is possible to maintain the high-efficiency operation of photovoltaic panels and maximize power generation efficiency. Timely optimization of the system's operating status ensures the stability and reliability of power output, while also extending the overall lifespan of the photovoltaic panels and reducing operation and maintenance costs.

[0020] In a second aspect, this application provides a server comprising: one or more processors and a memory; the memory being coupled to the one or more processors, the memory being used to store computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the server to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a server, cause the server to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product that, when run on a server, causes the server to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By adopting the technical means of acquiring the total power generation data of each photovoltaic panel on the roof in real time, and determining the expected power generation efficiency based on location and illuminance to sort the photovoltaic panels to be shut down, the problem of inaccurate control of photovoltaic power generation in the existing technology is effectively solved. This achieves precise control of photovoltaic power generation, avoids energy waste, and ensures the stability and rationality of power supply.

[0025] 2. By adopting a power generation prediction model based on historical power generation data and historical weather information, and combining it with future weather forecasts and power generation prediction techniques, the technology effectively solves the problem of accurately predicting photovoltaic power generation in existing technologies. This enables the technology to adjust the working status of photovoltaic panels in advance, improve the reliability of power supply, and optimize energy distribution strategies.

[0026] 3. By adopting the technical means of obtaining residential electricity consumption data of different time periods in the building area to determine the predicted electricity consumption, and then setting preset electricity consumption thresholds for different time periods, the problem of unreasonable electricity consumption threshold settings in the existing technology is effectively solved. This achieves the technical effect of making the electricity consumption thresholds more scientific and adaptable to actual needs, avoiding power supply and demand imbalance, and improving energy utilization efficiency and economy. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating an intelligent control method for photovoltaic power generation in an embodiment of this application.

[0028] Figure 2 This is another flowchart illustrating the intelligent control method for photovoltaic power generation in the embodiments of this application;

[0029] Figure 3 This is a schematic diagram of the physical device structure of a server in an embodiment of this application. Detailed Implementation

[0030] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0032] To facilitate understanding, an application scenario of the relevant method is introduced below.

[0033] A company has installed 100 photovoltaic (PV) panels on its roof. Specific technicians monitor these panels at set times, checking the power generation meter readings on each panel. These readings need to be recorded and summarized by the technicians, or the total power generation of the entire system can be obtained directly through a central management device. During the day, if the total power generation exceeds the preset daytime power consumption limit, the technicians can check the location of each PV panel and then manually disconnect some of them, or control the PV panels to shut down via a control panel, until the total power generation drops below the threshold.

[0034] It is evident that in related technologies, it is difficult to select which photovoltaic panels to shut down based on the real-time efficiency of each panel. This may result in shutting down more efficient panels, and when the light intensity changes, it is difficult to quickly reassess the power generation efficiency of each panel, leading to the system being in a low-efficiency state for an extended period.

[0035] The intelligent photovoltaic power generation control method described in this application monitors the power generation of each photovoltaic panel in real time. When the total power generation exceeds a threshold, the system automatically determines which photovoltaic panels to be shut down based on their power generation efficiency, achieving precise control over power generation and avoiding energy waste caused by excessive power generation. The following describes scenarios where the intelligent photovoltaic power generation control method of this application is used:

[0036] Continuing the previous example, 100 photovoltaic (PV) panels are installed on the roof, each connected to a smart PV power generation control system. The system's server monitors the power generation of each panel in real time using monitoring equipment. When the system detects that the total power generation is about to exceed the current time period's threshold, it calculates the amount of power that needs to be reduced. The server then obtains the location and real-time illuminance of each PV panel, estimates the power generation efficiency of each panel based on the differences in location and illuminance, and sorts the PV panels from high to low efficiency. The server then determines the power output of the PV panels starting from the end of the sorted list until the total determined power output meets the required power reduction. Finally, the server automatically shuts down the PV panels at the end of the sorted list, precisely controlling power generation and preventing waste.

[0037] To facilitate understanding, the method provided in this implementation will be described in detail below, using the above scenario as an example. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating an intelligent control method for photovoltaic power generation in an embodiment of this application.

[0038] S101. The real-time total power generation data of each photovoltaic panel on the roof is obtained through the power generation monitoring module.

[0039] Specifically, the server establishes a communication connection with the power generation monitoring module, which contains sensors to detect the power generation of each photovoltaic panel. These sensors can be current and voltage detection devices connected to each photovoltaic panel, capable of acquiring the output current and voltage parameters of the photovoltaic panel in real time. The power generation monitoring module uses the data from these sensors to calculate the power generation of each photovoltaic panel at the current moment. Then, it sends the real-time power generation data of each photovoltaic panel to the server via a wired or wireless network.

[0040] The server receives real-time power generation data from each photovoltaic (PV) panel in the power generation monitoring module. The server can establish a PV panel power generation database, recording the ID of each PV panel and its corresponding data. This database can be implemented using a relational or non-relational database. Upon receiving real-time power generation data, the server updates the corresponding data items for each PV panel in the database. Furthermore, the server needs to ensure that it can continuously acquire PV panel power generation data in real-time. If data is not received from a particular PV panel for an extended period, the server can determine that the monitoring equipment for that panel has malfunctioned and can send an alert to the administrator, prompting them to check the relevant sensors.

[0041] For example, in a practical application, a rooftop might have 100 photovoltaic (PV) panels, each with a unique ID and equipped with a power generation detection sensor. Every minute, the sensor transmits the detected current and voltage values ​​to a server via a WiFi network. The server's power generation monitoring module calculates the power generation of each PV panel corresponding to that ID within one minute. The server compares the latest received power generation data from the 100 PV panels with records in its database to update the real-time total power generation value. If a particular ID fails to receive data for five consecutive times, the server determines that the PV panel monitoring device for that ID may be malfunctioning and sends an alert to the administrator via SMS.

[0042] S102. If the real-time total power generation data exceeds the upper limit of the preset power consumption threshold for the current period, calculate the power generation to be reduced.

[0043] After acquiring real-time power generation data from all photovoltaic panels, the server needs to determine whether the current total power generation exceeds the set upper limit of the power consumption threshold. This upper limit refers to the maximum power output allowed by the photovoltaic power generation system within the current time period. This threshold can be set according to actual conditions, such as based on peak electricity consumption periods, total electricity consumption limits, and energy storage capacity. The threshold can also be set in conjunction with historical power usage data for each time period and user electricity plans for the day. The upper limit can be set to a fixed value or a dynamically changing range.

[0044] The server needs to determine the current time period and query the corresponding preset power consumption threshold limit. Then, it compares the real-time total power generation data with this threshold limit. If the real-time total power generation exceeds the threshold limit, the server calculates the excess power generation to be reduced, which is the difference between the real-time total power generation and the threshold limit. For example, 7 PM to 9 PM is a peak power consumption period, and the preset power consumption threshold limit for this period is 8 kilowatts. The monitoring module's real-time total power generation is 10 kilowatts. In this case, the server will subtract 8 kilowatts from 10 kilowatts to calculate a power generation reduction of 2 kilowatts.

[0045] After obtaining the value of power generation to be reduced, the server needs to determine the photovoltaic panels that should stop working in subsequent steps in order to control the total power generation below the upper limit of the threshold.

[0046] If the real-time total power generation data is lower than the preset lower limit of the power consumption threshold for the current period, an alarm message will be sent to the management terminal to notify the management personnel that the current power generation is abnormal so that they can take appropriate follow-up actions.

[0047] In some embodiments, if the total power generation exceeds a threshold, the difference is calculated as the storable amount of electricity. The server connects to the battery management system to obtain the current stored capacity and remaining capacity of the battery. If the remaining battery capacity is sufficient to hold the storable electricity, the server sends a storage control command to the battery management system, containing the amount of electricity to be stored. Upon receiving the command, the battery management system automatically controls the battery to charge until the stored capacity reaches the amount sent by the server. The battery management system then reports the charging status to the server, indicating that storage is complete. After receiving the feedback, the server records details of storing the excess power into the battery, such as the amount of stored electricity and the time. In this way, the server can intelligently determine when there is excess power generation and arrange for the battery to store the excess electricity, avoiding waste and making the photovoltaic system operate more efficiently.

[0048] In some embodiments, the preset electricity consumption threshold can be determined as follows: The server needs to acquire electricity consumption data of residents in the building area within a set historical time range and differentiate it according to different time periods. Specifically, the server needs to connect to the building area's electricity consumption monitoring system. This system can record the electricity consumption of each unit in the area in real time through devices such as smart meters. The server can query and extract electricity consumption data within a specified time range from the monitoring system's database, such as electricity consumption information for the most recent year. Simultaneously, the server needs to process this raw data, classifying and summarizing it according to different time periods. Time periods can be set as peak-valley periods or divided into hourly periods. The total electricity consumption of the building area within each time period is obtained. For example, extracting electricity consumption data from the most recent year and classifying it according to weekday daytime, nighttime, weekend, etc., yields the total electricity consumption for the period from 10:00 AM to 12:00 PM last weekend. After acquiring historical time period electricity consumption data, the server can train a machine learning model based on this data to predict electricity consumption for a future time period. Input features can include historical electricity consumption data for the same time period, temperature, weather, sunshine, holidays, etc. After training, the model can output a predicted electricity consumption value for a given future time period. For example, using a machine learning model, input features include electricity consumption, temperature, and weather conditions from 10 AM to 12 PM on each weekday of the past year. After training the model, it can predict electricity consumption for that time period on the following Monday. Finally, the server can set upper and lower limits for electricity consumption thresholds for that time period based on the prediction results. For example, if the predicted electricity consumption for the following Monday morning is 2000 kilowatts, based on empirical data, the upper limit can be set to 120% of the predicted value, and the lower limit to 80% of the predicted value, i.e., an upper limit of 2400 kilowatts and a lower limit of 1600 kilowatts.

[0049] If real-time electricity consumption exceeds the upper limit or falls below the lower limit, the server can trigger an alarm or adjust the output of the photovoltaic panels to maintain electricity consumption within a reasonable range. This method allows for more intelligent threshold setting, improving the accuracy of electricity usage planning and preventing waste.

[0050] S103. After obtaining the location information and current illuminance data of each photovoltaic panel, determine the expected power generation efficiency of each photovoltaic panel.

[0051] Once the required reduction in power generation is calculated, the server needs to determine which photovoltaic (PV) panels should be shut down. The selection principle prioritizes shutting down less efficient PV panels to ensure the highest overall system efficiency. To achieve this, the server needs to acquire real-time location information for each PV panel and ambient light intensity data. Location information can be obtained from the PV panel installation diagram, showing the specific coordinates of each panel, or from the GPS positioning module within each PV panel; this is not a limitation. Illuminance data can be collected by illuminance sensors placed near the PV panels.

[0052] The server matches location information and illuminance data to each photovoltaic (PV) panel to determine the ambient light conditions for each panel. Depending on the panel's location, the intensity of direct and diffracted sunlight received will vary, resulting in different light conditions. Illuminance directly affects the PV panel's power generation efficiency. Based on the specific location and illuminance data of each PV panel, and referring to its performance parameters, the server calculates the expected power generation efficiency for each panel under the current environment. PV panels with higher power generation efficiency will generate more electricity.

[0053] By acquiring location and illumination data to determine real-time projected efficiency, the server can accurately assess the current output status of each photovoltaic panel, enabling it to more precisely select and stop inefficient photovoltaic panels when controlling the subsequent operation of the control board.

[0054] S104. Based on the expected power generation efficiency, sort the corresponding photovoltaic panels from high to low to obtain the sorting results;

[0055] After determining the expected power generation efficiency of each photovoltaic (PV) panel, the server needs to sort the PV panels according to their efficiency, from highest to lowest. Specifically, the server can establish a PV panel efficiency database, recording the ID of each PV panel and its corresponding expected power generation efficiency. The server extracts the expected power generation efficiencies of all PV panels and forms an efficiency array. Then, it uses a sorting algorithm to sort this array, resulting in the order of the PV panels from highest to lowest efficiency. For example, if there are 5 PV panels with expected power generation efficiencies of 0.15, 0.2, 0.12, 0.18, and 0.11, the server will create an array [0.15, 0.2, 0.12, 0.18, 0.11] and sort it, resulting in the array [0.2, 0.18, 0.15, 0.12, 0.11], which represents the order of PV panel efficiency from highest to lowest.

[0056] In the ranking results, the photovoltaic panels ranked higher have higher power generation efficiency and should be shut down last; the photovoltaic panels ranked lower have lower power generation efficiency and should be shut down first. This provides a basis for determining the shutdown order later.

[0057] S105. Determine the power of each photovoltaic panel sequentially from back to front according to the sorting result until the sum of the total power is greater than or equal to the value of the power generation to be reduced.

[0058] After obtaining the photovoltaic panel efficiency ranking results, the server needs to determine the power of each photovoltaic panel sequentially from back to front according to the ranking results, and then accumulate them until the total power reaches the power generation reduction requirement.

[0059] Specifically, the server can maintain a cumulative total power variable, initialized to 0. Then, starting from the end of the sorted array, it retrieves the ID of each photovoltaic panel, queries the real-time power generation data of that panel based on the ID, which is the power of that panel, and adds it to the total power variable. Photovoltaic panels are retrieved sequentially from the end to the beginning, and the values ​​are added one by one until the total power variable is greater than or equal to the power generation to be reduced.

[0060] By using this incremental approach, a precise list of photovoltaic panels that meet the requirements for reducing power generation can be obtained.

[0061] S106. Determine the last photovoltaic panel in the sorting result as the first photovoltaic panel to be turned off;

[0062] After obtaining the list of photovoltaic (PV) panels that meet the power reduction requirements, the server needs to determine the first PV panel to be shut down, in reverse order. This first panel to be shut down is the last one added in the previous list that caused the total power to exceed the threshold. This method aligns with the principle of determining the shutdown order based on the sorting of efficiency from lowest to highest. Shutting down panels from the back to the front prioritizes shutting down less efficient PV panels, thereby improving the overall power generation efficiency of the system.

[0063] S107. Continue to calculate the photovoltaic panel power from the back of the sorting results multiple times until it is determined that the power generation to be reduced is met, and identify multiple photovoltaic panels to be turned off;

[0064] After the first photovoltaic panel to be shut down is determined, the server needs to continue sorting backwards, sequentially adding the power of each photovoltaic panel until the total power meets the required reduction in power generation. This process identifies the multiple photovoltaic panels that meet the requirement to be shut down. Specifically, the server continues to maintain the previously determined total power variable, adding the power of the first photovoltaic panel to be shut down to the total power. Then, starting from the second-to-last photovoltaic panel in the sorting results, the process of retrieving photovoltaic panels, querying their power, and adding them to the total power is repeated until the total power is greater than or equal to the required reduction in power generation.

[0065] The following example illustrates this: Assume that, after sorting by expected power generation efficiency, the 10 photovoltaic panels are ranked as follows: Panel A > Panel B > Panel C > Panel D > Panel E > Panel F > Panel G > Panel H. The power generation of each panel is as follows: Panel A: 300W, Panel B: 250W, Panel C: 200W, Panel D: 180W, Panel E: 150W, Panel F: 120W, Panel G: 100W, Panel H: 80W. The calculated power generation reduction is 310W. The server then determines the photovoltaic panels to be shut down by adding up the power generation of each panel starting from the end of the sorting result: Panel H (80W), cumulative amount 80W < 310W. Continuing to accumulate: H panel (80W), G panel (100W), cumulative total 80W + 100W = 180W < 310W. Continuing to accumulate: H panel (80W), G panel (100W), F panel (120W), cumulative total 80W + 100W + 120W = 300W < 310W. Continuing to accumulate: H panel (80W), G panel (100W), F panel (120W), E panel (150W), cumulative total 80W + 100W + 120W + 150W = 450W > 310W. Therefore, E panel is identified as the first photovoltaic panel to be shut down. The remaining power generation reduction is 310W - 150W = 160W. Continuing to add from the end: H panel (80W), G panel (100W), the cumulative total is 80W + 100W = 180W > 160W. Therefore, G panel is designated as the second photovoltaic panel to be shut down. Thus, the two photovoltaic panels that meet the requirement of a power generation reduction of 310W are: E panel (150W) and G panel (100W).

[0066] In some embodiments, temperature also affects the operating status of photovoltaic panels. The server can monitor the operating temperature of each photovoltaic panel in real time using temperature monitoring equipment. Specifically, the server can control temperature sensors installed on the back of each photovoltaic panel to acquire real-time temperature data. Temperature sensors can be composed of various sensors such as thermistors, thermocouples, and infrared detectors. These sensors are connected to the server via cables or wirelessly. The server needs to ensure the normal operation of the temperature monitoring equipment and its continuous, uninterrupted reading of the photovoltaic panel's real-time temperature value. The server can set a normal temperature range. If the monitoring equipment for a photovoltaic panel fails to read a temperature for an extended period, the server can determine that the equipment is faulty and send an alarm to the administrator. After monitoring the real-time temperature of each photovoltaic panel, the server needs to determine if any panel is overheating. For this purpose, the server needs to preset a temperature upper limit, which can be determined based on the photovoltaic panel's design parameters and operating environment, such as setting it to 65°C. After acquiring the real-time temperature of each photovoltaic panel, the server will check whether it exceeds 65°C for each panel. Alternatively, a temperature range can be set; any temperature below the lower limit or above the upper limit is considered abnormal.

[0067] For example, the temperature range is set to 20°C to 65°C. When the server receives real-time temperature data of 70°C from photovoltaic panel B, it determines that panel B's temperature has exceeded the preset upper limit of 65°C. During the determination process, if any photovoltaic panel's real-time temperature exceeds the preset upper limit, it indicates a risk of overheating. In this case, the server immediately identifies the panel as a panel to be shut down. Its ID and shutdown control command are added to the processing queue. When controlling the photovoltaic panel to stop working, this panel will be shut down to prevent its temperature from continuing to rise and causing damage. Simultaneously, the server will also send an over-temperature alarm, prompting technicians to check if there is any abnormality in the panel's heat dissipation system. After the temperature drops to the normal range, the panel can be restarted.

[0068] S108, Control the first photovoltaic panel to be shut down and multiple photovoltaic panels to be shut down to stop working.

[0069] The server can store the ID and shutdown control command for each photovoltaic (PV) panel to be shut down. Then, the server sends these control commands sequentially to the corresponding PV panels via wired or wireless network. Upon receiving the shutdown command, the PV panel will stop operating accordingly. If the server can directly connect to the power controller corresponding to the PV panel, it can programmatically control the power controller to disconnect the circuit after identifying the panels to be shut down, without issuing control commands. Regardless of the method used, the server can precisely control the shutdown of the PV panels, keeping power generation within allowable thresholds, ensuring power supply while avoiding waste. Furthermore, the server can record detailed information about the shutdown operation, such as the ID of the PV panel being shut down and the shutdown time. This information can be used as a reference for future system optimization or anomaly handling.

[0070] By employing the intelligent photovoltaic power generation control method in this application embodiment, through real-time monitoring of the power generation of each panel and evaluation of efficiency to determine the shutdown sequence, not only is precise control of photovoltaic power generation achieved, but also energy waste caused by excessive power generation is avoided, ensuring the stability and rationality of power supply.

[0071] In some embodiments, the power generation efficiency of photovoltaic (PV) panels is affected by factors such as weather, humidity, and device aging, exhibiting significant time-varying changes. If not periodically reassessed and adjusted, the operating configuration will gradually deviate from its optimal state. In this case, since PV panel efficiency varies, the server can periodically obtain the real-time power generation efficiency of all PV panels at preset time intervals, such as noon each day. The server extracts the real-time efficiency data for each panel, sorts the efficiency values ​​using a sorting algorithm, and obtains a ranking result of the current PV panel efficiency. The server queries the PV panel operating status records in the database to confirm whether each panel is currently running. It compares the ranking result with the operating status, determining that panels with high real-time efficiency but currently shut down are panels to be started, and panels with low real-time efficiency but currently running are panels to be stopped. Finally, the server issues control commands to open the previously incorrectly shut-down high-efficiency panels to be started and close the previously incorrectly running low-efficiency panels to be stopped.

[0072] This ensures that the photovoltaic system always operates in optimal configuration and automatically corrects mismatches caused by efficiency variations.

[0073] In some embodiments, the photovoltaic power output of solar panels can be affected by weather changes such as cloudy skies and low rainfall. The power generation efficiency prediction model can incorporate weather conditions to assess output changes in advance and adjust the system accordingly. The following provides a more detailed description of the method provided in this embodiment. Please refer to... Figure 2 This is another flowchart illustrating the intelligent control method for photovoltaic power generation in this application embodiment.

[0074] S201. After obtaining the historical power generation data of each photovoltaic panel, determine the power generation prediction model based on the historical power generation data and the corresponding historical weather information.

[0075] In addition to obtaining real-time total power generation and determining the efficiency of photovoltaic panels, the server can also predict power generation in the future by establishing a power generation prediction model, so as to adjust the working status of photovoltaic panels in advance.

[0076] Specifically, the server needs to obtain historical power generation data for each photovoltaic panel over a certain period of time. This data can come from statistics in the server's historical database. Simultaneously, the server needs to obtain historical weather information corresponding to the time period of the historical power generation data, such as daily sunshine duration, temperature, and rainfall.

[0077] The server organizes, labels, and formats the historical power generation data and corresponding historical weather information for each photovoltaic panel, then inputs it into the machine learning algorithm module to train a power generation prediction model. The trained model can then be given environmental parameters and output a predicted power generation value for each photovoltaic panel. The server will save this model for future use.

[0078] For example, historical power generation data and weather data from the past three months were extracted and labeled with the ID of each photovoltaic panel. This training data was then fed into a neural network model for training, resulting in a complete power generation prediction model. When given weather parameters for a specific date, the model can output the expected power generation of each photovoltaic panel under those weather conditions.

[0079] S202. After obtaining the weather forecast information for a future set time starting from the current moment, input the weather forecast information and the power generation of each photovoltaic panel into the power generation prediction model to obtain the predicted power generation of each photovoltaic panel in the future set time.

[0080] After establishing a power generation prediction model, the server can take the following steps when predicting power generation over a future period: First, it obtains weather forecast information for a predetermined time period starting from the current moment from a weather station or via the network. This predetermined time period can be determined based on actual conditions, such as predicting weather conditions for the next day, three days, or one week. Second, the server obtains the current power generation data of each photovoltaic panel as one of the model inputs. Then, the server inputs the obtained weather forecast information for the future time period and the current power generation of each photovoltaic panel into the trained power generation prediction model. The model then outputs the predicted power generation of each photovoltaic panel for that time period.

[0081] For example, by obtaining the weather forecast for the next three days, including information such as sunshine duration, temperature, and precipitation, and simultaneously obtaining the current power generation capacity of each photovoltaic panel, the model can predict the daily power generation of each photovoltaic panel for the next three days.

[0082] In this way, the server can reliably predict power generation for a given future time period based on accurate weather forecast data and the current actual power generation situation, using a model.

[0083] S203. Determine the total projected power generation for a future period based on the sum of the projected power generation of all photovoltaic panels;

[0084] After the server predicts the power generation of each photovoltaic panel over a future period, it needs to further summarize and calculate to determine the total expected power generation of the photovoltaic system during this period. Specifically, the server can iterate through the IDs of all photovoltaic panels, extract their expected power generation for the predicted time period, and accumulate them into a total power generation variable. This yields the predicted total power generation of the entire photovoltaic system from the current moment to the next predicted time period. For example, predicting the expected power generation of 10 photovoltaic panels for the next 3 days as follows: Panel A: 20 kWh, 30 kWh, 25 kWh; Panel B: 15 kWh, 20 kWh, 18 kWh... By accumulating these predicted values, the estimated total power generation for the next 3 days can be calculated as follows: Day 1: 300 kWh, Day 2: 350 kWh, Day 3: 320 kWh. After determining the total power generation prediction for the future time period, the server can adjust the working status of the photovoltaic panels in advance for more refined energy management, avoiding over- or under-generation.

[0085] S204. Adjust the working status of each photovoltaic panel in advance according to the expected total power generation and the preset power consumption threshold.

[0086] After predicting the total power generation over a future period, the server can adjust the operating status of each photovoltaic panel in advance. Specifically, the server will pre-set the power consumption thresholds for different time periods within this predicted period. These thresholds can be determined based on historical power consumption data and usage habits for that period.

[0087] The server can then calculate the difference between the predicted total power generation and the threshold values ​​for each time period during this period. If the difference is greater than 0, it means that more electricity is expected to be generated than the electricity consumption, so some of the less efficient photovoltaic panels can be turned off in advance; if the difference is less than 0, the more efficient photovoltaic panels can be turned on in advance to increase power generation.

[0088] The server can determine the number of photovoltaic (PV) panels that need adjustment based on the difference in power generation (excess or deficiency) and the efficiency ranking of each panel. Then, it selects the appropriate number of panels according to the ranking and adjusts their operating status. For example, if the predicted total power generation for the next day is 500 kWh, and the morning power consumption threshold is set at 100 kWh and the daytime power consumption threshold at 300 kWh, then the morning power consumption will exceed the threshold by 100 kWh, and the daytime power consumption will fall short by 200 kWh. The server can then turn off the low-efficiency PV panels (corresponding to the lowest efficiency rating of 100 kWh) at dusk the previous day, and turn on the high-efficiency PV panels (corresponding to the highest efficiency rating of 200 kWh) in the morning.

[0089] By predicting power generation in advance and proactively adjusting the status of photovoltaic panels, power generation can be controlled within a reasonable range of electricity demand, avoiding waste caused by over-generation or insufficient power affecting electricity consumption, thus achieving refined energy management and improving the economic efficiency of the system.

[0090] The server in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a server in an embodiment of this application.

[0091] It should be noted that, Figure 3 The server structure shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0092] like Figure 3 As shown, the server includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0093] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0094] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0095] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0097] Specifically, the server in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the intelligent control method for photovoltaic power generation provided in the above embodiment.

[0098] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the server described in the above embodiments; or it may exist independently and not assembled into the server. The storage medium carries one or more computer programs that, when executed by a processor of the server, cause the server to implement the intelligent photovoltaic power generation control method provided in the above embodiments.

[0099] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0100] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for intelligent control of photovoltaic power generation, applied to the server of a management system, characterized in that, The method includes: The real-time total power generation data of each photovoltaic panel on the roof is obtained through the power generation monitoring module. If the real-time total power generation data exceeds the preset power consumption threshold limit for the current period, the power generation to be reduced is calculated. After obtaining the location information and current illuminance data of each photovoltaic panel, the expected power generation efficiency of each photovoltaic panel is determined. Based on the projected power generation efficiency, the corresponding photovoltaic panels are sorted from high to low to obtain the sorting results; Based on the sorting results, the power of each photovoltaic panel is determined sequentially from back to front until the sum of the total power is greater than or equal to the value of the power generation to be reduced. The last photovoltaic panel in the sorting results is identified as the first photovoltaic panel to be shut down. Continue calculating the photovoltaic panel power from the rear of the sorting results backward multiple times until it is determined that the power generation to be reduced meets the specified value, and identify multiple photovoltaic panels to be turned off; Control the first photovoltaic panel to be shut down and multiple photovoltaic panels to be shut down to stop working; After the step of obtaining real-time total power generation data of each rooftop photovoltaic panel through the power generation monitoring module, the following steps are also included: After obtaining the historical power generation data of each photovoltaic panel, a power generation prediction model is determined based on the historical power generation data and the corresponding historical weather information. After obtaining weather forecast information for a future set time period starting from the current moment, the weather forecast information and the power generation of each photovoltaic panel are input into the power generation prediction model to obtain the predicted power generation of each photovoltaic panel in the future set time period. The total projected power generation for a future period is determined by summing the projected power generation of all photovoltaic panels. Adjust the working status of each photovoltaic panel in advance according to the expected total power generation and the preset power consumption threshold; Following the step of controlling the multiple photovoltaic panels to be shut down to stop operating, the method further includes: The real-time power generation efficiency of all photovoltaic panels is monitored periodically at preset time intervals. Compare the real-time power generation efficiency of each photovoltaic panel to determine the ranking of power generation efficiency among the photovoltaic panels; Determine whether the ranking of power generation efficiency matches the current operating status of each photovoltaic panel; If they do not match, the server determines that the photovoltaic panels with high power generation efficiency and currently not running are photovoltaic panels to be started, and determines that the photovoltaic panels with low power generation efficiency and currently running are photovoltaic panels to be stopped. The operating status of each photovoltaic panel is readjusted according to the photovoltaic panels to be stopped and the photovoltaic panels to be started, so that the operating status matches the real-time power generation efficiency ranking results. After determining the last photovoltaic panel in the sorting results as the first photovoltaic panel to be turned off, the process further includes: The real-time temperature of each photovoltaic panel is determined using temperature monitoring equipment; Determine whether the real-time temperature of each photovoltaic panel is higher than the preset upper temperature limit; If it is determined that the real-time temperature is higher than the upper temperature limit, the corresponding photovoltaic panel is identified as a photovoltaic panel to be turned off.

2. The method according to claim 1, characterized in that, If the real-time total power generation data exceeds the preset power consumption threshold limit for the current time period, before calculating the power generation to be reduced value, the method further includes: Obtain residential electricity consumption data for different time periods within a set historical timeframe for a building area; Determine the predicted electricity consumption for future periods based on residential electricity consumption data; Based on the predicted electricity consumption, preset electricity consumption thresholds for different time periods are determined.

3. The method according to claim 1, characterized in that, The management system includes wireless sensors for detecting the power generation of the photovoltaic panels. After the step of acquiring real-time total power generation data of each photovoltaic panel on the roof through the power generation monitoring module, it also includes: If the real-time total power generation data is lower than the preset lower limit of the power consumption threshold for the current time period, an alarm message will be sent to the management terminal to notify the user that the current power generation is abnormal.

4. The method according to claim 1, characterized in that, After obtaining the real-time total power generation data of each rooftop photovoltaic panel through the power generation monitoring module, the following steps are also included: If the real-time total power generation data is higher than the preset power consumption threshold upper limit for the current time period, then the excess portion of the power generation can be stored. A control command is sent to the battery to charge and store the storable generated electricity.

5. A server, characterized in that, The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the server to perform the method as described in any one of claims 1-4.

6. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the server, it causes the server to perform the method as described in any one of claims 1-4.

7. A computer program product, characterized in that, When the computer program product is run on the server, the server performs the method as described in any one of claims 1-4.

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