A smart energy management system for solar photovoltaic power generation

Abstract

This invention discloses an intelligent energy management system for solar photovoltaic power generation, belonging to the technical field of solar photovoltaic power generation management systems. It includes a multi-source data acquisition and fusion module, a central intelligent control platform, an intelligent energy storage management system, an adaptive inverter control unit, a visual human-machine interface, and a remote communication and collaborative optimization module. Through precise intelligent tracking, MPPT algorithms, and distributed MPPT to address shading, the power generation efficiency of the solar photovoltaic power generation system can be increased by 15%-30% compared to traditional systems, fully tapping the potential of solar energy resources.

Description

Technical Field

[0001] This invention relates to an intelligent energy management system for solar photovoltaic power generation, belonging to the technical field of solar photovoltaic power generation management systems. Background Technology

[0002] Under the global trend of energy transition, solar photovoltaic (PV) power generation, as an important form of clean and renewable energy utilization, is rapidly gaining popularity. However, current solar PV power generation systems face numerous challenges. From the power generation perspective, the power generation efficiency of PV panels is affected by various factors such as the angle of sunlight, cloud cover, air quality, and module aging, resulting in significant fluctuations in power output. Moreover, the lack of coordination between PV arrays of different specifications, orientations, and installation locations makes it difficult to maximize overall efficiency.

[0003] In the energy storage and utilization sector, on the one hand, the charging and discharging management of energy storage batteries is rudimentary, which easily leads to overcharging and over-discharging, which not only reduces battery life but may also cause safety issues; on the other hand, in the face of complex and ever-changing electricity demand, the power distribution lacks flexibility and cannot take into account both peak power supply guarantee and off-peak energy storage value-added.

[0004] Furthermore, the intermittent and unstable power output of photovoltaic (PV) power generation systems during their interaction with the power grid can easily impact the grid, causing issues such as voltage fluctuations and harmonic pollution, thus affecting power quality and limiting the feasibility of large-scale grid connection for PV power generation. Therefore, innovative energy management technologies are urgently needed to overcome these challenges and promote the steady development of the solar PV power generation industry. Summary of the Invention

[0005] To address the existing problems, this invention provides an intelligent energy management system for solar photovoltaic power generation. By integrating advanced monitoring, control, and optimization strategies, it achieves efficient solar energy collection, intelligent allocation, and stable supply, thereby improving the overall performance of the photovoltaic power generation system. The specific technical solution is as follows:

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

[0007] The multi-data acquisition and fusion module calibrates all sensors to ensure the accuracy of data acquisition; the central intelligent management and control platform loads 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 equalization circuit; the adaptive inverter control unit tests key components such as inverter switching transistors and heat dissipation systems; the visual human-machine interface displays the startup screen and synchronizes initial data; and the remote communication and collaborative optimization module establishes internal and external communication links and performs handshake tests with peripheral devices.

[0008] The multi-source data acquisition and fusion module includes the photovoltaic array area, energy storage unit, inverter, grid connection point, and various electrical load terminals. Within the photovoltaic array, high-precision solar irradiance sensors, combined with intelligent tracking devices, can not only accurately measure real-time sunlight intensity but also dynamically adjust the angle of the solar panels based on the sun's position to ensure maximum sunlight reception. Simultaneously, temperature sensors monitor the panel temperature in real time, as temperature significantly impacts photovoltaic conversion efficiency. Utilizing multispectral imaging technology, the module can also detect dust, dirt, or minor damage on the photovoltaic module surface, providing early warnings of potential power generation efficiency degradation.

[0009] The energy storage unit is equipped with sensors for power monitoring, voltage and current detection, and internal resistance measurement, comprehensively monitoring key indicators such as remaining battery charge (SOC), state of health (SOH), and charge / discharge current-voltage curves, providing a data foundation for precise battery management. A sensor network before and after the inverter closely monitors power, voltage, current, and phase changes during the energy conversion process, ensuring efficient and stable energy conversion. Sensors at the grid connection collect real-time parameters such as grid voltage, frequency, and harmonic content, ensuring compatibility between the photovoltaic power generation system and the grid. Sensors at the load end meticulously capture the power consumption characteristics, real-time power demands, and usage patterns of different loads to enable on-demand power supply.

[0010] The massive amounts of data collected are aggregated 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 in-depth integration and analysis.

[0011] The central intelligent management and control platform, serving as the brain of the entire energy management system, is built upon high-performance cloud computing servers and artificial intelligence chipsets. It first runs complex environmental prediction models, combining local meteorological data, historical sunshine records, and real-time sky imaging analysis to predict sunshine patterns over the next few hours or even days, providing a forward-looking basis for power generation planning. For example, it predicts periods of reduced sunshine based on cloud movement speed and thickness, allowing for adjustments to energy storage strategies in advance.

[0012] Based on real-time and predicted data, an advanced family of maximum power point tracking (MPPT) algorithms is used to optimize photovoltaic array power generation. By integrating intelligent variable step-size conductance incremental method with fuzzy logic control, the operating voltage of the photovoltaic panels is rapidly adjusted when sunlight changes quickly, accurately locating the maximum power point and ensuring optimal power generation efficiency at all times. For cases of partial shading, distributed MPPT technology is introduced, enabling each photovoltaic submodule to independently track its maximum power, preventing overall power generation efficiency from being severely affected by localized shading.

[0013] Based on the status of energy storage units, load demand curves, and grid acceptance capacity, the storage, release, and grid-connected scheduling of electrical energy are planned in a coordinated manner. During off-peak hours with sufficient sunlight, excess electrical energy is intelligently controlled to charge the energy storage batteries using the optimal charging mode. A multi-stage constant current-constant voltage charging strategy is adopted, taking into account battery characteristics, to avoid overcharging while maximizing battery energy storage. During peak hours, the discharge of energy storage batteries is coordinated with photovoltaic power generation, and power is dynamically allocated according to the urgency of the load to ensure uninterrupted power supply to critical loads. When interacting with the grid, the grid-connected power and reactive power compensation are precisely controlled, and the output is adjusted in real time according to grid voltage fluctuations to ensure the injection of high-quality electrical energy into the grid, maintain the stability of the grid power factor, and reduce the impact on the grid.

[0014] The intelligent energy storage management system closely connects with the central intelligent control platform to provide refined management of energy storage batteries. Employing active balancing technology, it constructs a battery balancing circuit based on a bidirectional DC-DC converter. This circuit monitors the voltage and SOC differences of individual cells within the battery pack in real time, intelligently transferring energy from high-capacity cells to low-capacity cells. This ensures consistent overall performance of the battery pack and extends battery life.

[0015] Customized charge and discharge management strategies are implemented to address the unique characteristics of different types of energy storage batteries (such as lithium-ion and lead-acid batteries). In low-temperature environments, a battery preheating program is automatically initiated to enhance battery activity and ensure charging and discharging efficiency. Charge and discharge parameters are dynamically adjusted based on the battery aging process to prevent overcharging and discharging of degraded batteries, optimizing battery performance throughout its entire lifecycle. Simultaneously, integrated battery fault diagnosis functions are employed. By monitoring real-time changes in parameters such as battery internal resistance, self-discharge rate, and gas generation, machine learning models are used to predict battery fault risks in advance. Once a potential hazard is detected, an alarm is promptly sent to the central control platform, prompting isolation or maintenance measures to be taken.

[0016] The adaptive inverter control unit plays a crucial role in ensuring high-quality power conversion and grid-connected operation. It employs Model Predictive Control (MPC) combined with Space Vector Pulse Width Modulation (SVPWM) technology to optimize the on-time sequence of inverter switches in real time based on load characteristics and grid conditions. On one hand, it precisely regulates the output voltage and current waveforms for nonlinear and dynamically changing loads, ensuring power quality meets the needs of sensitive electrical equipment and keeping harmonic distortion at extremely low levels. On the other hand, during grid-connected operation, it rapidly adjusts the inverter's output power and phase based on grid voltage and frequency commands and real-time feedback, achieving seamless grid synchronization and actively participating in grid reactive power regulation to improve grid stability.

[0017] The built-in intelligent thermal management module monitors the temperature of key components such as inverter power devices and transformers in real time. It automatically adjusts the heat dissipation intensity through liquid cooling or air cooling systems to ensure stable operation of the inverter under different load and environmental conditions, and avoids efficiency reduction or device damage due to overheating. At the same time, it has fault self-diagnosis and redundancy switching functions. Once a fault in a key component is detected, it quickly switches to the backup circuit to ensure power supply continuity and uploads fault information to the central management platform for timely maintenance.

[0018] The system features an intuitive and user-friendly visual human-machine interface, enabling maintenance personnel to monitor the overall operation of the photovoltaic power generation system in real time. A high-definition large screen displays key information such as the real-time trend of photovoltaic array power generation, comparisons of panel power generation efficiency in different areas, dynamic changes in the SOC of energy storage batteries, load power distribution, and grid connection parameters. Visual charts and animations vividly present the system's operating status, facilitating rapid identification of system health and performance bottlenecks.

[0019] It provides a convenient operation interface, allowing maintenance personnel to remotely set system parameters via touch operation or voice commands, such as adjusting MPPT algorithm sensitivity, modifying energy storage battery charging and discharging thresholds, and customizing load power supply priorities. It also allows real-time querying of historical operating data and generation of energy efficiency analysis reports, providing data support for system optimization and maintenance decisions. Furthermore, it features intelligent early warning capabilities. In the event of system anomalies, such as photovoltaic module failure, battery overcharging and over-discharging, or grid voltage exceeding limits, it accurately alerts maintenance personnel through audible and visual alarms and pop-up messages, clearly indicating the location and nature of the fault to assist in rapid troubleshooting and repair.

[0020] Leveraging advanced Internet of Things (IoT) technology and dedicated communication protocols, the remote communication and collaborative optimization module enables interconnection between the photovoltaic power generation system and various external entities. Internally, it constructs a low-latency, high-reliability local area network covering the entire system, ensuring smooth data exchange between each subsystem and the central control platform. Externally, it establishes information bridges with the power grid dispatch center, meteorological monitoring stations, and surrounding distributed energy systems through 4G / 5G networks.

[0021] The system shares real-time information with the power grid dispatch center, including photovoltaic power generation plans, grid-connected power, and available energy storage. It receives grid control commands and flexibly adjusts power generation and supply strategies to help the grid smooth peak and valley loads, achieving coordinated optimization of the power source, grid, and load. It also collaborates with meteorological monitoring stations to obtain timely, high-precision weather forecasts, providing more accurate input for solar radiation prediction models and optimizing system operation in advance. Furthermore, it interacts with surrounding distributed energy systems (such as wind power and biomass power) to explore hybrid energy complementarity models, achieving synergistic complementarity in energy types and spatiotemporal distribution, thus improving the reliability and resilience of regional energy supply. In addition, it supports remote online upgrades, allowing the system to automatically update software algorithms and control strategies via cloud-based push notifications, adapting to evolving technological 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 address shading, the power generation efficiency of solar photovoltaic power generation systems can be increased by 15%-30% compared to traditional systems, fully tapping the potential of solar energy resources.

[0024] Extended lifespan and enhanced safety of energy storage: The intelligent energy storage management system’s refined charge and discharge control and active balancing technology can effectively extend the lifespan of energy storage batteries by 20%-40%, reduce battery replacement costs, and reduce 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 with low harmonic distortion. It accurately controls power and reactive power during grid connection, significantly reducing the impact on the grid, enhancing the grid's ability to accept photovoltaic power generation, and promoting 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 methods, remote communication enables collaborative optimization, facilitates real-time monitoring and remote operation by operation and maintenance personnel, and reduces operation and maintenance costs and improves system reliability and operational efficiency by leveraging big data and artificial intelligence to assist decision-making. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is an architectural block diagram of an intelligent energy management system for solar photovoltaic power generation according to an embodiment of the present invention. Detailed Implementation

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

[0030] Example 1:

[0031] This embodiment provides an intelligent energy management system for solar photovoltaic power generation. See [link to relevant documentation]. Figure 1 The system includes:

[0032] During the initial system startup, each module performs a self-test procedure. The multi-source data acquisition and fusion module calibrates all sensors to ensure the accuracy of data acquisition; the central intelligent management and control platform loads initial configuration parameters and pre-trained artificial intelligence models; the intelligent energy storage management system detects the initial state of the energy storage batteries and activates the equalization circuit; the adaptive inverter control unit tests key components such as inverter switching transistors and the cooling system; the visual human-machine interface displays the startup screen and synchronizes initial data; and the remote communication and collaborative optimization module establishes internal and external communication links and performs handshake tests with peripheral devices.

[0033] Once in normal operation, the data acquisition network continues to function. As the sun rises in the morning, the solar irradiance sensor quickly detects increased light intensity and transmits the data to the central control platform. Simultaneously, the temperature sensor detects that the photovoltaic panels are at a low temperature, which is conducive to power generation. Based on real-time sunlight data, the platform activates the intelligent tracking device, adjusting the panel angle to be perpendicular to the sunlight. Combined with the MPPT algorithm, it fine-tunes the panel's operating voltage, initiating the day's efficient power generation process.

[0034] As sunlight intensifies, photovoltaic power generation gradually increases. When the SOC of the energy storage battery is detected to be below the set charging threshold and the load demand remains stable, the central control platform issues an instruction, and excess energy is used by the intelligent energy storage management system to charge the battery in an optimized charging mode. During the charging process, the active balancing circuit balances the charge of each individual battery in real time to prevent overcharging of individual batteries.

[0035] During midday, cloud cover may cause fluctuations in sunlight. The central control platform uses environmental prediction models to anticipate these fluctuations and, combined with distributed MPPT technology, quickly coordinates the operation of each photovoltaic sub-module to maintain stable overall power generation. At the same time, it flexibly adjusts the charging and discharging status of the energy storage batteries according to changes in load demand to ensure continuous and stable power supply to the load.

[0036] As the sun sets in the evening, sunlight weakens, and photovoltaic power generation decreases. At this time, if the load is at its peak, the central control platform directs the energy storage battery to discharge, and coordinates with the inverter to adaptively adjust the output to meet the load's power demand. The inverter uses SVPWM technology to ensure the quality of the output power, and the thermal management module starts to dissipate heat as needed to ensure the stable operation of the inverter.

[0037] Maintenance personnel can view the system's operating status at any time through a visual HMI. If the power generation efficiency of a photovoltaic array in a certain area is found to be low, detailed data can be queried through the interface to determine if it is due to dust accumulation on the component surface and to arrange cleaning operations. When encountering grid voltage fluctuations, the system automatically adjusts the grid-connected power and reactive power compensation based on grid feedback information, and maintenance personnel can also view the adjustment process and effects on the HMI.

[0038] The remote communication module continuously interacts with external systems. 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, updates environmental prediction models with weather forecasts from meteorological stations, and explores complementary solutions with surrounding energy systems. For example, during periods of high wind power generation and low photovoltaic power generation, it negotiates energy sharing to improve regional energy efficiency. Furthermore, the system can automatically complete software upgrades based on cloud pushes, introducing new optimization algorithms to 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent energy management system for solar photovoltaic power generation, characterized in that, It includes a multi-source data acquisition and fusion module, a central intelligent control platform, an intelligent energy storage management system, an adaptive inverter control unit, a visual human-machine interface, and a remote communication and collaborative optimization module; The multi-data acquisition and fusion module is equipped with various sensors distributed in the photovoltaic array, energy storage unit, inverter, grid access point and load end, which are used to collect data on light, temperature, power and electrical parameters, and transmit the data to the central intelligent management and control platform in real time through a high-speed data transmission channel. The central intelligent management and control platform relies on cloud computing servers and artificial intelligence chipsets to run environment prediction models and maximum power point tracking algorithms. Based on energy storage, load and grid information, it formulates energy management strategies and issues control commands to each module. The intelligent energy storage management system precisely manages the charging and discharging of energy storage batteries according to instructions from the central control platform. It adopts active balancing technology, customizes charging and discharging strategies for different battery characteristics, and has fault diagnosis functions. The adaptive inverter control unit uses model predictive control and space vector pulse width modulation technology to optimize the turn-on timing of inverter switching transistors, ensuring power quality and grid synchronization, and has built-in thermal management and fault redundancy switching functions. A visual human-computer interaction interface displays core system operation information on a high-definition large screen, provides an operation entry point 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 utilizes IoT technology to achieve interconnection and interoperability between the system and external systems, and collaborates with power grids, weather stations, and surrounding energy systems to support remote online upgrades.

2. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that, The solar irradiance sensor in the multi-data acquisition and fusion module, combined with an intelligent tracking device, can dynamically adjust the angle of the photovoltaic panel according to the sun's position, and uses multispectral 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 central intelligent control platform adopts a maximum power point tracking algorithm that integrates intelligent variable step size conductance incremental 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 is based on a bidirectional DC-DC converter to construct a battery balancing circuit, which intelligently transfers the energy of high-capacity cells to low-capacity cells.

5. The intelligent energy management system for solar photovoltaic power generation according to claim 1, characterized in that, The adaptive inverter control unit uses model predictive control combined with space vector pulse width modulation technology, which can accurately regulate 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 screen of the visual human-computer interaction interface displays the system's operating status using visual charts, animations, and other forms, and the operation entry supports touch operation or voice commands.

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 dispatch center, meteorological monitoring station, and surrounding distributed energy systems through the 4G / 5G network to achieve collaborative optimization of the source-grid-load system.

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