Intelligent energy management system for solar photovoltaic power generation

Abstract

The invention discloses an intelligent energy management system for solar photovoltaic power generation, and belongs to the technical field of solar photovoltaic power generation management systems. Comprising a multivariate data acquisition and fusion module, a central intelligent management and control platform, an intelligent energy storage management system, a self-adaptive inverter control unit, a visual human-computer interaction interface and a remote communication and collaborative optimization module. The power generation efficiency of the solar photovoltaic power generation system can be improved by 15%-30% compared with that of a traditional system through accurate intelligent tracking, an MPPT algorithm and distributed MPPT to deal with shadow shielding, and the potential of solar energy resources is fully exploited.

Description

Technical Field

[0001] The invention relates to an intelligent energy management system for solar photovoltaic power generation, and belongs to the technical field of solar photovoltaic power generation management systems. Background Art

[0002] Under the general trend of global energy transformation, solar photovoltaic power generation is rapidly gaining popularity as an important form of clean and renewable energy. However, the current solar photovoltaic power generation system faces many challenges. From the power generation end, the power generation efficiency of photovoltaic panels is affected by many factors such as sunlight angle, cloud cover, air quality, component aging, etc., and the power generation fluctuates greatly. Moreover, there is a lack of coordination between photovoltaic arrays of different specifications, different orientations and installation locations, making it difficult to maximize overall efficiency.

[0003] In the link of electric energy storage and utilization, on the one hand, the charging and discharging management of energy storage batteries is extensive, which is prone to overcharging and over-discharging, which not only reduces battery life but also may cause safety problems; on the other hand, faced with complex and changeable electricity demand, electricity distribution lacks flexibility and cannot take into account both peak power demand and valley power storage value-added.

[0004] Furthermore, in the process of interaction between photovoltaic power generation system and power grid, due to the intermittent and unstable power output, it is easy to cause impact on the power grid, such as voltage fluctuation, harmonic pollution, etc., which affects the power supply quality of the power grid and also limits the feasibility of large-scale grid connection of photovoltaic power generation. Therefore, innovative energy management technologies are urgently needed to overcome these problems and promote the steady development of the solar photovoltaic power generation industry. Summary of the invention

[0005] In order to solve the existing problems, the present invention provides an intelligent energy management system for solar photovoltaic power generation, which integrates advanced monitoring, control and optimization strategies to achieve efficient collection, intelligent allocation and stable supply of solar energy, and improve the comprehensive performance of the photovoltaic power generation system. The specific technical solutions are as follows:

[0006] The intelligent energy management system for solar photovoltaic power generation includes a multivariate data acquisition and fusion module, a central intelligent management and control platform, an intelligent energy storage management system, an adaptive inverter control unit, a visual human-computer interaction interface, and a remote communication and collaborative optimization module.

[0007] The multivariate data acquisition and fusion module calibrates all sensors to ensure the accuracy of data collection; the central intelligent management and control platform loads the initial configuration parameters and pre-trained artificial intelligence models; the intelligent energy storage management system detects the initial state of the energy storage battery and activates the balancing circuit; the adaptive inverter control unit tests the inverter switch tube, cooling system and other key components; the visual human-computer interaction interface completes the startup screen display and synchronizes the initial data; the remote communication and collaborative optimization module establishes internal and external communication links and performs handshake tests with peripheral equipment.

[0008] The multivariate data acquisition and fusion module includes the photovoltaic array area, energy storage unit, inverter, grid access point and various types of power load ends. In the photovoltaic array, the high-precision solar irradiation sensor combined with the intelligent tracking device can not only accurately measure the real-time light intensity, but also dynamically adjust the angle of the solar panel according to the position of the sun to ensure the maximum light reception; at the same time, the temperature sensor monitors the temperature of the solar panel in real time, because the temperature has a significant impact on the photovoltaic conversion efficiency. With the help of multispectral imaging technology, it is also possible to detect whether there is dust, dirt or minor damage on the surface of the photovoltaic module, and to warn in advance of possible power generation efficiency reduction problems.

[0009] The energy storage unit is equipped with power monitoring, voltage and current detection, and internal resistance measurement sensors to fully control the remaining battery capacity (SOC), health status (SOH), charge and discharge current and voltage curves and other key indicators, providing a data basis for accurate battery management. The sensor network before and after the inverter closely monitors the power, voltage, current and phase changes during the power conversion process to ensure the efficiency and stability of power conversion. Sensors at the grid access point collect parameters such as the voltage, frequency, and harmonic content of the grid in real time to ensure the compatibility of the photovoltaic power generation system with the grid when it is connected to the grid. Sensors at the load end carefully capture the power consumption characteristics, real-time power requirements, and usage period patterns of different loads to achieve on-demand power supply.

[0010] The massive amounts of data collected are gathered to the central intelligent management and control platform through high-speed, interference-resistant data transmission channels, such as dedicated links based on 5G communication modules or industrial Ethernet, for deep integration and analysis and processing.

[0011] As the brain of the entire energy management system, the central intelligent management and control platform is built on high-performance cloud computing servers and artificial intelligence chipsets. It first runs a complex environmental prediction model, combines local meteorological data, historical light records, and real-time sky imaging analysis to predict the trend of light changes in the next few hours or even days in advance, providing a forward-looking basis for power generation planning. For example, based on the speed and thickness of cloud movement, it predicts the period of weakened light and adjusts the energy storage strategy in advance.

[0012] Based on real-time and predicted data, the advanced maximum power point tracking (MPPT) algorithm family is used to optimize photovoltaic array power generation. The intelligent variable step conductivity increment method and fuzzy logic control are integrated to quickly adjust the working voltage of the photovoltaic panel when the light changes rapidly, accurately locate the maximum power point, and ensure that the power generation efficiency is always optimal; for partial shadow occlusion, the distributed MPPT technology is introduced so that each photovoltaic sub-module can independently track the maximum power to avoid the overall power generation efficiency being seriously affected by local shadows.

[0013] According to the status of energy storage units, load demand curves and the acceptance capacity of the power grid, the storage, release and grid-connected scheduling of electric energy are planned in a coordinated manner. During periods of low electricity consumption and sufficient sunlight, the excess electric energy is intelligently regulated to charge the energy storage battery in the optimal charging mode. A multi-stage constant current-constant voltage charging strategy is adopted in combination with battery characteristics to avoid overcharging while maximizing battery energy storage. During peak electricity consumption periods, the discharge of energy storage batteries and photovoltaic power generation are coordinated to jointly supply power, and power is dynamically allocated according to the severity of the load to ensure uninterrupted power supply to key loads. When interacting with the power grid, the grid-connected power and reactive power compensation are accurately controlled, and the output is adjusted in real time according to the voltage fluctuations of the power grid to ensure the injection of high-quality electric energy into the power grid, maintain the stability of the power factor of the power grid, and reduce the impact on the power grid.

[0014] The intelligent energy storage management system is closely connected to the instructions of the central intelligent control platform, and the intelligent energy storage management system manages the energy storage battery in a refined manner. It adopts active balancing technology, builds a battery balancing circuit based on a bidirectional DC-DC converter, monitors the voltage and SOC differences of each single cell in the battery pack in real time, and intelligently transfers the energy of the high-power single cell to the low-power single cell, ensuring the overall performance of the battery pack is consistent and extending the battery life.

[0015] Based on the differences in characteristics of different types of energy storage batteries (such as lithium-ion batteries and lead-acid batteries), we customize exclusive charge and discharge management strategies. In low-temperature environments, we automatically start the battery preheating program to improve battery activity and ensure charge and discharge efficiency; we dynamically adjust the charge and discharge parameters according to the battery aging process to avoid overcharging and discharging of degraded batteries and optimize the battery's full life cycle performance. At the same time, we integrate the battery fault diagnosis function, monitor the changes in parameters such as battery internal resistance, self-discharge rate, and gas generation in real time, and use machine learning models to predict the risk of battery failure in advance. Once hidden dangers are found, they will promptly report to the central control platform and take isolation or maintenance measures.

[0016] The adaptive inverter control unit plays a key role in ensuring high-quality power conversion and grid-connected operation. It uses model predictive control (MPC) combined with space vector pulse width modulation (SVPWM) technology to optimize the conduction timing of the inverter switch tube in real time according to the load characteristics and grid conditions. On the one hand, facing nonlinear and dynamically changing loads, it accurately controls the output voltage and current waveforms to ensure that the power quality meets the needs of sensitive power-consuming equipment and the harmonic distortion rate is controlled at an extremely low level; on the other hand, during grid-connected operation, according to the voltage and frequency instructions and real-time feedback of the grid, it quickly adjusts the inverter output power and phase to achieve seamless synchronous grid connection, actively participates in the reactive power regulation of the grid, and improves the stability of the grid.

[0017] The built-in intelligent thermal management module monitors the temperature of key components such as inverter power devices and transformers in real time, and automatically adjusts the heat dissipation intensity through liquid cooling or air cooling systems to ensure that the inverter can work stably under different loads and environmental conditions, avoiding efficiency loss or device damage due to overheating. At the same time, it has fault self-diagnosis and redundant switching functions. Once a key component failure is detected, it will quickly switch to the backup circuit to ensure power supply continuity and upload fault information to the central control platform for timely maintenance.

[0018] The intuitive and convenient visual human-computer interaction interface is designed to facilitate operation and maintenance personnel to control the overall operation of the photovoltaic power generation system in real time. The core information such as the real-time trend of photovoltaic array power generation, the comparison of power generation efficiency of solar panels in each region, the dynamic change of SOC of energy storage batteries, the distribution of load power consumption and grid access parameters are displayed on a high-definition large screen; the system operation status is vividly presented in the form of visual charts and animations, which facilitates quick insight into the health of the system and performance bottlenecks.

[0019] It provides a convenient operation portal, and operation and maintenance personnel can remotely set system parameters through touch operation or voice commands, such as adjusting the sensitivity of the MPPT algorithm, modifying the charge and discharge thresholds of the energy storage battery, and customizing the load power supply priority. It can also query historical operation data in real time and generate energy efficiency analysis reports to provide data support for system optimization and operation and maintenance decisions. At the same time, it has an intelligent early warning function. Once the system is abnormal, such as photovoltaic component failure, battery overcharge and overdischarge, grid voltage exceeding the limit, etc., it accurately prompts the operation and maintenance personnel through sound and light alarms and pop-up information, and marks the fault location and nature in detail to assist in rapid troubleshooting and repair.

[0020] Remote communication and collaborative optimization module uses advanced Internet of Things (IoT) technology and dedicated communication protocols to achieve interconnection between photovoltaic power generation systems and external multiple entities. Internally, a low-latency, high-reliability local area network covering the entire system is built to ensure smooth data interaction between each subsystem and the central control platform; externally, an information bridge is established with the power grid dispatching center, meteorological monitoring station, and surrounding distributed energy systems through 4G / 5G networks.

[0021] Share the power generation plan, grid-connected power, available energy storage and other information of the photovoltaic power generation system with the power grid dispatching center in real time, receive the control instructions of the power grid, flexibly adjust the power generation and power supply strategies, help the power grid to reduce peak loads and fill valleys, and realize the coordinated optimization of source-grid-load; cooperate with the meteorological monitoring station to obtain high-precision weather forecasts in time, provide more accurate input for the light prediction model, and optimize the system operation preparation in advance; interact with the surrounding distributed energy systems (such as wind power generation and biomass power generation), explore the hybrid energy complementary mode, realize the coordinated complementarity in energy types, time and space distribution, and improve the reliability and resilience of regional energy supply. In addition, it supports remote online upgrade function, and the system can automatically update software algorithms and control strategies based on cloud push to adapt to the evolving technical needs and application scenarios.

[0022] The beneficial effects of the present invention are:

[0023] Significantly improved power generation efficiency: Through precise intelligent tracking, MPPT algorithm and distributed MPPT to deal with shadow shading, the power generation efficiency of solar photovoltaic power generation system can be increased by 15%-30% compared with traditional systems, fully tapping the potential of solar energy resources.

[0024] Extended energy storage life and enhanced safety: The refined charge and discharge control and active balancing technology of the intelligent energy storage management system can effectively extend the life of the energy storage battery by 20%-40%, reduce the cost of battery replacement, and reduce the safety hazards caused by overcharging and over-discharging.

[0025] Power quality optimization and grid friendliness: The adaptive inverter control unit ensures high-quality power output, low harmonic distortion, and precise control of power and reactive power when connected to the grid, which greatly reduces the impact on the grid, improves the grid's ability to accept photovoltaic power generation, and promotes the grid connection and consumption of new energy.

[0026] Convenient operation and maintenance and intelligent decision-making: Visual HMI provides intuitive and convenient operation and maintenance means, remote communication realizes collaborative optimization, and facilitates real-time monitoring and remote operation by operation and maintenance personnel. With the help of big data and artificial intelligence to assist in decision-making, operation and maintenance costs can be reduced and system reliability and operational efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a framework diagram of an intelligent energy management system for solar photovoltaic power generation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] Embodiment 1:

[0031] This embodiment provides an intelligent energy management system for solar photovoltaic power generation. Figure 1 , the system comprising:

[0032] At the initial start of the system, each module performs a self-test procedure. The multivariate data acquisition and fusion module calibrates all sensors to ensure the accuracy of data acquisition; the central intelligent management and control platform loads the initial configuration parameters and pre-trained artificial intelligence models; the intelligent energy storage management system detects the initial state of the energy storage battery and activates the balancing circuit; the adaptive inverter control unit tests the inverter switch tube, cooling system and other key components; the visual human-computer interaction interface completes the startup screen display and synchronizes the initial data; the remote communication and collaborative optimization module establishes internal and external communication links and performs handshake tests with peripheral devices.

[0033] Entering the normal operation stage, the data collection network continues to work. When the sun rises in the early morning, the solar radiation sensor quickly senses the increase in light intensity and transmits data to the central control platform. At the same time, the temperature sensor detects that the temperature of the photovoltaic panels is low, which is conducive to power generation. The platform activates the intelligent tracking device based on real-time light data, adjusts the angle of the panel to make it perpendicular to the sunlight, and fine-tunes the working voltage of the panel in combination with the MPPT algorithm to start the efficient power generation process of the day.

[0034] As the sunlight continues to increase, the amount of photovoltaic power generation gradually increases. When it is detected that the SOC of the energy storage battery is lower than the set charging threshold and the load power demand is stable, the central control platform issues a command, and the excess power is used to charge the battery through the intelligent energy storage management system in an optimized charging mode. During the charging process, the active balancing circuit balances the power of each single battery in real time to avoid overcharging of individual batteries.

[0035] During the noon period, clouds may block the light and cause fluctuations. The central control platform uses the environmental prediction model to make early predictions and combines distributed MPPT technology to quickly coordinate the work of each photovoltaic sub-module to maintain the overall power generation stability; at the same time, according to the changes in load demand, the charging and discharging state of the energy storage battery is flexibly adjusted to ensure continuous and stable power supply to the load.

[0036] In the evening, when the sun sets, the sunlight weakens and the photovoltaic power generation decreases. At this time, if the load is at its peak power consumption, the central control platform will command the energy storage battery to discharge and coordinate the inverter to adaptively adjust the output to meet the load power demand; the inverter uses SVPWM technology to ensure the output power quality, and the thermal management module starts heat dissipation on demand to ensure the stable operation of the inverter.

[0037] Operation and maintenance personnel can check the system operation status at any time through the visual HMI. If the power generation efficiency of the photovoltaic array in a certain area is found to be low, detailed data can be queried through the interface operation to determine whether it is dust on the surface of the component and arrange cleaning operations; when the grid voltage fluctuates, the system automatically adjusts the grid-connected power and reactive power compensation based on the grid feedback information, and the operation and maintenance personnel can also view the adjustment process and effect on the HMI.

[0038] The remote communication module continuously interacts with the outside world. It regularly reports power generation plans and real-time grid-connected power to the power grid dispatch center, receives control instructions to optimize power generation strategies, obtains weather forecasts from meteorological stations to update environmental prediction models, and explores complementary solutions with surrounding energy systems, such as negotiating energy mutual assistance during periods of high wind power generation and low photovoltaic power generation to improve regional energy utilization efficiency. In addition, the system can automatically complete software upgrades based on cloud push, introduce new optimization algorithms, and continuously improve performance.

[0039] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent energy management system for solar photovoltaic power generation, characterized in that: It includes multi-data collection and fusion module, central intelligent management and control platform, intelligent energy storage management system, adaptive inverter control unit, visual human-computer interaction interface and remote communication and collaborative optimization module; The multi-dimensional data collection and fusion module is equipped with a variety of sensors distributed in the photovoltaic array, energy storage unit, inverter, grid access point and load end to collect light, temperature, power and electrical parameter data, and transmit the data in real time to the central intelligent control platform through a high-speed data transmission channel; The central intelligent control platform, relying on cloud computing servers and artificial intelligence chipsets, runs environmental prediction models and maximum power point tracking algorithms, formulates energy management strategies based on energy storage, load and grid information, and issues control instructions to each module; The intelligent energy storage management system manages the charging and discharging of energy storage batteries in detail according to the instructions of the central control platform. It adopts active balancing technology to customize the charging and discharging strategies for different battery characteristics and has fault diagnosis function. Adaptive inverter control unit, using model predictive control and space vector pulse width modulation technology to optimize the inverter switch conduction timing, ensure power quality and grid synchronization, built-in thermal management and fault redundancy switching functions; Visual human-computer interaction interface, which displays the core information of system operation through a high-definition large screen, provides an operation portal for operation and maintenance personnel to remotely set parameters and query data, and has intelligent early warning function; The remote communication and collaborative optimization module uses the Internet of Things technology to achieve interconnection within and outside the system, collaborate with the power grid, weather stations, and surrounding energy systems, and support remote online upgrades.

2. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that: The solar radiation sensor of the multivariate data acquisition and fusion module is combined with an intelligent tracking device to dynamically adjust the angle of the photovoltaic panel according to the position of the sun, and use multi-spectral imaging technology to detect the surface condition of the photovoltaic module.

3. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that: The maximum power point tracking algorithm adopted by the central intelligent management and control platform integrates the intelligent variable step conductance increment method and fuzzy logic control, and introduces distributed MPPT technology for partial shadow occlusion.

4. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that: The active balancing technology of the intelligent energy storage management system constructs a battery balancing circuit based on a bidirectional DC-DC converter, and intelligently transfers the energy of high-power cells to low-power cells.

5. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that: The adaptive inverter control unit adopts model predictive control combined with space vector pulse width modulation technology, which can accurately control the output voltage and current waveforms, and control the harmonic distortion rate at an extremely low level.

6. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that: The high-definition large screen of the visual human-computer interaction interface uses visual charts, animations, etc. to display the system operation status, and the operation entrance supports touch operation or voice command.

7. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that: The remote communication and collaborative optimization module establishes an information bridge with the power grid dispatching center, meteorological monitoring station, and surrounding distributed energy systems through the 4G / 5G network to achieve collaborative optimization of source-grid-load.

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