Photovoltaic power generation management method and system

Abstract

The invention discloses a photovoltaic power generation management method and system, and relates to the technical field of photovoltaic power generation management, and the method comprises the steps: S1, carrying out the real-time monitoring and classification of loads: carrying out the real-time monitoring of various types of electricity loads in an off-grid system through an intelligent electric meter and a sensor; s2, implementing an energy distribution algorithm: performing energy distribution by using a dynamic energy distribution algorithm according to the battery electric quantity, the photovoltaic power generation power and the load demand real-time monitoring data; s3, power generation equipment cooperative management: establishing a cooperative control mechanism of a photovoltaic panel and a diesel generator by means of the monitored environmental parameters; s4, intelligent management of energy storage equipment: monitoring parameters such as voltage, current, temperature and residual electric quantity of the battery in real time; s5, equipment state monitoring and data analysis; and S6, executing the active maintenance mechanism. According to the photovoltaic power generation management method and system, energy can be accurately distributed according to the load requirement, the generated power and the electric quantity of the battery which are monitored in real time.

Description

Technical Field

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

[0002] As the global demand for clean energy continues to grow, off-grid photovoltaic power generation systems have been widely used in remote areas, emergency energy security and other fields due to their independence and flexibility.

[0003] Traditional energy distribution methods lack precise control over real-time load demand and the dynamic status of power generation equipment, often leading to energy waste or insufficient power supply to critical loads. Furthermore, equipment collaborative operation management is not intelligent enough, and photovoltaic panels and diesel generators and other power generation equipment fail to work efficiently together, making it impossible to adjust power generation strategies in a timely manner according to environmental changes. To address the shortcomings of existing technologies, we propose a photovoltaic power generation management method and system. Summary of the Invention

[0004] The main purpose of the present invention is to provide a photovoltaic power generation management method and system, which can effectively solve the problems in the background technology.

[0005] To achieve the above object, the technical solution adopted by the present invention is: A photovoltaic power generation management method includes S1: real-time load monitoring and classification: using smart meters and sensors to monitor various types of power loads in an off-grid system in real time and collect data such as power demand and usage duration; S2: Energy allocation algorithm implementation: Based on the real-time monitoring data of battery power, photovoltaic power generation and load demand, a dynamic energy allocation algorithm is used to allocate energy; S3: Collaborative management of power generation equipment: Using sensors to monitor environmental parameters such as light intensity in real time, a collaborative control mechanism for photovoltaic panels and diesel generators is established; S4: Intelligent management of energy storage equipment: Real-time monitoring of battery parameters such as voltage, current, temperature, and remaining capacity, using advanced charge and discharge control algorithms to optimize charge and discharge strategies based on battery status and power demand; S5: Equipment status monitoring and data analysis: Through sensors and intelligent monitoring equipment, the operating status of key equipment such as photovoltaic panels, inverters, battery packs and diesel generators is monitored 24 hours a day, and parameters such as voltage, current, temperature, speed, and power are collected; S6: Execution of proactive maintenance mechanism: Develop proactive maintenance plans based on the results of the equipment failure prediction model.

[0006] Preferably, in S1, the loads can be divided into critical loads, important loads and non-essential loads. Critical loads are lighting fixtures that ensure basic life or lighting fixtures that ensure safety, etc., important loads are household appliances such as refrigerators and air conditioners, etc., and non-essential loads are some high-energy-consuming and non-urgent electrical appliances.

[0007] Preferably, in S2, when photovoltaic power generation is sufficient but the battery is not fully charged, priority is given to charging the battery and meeting the power needs of all loads. If photovoltaic power generation is insufficient, power is first provided to critical loads, and then allocated to important loads and non-essential loads in order of priority. Non-essential loads that cannot be met are powered off. At night or when sunlight is insufficient, power is mainly supplied by battery discharge, and power is supplied according to load priority. When the battery power level falls below a safety threshold, only power is provided to critical loads, and other loads are shut down.

[0008] Preferably, in S3, when the illumination intensity reaches the photovoltaic panel start-up threshold, the photovoltaic panel is started first to generate electricity. When the illumination intensity cannot meet the demand or the battery power is too low, the diesel generator is started. According to the power generation efficiency and cost, the operation combination of the power generation equipment is optimized, the photovoltaic panel is given priority to generate electricity, and the use of the diesel generator is reduced.

[0009] Preferably, in said S4, during charging, the charging current and voltage are adjusted according to the remaining battery power and temperature to prevent overcharging, and during discharging, the discharge depth is controlled according to the load demand and the remaining battery power.

[0010] Preferably, in S5, big data analysis and machine learning techniques are used to deeply mine historical and real-time data to establish an equipment failure prediction model.

[0011] Preferably, in S6, when it is predicted that the equipment may fail, maintenance personnel are arranged in advance to inspect and maintain it, replace potential faulty parts, avoid power outages caused by equipment failure, establish a maintenance knowledge base, record maintenance history, failure cases and solutions, provide technical support for maintenance personnel, and provide real-time guidance and supervision of the maintenance process through a remote monitoring system.

[0012] A photovoltaic power generation management system includes: a real-time monitoring module: which displays the operating parameters, environmental parameters and energy distribution of each device in the off-grid photovoltaic power generation system in real time in the form of intuitive charts and dashboards; Energy management module: realizes intelligent control of power generation equipment, energy storage equipment and power loads based on dynamic energy allocation strategy; Equipment control module: provides a remote control interface, allowing users to remotely operate and control equipment such as photovoltaic panels, inverters, battery packs and diesel generators through a central management platform; Fault prediction and maintenance module: Based on equipment status monitoring data and fault prediction models, it predicts potential equipment failures in real time and issues early warning information in a timely manner.

[0013] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, the dynamic energy allocation strategy accurately allocates energy based on the real-time monitoring of load demand, power generation power and battery power. By classifying and managing the loads, it prioritizes the power consumption of key loads and reasonably regulates non-essential loads, effectively avoiding energy waste and improving energy utilization efficiency. Under different lighting and power consumption conditions, the system intelligently switches the energy supply mode, such as giving priority to photovoltaic power generation and charging during the day and relying on battery discharge at night to ensure a continuous and stable power supply, thereby enhancing the power supply reliability of the off-grid photovoltaic power generation system and meeting various power needs of users. In the present invention, the collaborative operation and management of intelligent devices realizes the intelligent collaboration of multiple power generation equipment such as photovoltaic panels and diesel generators. According to environmental parameters such as light intensity, the system automatically selects the most suitable combination of power generation equipment to give full play to the advantages of each equipment. When there is sufficient light, the photovoltaic panels generate electricity efficiently; when energy is insufficient, the diesel generators supplement it in time, effectively improving the overall power generation capacity of the system. This collaborative mechanism enables the system to better adapt to complex and changing environments. Regardless of different seasons in remote areas or in emergency power supply scenarios, it can output electricity stably, expanding the application scope of off-grid photovoltaic power generation systems. In the present invention, the fault prediction and proactive maintenance mechanism uses big data analysis and machine learning technology to deeply mine equipment operation data and accurately predict potential faults in advance. Maintenance personnel intervene in advance based on the prediction results and replace potential faulty components in a timely manner, greatly reducing the probability of power outages caused by sudden equipment failures and reducing the extent of equipment damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the photovoltaic power generation management method and system of the present invention. DETAILED DESCRIPTION

[0015] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0016] like Figure 1 As shown, a photovoltaic power generation management method includes S1: real-time monitoring and classification of loads: using smart meters and sensors to monitor various types of power loads in the off-grid system in real time, collecting data such as power demand and usage duration. The loads can be divided into critical loads, important loads, and non-essential loads. Critical loads include lighting fixtures that ensure basic life or lighting fixtures that ensure safety, important loads include household appliances such as refrigerators and air conditioners, and non-essential loads include some high-energy-consuming and non-urgent electrical appliances. S2: Energy allocation algorithm implementation: Based on real-time monitoring data of battery power, photovoltaic power generation, and load demand, a dynamic energy allocation algorithm is used to allocate energy. When photovoltaic power generation is sufficient but the battery is not fully charged, the battery is prioritized to charge and meet the power needs of all loads. If photovoltaic power generation is insufficient, power is first guaranteed for critical loads, and then allocated to important loads and non-essential loads in order of priority. Non-essential loads that cannot be met are powered off. At night or when there is insufficient sunlight, power is mainly supplied by battery discharge, and power is supplied according to load priority. When the battery power falls below a safety threshold, only critical loads are guaranteed power, and other loads are shut down. S3: Collaborative management of power generation equipment: Using sensors to monitor environmental parameters such as light intensity in real time, a collaborative control mechanism for photovoltaic panels and diesel generators is established. When the light intensity reaches the threshold for photovoltaic panel activation, the photovoltaic panels are activated first. When the light intensity cannot meet the demand or the battery power is too low, the diesel generator is activated. Based on power generation efficiency and cost, the operating combination of power generation equipment is optimized, giving priority to photovoltaic panels for power generation and reducing the use of diesel generators. S4: Intelligent management of energy storage equipment: Real-time monitoring of battery parameters such as voltage, current, temperature, and remaining capacity. Using advanced charge and discharge control algorithms, the charging and discharging strategies are optimized based on battery status and power demand. During charging, the charging current and voltage are adjusted based on the remaining battery capacity and temperature to prevent overcharging. During discharging, the depth of discharge is controlled based on load demand and remaining capacity. S5: Equipment Status Monitoring and Data Analysis: Utilizing sensors and intelligent monitoring equipment, we continuously monitor the operating status of key equipment, such as photovoltaic panels, inverters, battery packs, and diesel generators, around the clock. We collect parameters such as voltage, current, temperature, speed, and power. Using big data analysis and machine learning techniques, we conduct in-depth mining of historical and real-time data to establish equipment failure prediction models. S6: Execution of proactive maintenance mechanism: Based on the results of the equipment failure prediction model, a proactive maintenance plan is formulated. When a possible equipment failure is predicted, maintenance personnel are arranged in advance to inspect and perform maintenance, replace potential faulty parts, and avoid power outages caused by equipment failure. A maintenance knowledge base is established to record maintenance history, failure cases, and solutions. Technical support is provided to maintenance personnel, and real-time guidance and supervision of the maintenance process is provided through a remote monitoring system.

[0017] like Figure 1 As shown in the figure, the real-time monitoring module: displays the operating parameters, environmental parameters and energy distribution of each device in the off-grid photovoltaic power generation system in real time in the form of intuitive charts and dashboards. Users can view the real-time operating status of the system through terminal devices such as computers, mobile phones or tablets to timely understand the system's working conditions; Energy management module: Based on dynamic energy allocation strategies, it realizes intelligent control of power generation equipment, energy storage equipment, and power loads. It provides energy statistics and analysis functions, generates energy consumption reports and power generation efficiency reports, helps users understand the system's energy usage, and optimizes energy management strategies. Equipment control module: Provides a remote control interface, allowing users to remotely operate and control equipment such as photovoltaic panels, inverters, battery packs, and diesel generators through a central management platform. This allows users to remotely start or stop diesel generators, adjust inverter output parameters, and control battery charge and discharge modes. It also has a device linkage control function that enables automatic coordination between devices based on preset logical relationships. When the battery power is too low, it automatically starts the diesel generator and switches to charging mode. When the light intensity increases, it automatically adjusts the angle of the photovoltaic panels to improve power generation efficiency. Fault prediction and maintenance module: Based on equipment status monitoring data and fault prediction models, it predicts potential equipment failures in real time and issues early warning information in a timely manner. The early warning information is notified to users and maintenance personnel via SMS, email, or APP push, reminding them to perform equipment inspection and maintenance in a timely manner. At the same time, the module is also responsible for formulating maintenance plans, arranging maintenance tasks, recording maintenance history, and evaluating and analyzing maintenance effects, realizing full-process management of equipment maintenance and improving equipment reliability and service life.

[0018] It should be noted that the present invention is a photovoltaic power generation management method and system. When in use, first, smart meters and sensors are used to monitor various power loads in the off-grid system in real time, collecting data such as power demand and usage duration. Then, based on the real-time monitoring data of battery power, photovoltaic power generation power, and load demand, a dynamic energy allocation algorithm is used to allocate energy. When photovoltaic power generation is sufficient and the battery is not fully charged, the battery is given priority to charge and meet the power needs of all loads. If photovoltaic power generation is insufficient, power is first guaranteed for key loads, and then allocated to important loads and non-essential loads in sequence according to priority. Non-essential loads that cannot be met are powered off. At night or when there is insufficient light, power is mainly supplied by battery discharge according to load priority. When the battery power falls below a safety threshold, only power is guaranteed for key loads, and other loads are shut down. Finally, sensors are used to monitor environmental parameters such as light intensity in real time to establish a coordinated control mechanism for photovoltaic panels and diesel generators. When the light intensity reaches the photovoltaic panel startup threshold, the photovoltaic panel is started first to generate electricity. When the light intensity cannot meet the demand or the battery power is too low, the diesel generator is started. Based on power generation efficiency and cost, the operation combination of power generation equipment is optimized, giving priority to photovoltaic panels for power generation and reducing the use of diesel generators. During charging and discharging, the battery voltage, current, temperature, and remaining capacity are monitored in real time. Advanced charge and discharge control algorithms are used to optimize the charge and discharge strategy based on the battery status and power demand. During charging, the charging current and voltage are adjusted according to the remaining battery capacity and temperature to prevent overcharging. During discharging, the discharge depth is controlled according to the load demand and remaining capacity. During fault detection, sensors and intelligent monitoring equipment are used to monitor the operating status of key equipment such as photovoltaic panels, inverters, battery packs and diesel generators 24 hours a day, collect parameters such as voltage, current, temperature, speed, and power, and use big data analysis and machine learning technologies to conduct in-depth mining of historical and real-time data to establish an equipment failure prediction model. Then, based on the results of the equipment failure prediction model, a proactive maintenance plan is formulated. When a possible equipment failure is predicted, maintenance personnel are arranged in advance to inspect and maintain it, replace potential faulty components, and avoid power outages caused by equipment failure.

[0019] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A photovoltaic power generation management method, characterized in that: include: S1: Real-time load monitoring and classification: Utilize smart meters and sensors to monitor various types of electrical loads in the off-grid system in real time, collecting data such as power demand and usage duration; S2: Energy allocation algorithm implementation: Based on real-time monitoring data of battery power, photovoltaic power generation and load demand, a dynamic energy allocation algorithm is used to allocate energy; S3: Collaborative management of power generation equipment: Using sensors to monitor environmental parameters such as light intensity in real time, a collaborative control mechanism for photovoltaic panels and diesel generators is established; S4: Intelligent management of energy storage equipment: Real-time monitoring of battery parameters such as voltage, current, temperature, and remaining capacity, using advanced charge and discharge control algorithms to optimize charge and discharge strategies based on battery status and power demand; S5: Equipment status monitoring and data analysis: Through sensors and intelligent monitoring equipment, the operating status of key equipment such as photovoltaic panels, inverters, battery packs and diesel generators is monitored 24 hours a day, and parameters such as voltage, current, temperature, speed, and power are collected; S6: Execution of proactive maintenance mechanism: Develop proactive maintenance plans based on the equipment failure prediction model results.

2. A photovoltaic power generation management method according to claim 1, characterized in that: In S1, loads can be divided into critical loads, important loads and non-essential loads. Critical loads include lighting fixtures that ensure basic life or medical equipment that ensures safety, important loads include household appliances such as refrigerators and air conditioners, and non-essential loads include some high-energy-consuming and non-urgent electrical appliances such as electric heaters.

3. A photovoltaic power generation management method according to claim 1, characterized in that: In S2, when PV power generation is sufficient but the battery is not fully charged, battery charging is prioritized to meet the power needs of all loads. If PV power generation is insufficient, power is first provided to critical loads, and then allocated to important and non-essential loads in order of priority. Non-essential loads that cannot be met are powered off. At night or during periods of low sunlight, battery discharge is the primary method of supplying power, with loads prioritized. When the battery charge falls below a safety threshold, power is provided only to critical loads, and other loads are shut down.

4. A photovoltaic power generation management method according to claim 1, characterized in that: In S3, when the illumination intensity reaches the threshold for starting the photovoltaic panels, the photovoltaic panels are started first to generate electricity. When the illumination intensity cannot meet the demand or the battery power is too low, the diesel generator is started. Based on the power generation efficiency and cost, the operation combination of the power generation equipment is optimized, giving priority to the photovoltaic panels to generate electricity and reducing the use of the diesel generator.

5. The photovoltaic power generation management method according to claim 1, characterized in that: In S4, during charging, the charging current and voltage are adjusted according to the remaining battery power and temperature to prevent overcharging, and during discharging, the discharge depth is controlled according to the load demand and the remaining battery power.

6. A photovoltaic power generation management method according to claim 1, characterized in that: In the above-mentioned S5, big data analysis and machine learning technology are used to conduct in-depth mining of historical and real-time data to establish an equipment failure prediction model.

7. A photovoltaic power generation management method according to claim 1, characterized in that: In S6, when it is predicted that the equipment may fail, maintenance personnel are arranged in advance to inspect and perform maintenance, replace potential faulty parts, avoid power outages caused by equipment failure, establish a maintenance knowledge base, record maintenance history, failure cases and solutions, provide technical support for maintenance personnel, and provide real-time guidance and supervision of the maintenance process through a remote monitoring system.

8. A photovoltaic power generation management system, applicable to a photovoltaic power generation management method according to any one of claims 1 to 7, characterized in that: include: Real-time monitoring module: displays the operating parameters, environmental parameters and energy distribution of each device in the off-grid photovoltaic power generation system in real time in the form of intuitive charts and dashboards; Energy management module: realizes intelligent control of power generation equipment, energy storage equipment and power loads based on dynamic energy allocation strategy; Equipment control module: provides a remote control interface, allowing users to remotely operate and control equipment such as photovoltaic panels, inverters, battery packs and diesel generators through a central management platform; Fault prediction and maintenance module: Based on equipment status monitoring data and fault prediction models, it predicts potential equipment failures in real time and issues early warning information in a timely manner.

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