Intelligent optical storage integrated AC / DC control method based on energy management and control algorithm

Abstract

The invention discloses an intelligent optical storage integrated AC / DC control method based on an energy management and control algorithm. The method comprises the following steps: acquiring and monitoring first data in real time; the working state of the photovoltaic module is dynamically adjusted through a maximum power point tracking algorithm, so that the photovoltaic module can output maximum power under various environmental conditions; according to the first data monitored in real time, the electric energy of photovoltaic power generation is dynamically distributed; acquiring a power grid fault state, switching an operation mode according to the power grid fault state, and controlling electric energy balance in the system; deploying an intelligent sensing system, collecting system operation state parameters in real time, and adjusting an energy management and power control strategy based on real-time data to enable the energy management and power control strategy to adapt to environment and load changes; monitoring faults and abnormal states, identifying fault types and positions, and intervening to recover normal operation of the system. The technical method for realizing optimal management and seamless operation switching of energy has the advantages of high system stability, high energy utilization efficiency and timely fault processing.

Description

Technical Field

[0001] The present invention relates to the field of fuzzy evaluation technology, and more specifically, to an intelligent photovoltaic-storage integrated AC / DC control method based on energy management and control algorithms. Background Art

[0002] With the rapid development of new energy technologies, intelligent photovoltaic and energy storage integrated AC and DC systems have gradually become an important part of the energy management field. Such systems integrate photovoltaic power generation, energy storage systems and grid interaction, and can achieve efficient utilization and optimized management of energy under various working conditions. However, in a complex and changing energy demand and supply environment, how to effectively manage and control these systems to ensure their stable and efficient operation is still a technical problem that needs to be solved urgently.

[0003] In the existing technology, traditional photovoltaic storage integrated system management methods usually rely on preset control strategies and static management modes. These methods show obvious limitations when facing complex operating environments and changing energy demands. Specifically, traditional methods have obvious shortcomings in the following aspects: 1. Lack of comprehensiveness in energy management: Traditional systems usually handle photovoltaic power generation, energy storage and load demand separately, lack a systematic energy management strategy, and find it difficult to achieve optimal allocation among various energy sources in a dynamically changing environment, resulting in reduced system efficiency and energy waste.

[0004] 2. Insufficient intelligence and adaptability: Traditional methods mostly rely on fixed control rules and parameter settings, and are unable to dynamically adjust the system operating status according to real-time data. Especially when facing environmental changes or load fluctuations, the system's adaptability is weak, which can easily lead to unstable system operation.

[0005] 3. Poor reliability of switching between grid-connected and island operation: In the existing technology, the system switching between grid-connected and island operation modes is often delayed and unstable, and seamless switching cannot be achieved. As a result, the system cannot provide stable power supply when the grid fails or is planned to go offline, affecting user experience and system reliability.

[0006] 4. Insufficient fault detection and self-healing capabilities: Fault detection in traditional systems mostly relies on passive monitoring, which is unable to identify and handle faults within the system in a timely manner, resulting in the system being unable to quickly resume normal operation when a fault occurs, increasing system downtime and maintenance costs.

[0007] Therefore, how to provide an intelligent photovoltaic and energy storage integrated AC and DC control method based on energy management and control algorithms is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent photovoltaic integrated AC / DC control method based on energy management and control algorithm, which realizes a technical method for optimal energy management and seamless operation switching, and has the advantages of high system stability, high energy utilization efficiency and timely fault handling.

[0009] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: (1) acquiring and monitoring first data in real time, wherein the first data includes the output power of the photovoltaic module and the charge state and load demand parameters of the energy storage system; (2) dynamically adjusting the working state of the photovoltaic module through the maximum power point tracking algorithm, so that the photovoltaic module can output the maximum power under various environmental conditions; (3) dynamically allocating the electric energy generated by the photovoltaic power generation according to the first data monitored in real time; (4) acquiring the fault state of the power grid, switching the operation mode according to the fault state of the power grid, and controlling the internal power balance of the system; (5) deploying an intelligent perception system to collect the system operation state parameters in real time, and adjusting the energy management and power control strategies based on the real-time data to adapt to the changes in the environment and load; (6) monitoring faults and abnormal conditions, identifying the fault type and location, and intervening to restore the normal operation of the system.

[0010] The technical effects and advantages of the intelligent photovoltaic and energy storage integrated AC and DC control method based on energy management and control algorithm of the present invention are as follows: (1) The present invention realizes energy optimization management and seamless operation switching in the photovoltaic energy storage integrated system by combining intelligent sensing technology, dynamic energy management strategy and adaptive control algorithm, so that the system can maintain efficient and stable operation status under various complex operating environments, especially when switching between grid-connected and island operation modes, ensuring the continuous power supply of key loads and the reliability of the system.

[0011] (2) The present invention effectively responds to the imbalance of energy supply and demand by real-time monitoring of the status data of photovoltaic power generation, energy storage system and load demand, and by using advanced prediction algorithms and dynamic adjustment strategies. The system can adjust the energy management strategy in advance based on real-time and predicted data to ensure that the energy of photovoltaic power generation and energy storage system is optimally configured under different environmental conditions, significantly improving the energy utilization efficiency of the system.

[0012] (3) The present invention uses a fault diagnosis and self-healing mechanism, real-time data monitoring and fault classification algorithms to quickly identify and handle faults or abnormal conditions in the system, automatically trigger corresponding self-healing strategies, optimize power distribution or switch to emergency control mode. This not only reduces system downtime and maintenance costs, but also ensures the safety and stability of the system when a fault occurs, thereby improving the overall reliability of the system.

[0013] (4) The present invention combines intelligent and adaptive control technologies. The system can automatically adjust operating parameters according to real-time data to adapt to load fluctuations and environmental changes, thereby enhancing the system's adaptive ability and response speed. In particular, when dealing with complex changes in energy demand, the system can quickly make adjustments to maintain optimal operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a system architecture diagram of the intelligent photovoltaic and energy storage integrated AC and DC control method based on energy management and control algorithm proposed in the present invention; Figure 2 This is a flow chart of the intelligent photovoltaic and energy storage integrated AC and DC control method based on energy management and control algorithm proposed in the present invention; Figure 3 The present invention provides a flow chart for seamlessly switching between grid-connected and islanded operation modes of the intelligent photovoltaic-storage integrated AC / DC control method based on energy management and control algorithms proposed in the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] refer to Figure 1-Figure 3 , an intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm, comprising the following steps: S1. Initialize the photovoltaic energy storage integrated system, obtain the output power of the photovoltaic modules, the state of charge of the energy storage system and the load demand parameters, and monitor the changes of these data in real time; In this implementation, S1 specifically includes: S11, obtain the output power of the photovoltaic module, and monitor the output voltage of the photovoltaic module in real time and output current , calculate the photovoltaic module at any time The instantaneous output power : ; in, Indicates the photovoltaic module at time The output voltage, Indicates the photovoltaic module at time Output current; S12, obtain the charge state of the energy storage system, and monitor the voltage of the energy storage system in real time , Current charge and charge and discharge current : ; ; in, For the initial moment The amount of charge, Indicates time The current at For energy storage system in time The actual charge, is the maximum charge capacity of the energy storage system; S13, obtain load demand parameters, by monitoring the power consumption of each load in the system, the total load power of the system It is composed of the sum of all load powers: ; ; in, and Respectively Load at time Voltage and current Indicates Load at time The power consumption, is the total number of loads in the system; S14. Set the sampling frequency based on real-time monitoring , sampling frequency and system response time Related: ; in, The system response time.

[0017] S2, through the maximum power point tracking algorithm, dynamically adjust the working state of the photovoltaic module, so that the photovoltaic module can output the maximum power under various environmental conditions; In this implementation, S2 specifically includes: S21, real-time collection of the output voltage of photovoltaic modules and output current , and use them to calculate the current output power of the photovoltaic module : ; in For photovoltaic modules in time The instantaneous output power, For photovoltaic modules in time The output voltage, For photovoltaic modules in time Output current; S22, according to the current output power , calculate and preset the maximum power point Power Deviation : ; in, For time The power deviation indicates the difference between the actual output power and the maximum power point. It is the maximum power point set by the system through the maximum power point tracking algorithm; S23, use the incremental conductance method to determine the direction and magnitude of the power deviation, and calculate the admittance change rate With current admittance The relationship to determine the adjustment direction: ; ; in, Indicates at time The rate of change of the current and voltage of the photovoltaic module at a certain moment is called the incremental admittance. It's time The instantaneous admittance of S24, according to power deviation The size and direction of the voltage adjustment are determined , and calculate the working voltage at the next moment : ; ; in, It's in time The voltage adjustment at the moment is determined by the power deviation and current change Decide, It is the gain factor used to control the voltage adjustment amplitude. For time The adjusted voltage at the moment; S25, the updated voltage Feedback is given to the photovoltaic module to adjust its operating voltage so that the output power of the photovoltaic module approaches and remains at the maximum power point.

[0018] S3. Based on the real-time monitored photovoltaic power generation, the state of charge of the energy storage system and the load demand, an energy management strategy is adopted to dynamically allocate the photovoltaic power generation energy, with part of it directly supplied to the load and part of it stored in the energy storage system. When the photovoltaic power generation is insufficient, the energy is released from the energy storage system to meet the load demand. In this implementation, S3 specifically includes: S31. Real-time monitoring of photovoltaic power generation , the state of charge of the energy storage system and load requirements : ; ; in, and The photovoltaic modules are The voltage and current, and Respectively Load at time The voltage and current, is the total number of loads in the system; S32. Calculate photovoltaic power generation based on real-time monitoring data and load demand The energy difference between : ; in, Indicates at time The difference between photovoltaic power generation and load demand when When , it means that the photovoltaic power generation exceeds the current load demand. When , it means that the photovoltaic power generation is not enough to meet the load demand; S33, when When Storage in energy storage system: ; in, For charging efficiency; Actual charging current : ; in, For energy storage system in time The voltage at S34, when When To meet load requirements: ; in, is the discharge efficiency; Actual discharge current : ; in, For energy storage system in time The voltage at S35, according to the state of charge of the energy storage system and energy management needs, dynamically adjusting charging and discharging operations, and the state of charge of the energy storage system : ; in, For time The actual charge of the energy storage system at is the maximum charge capacity of the energy storage system; Constrain energy management operations according to the following conditions: ; in, and are the minimum and maximum allowable state of charge of the energy storage system, respectively.

[0019] S4. When the system is connected to the grid, the energy exchange between the photovoltaic components, the energy storage system and the grid is realized through the grid control strategy; In this implementation, S4 specifically includes: S41. Real-time monitoring of photovoltaic power generation , the charging and discharging power of the energy storage system and power exchange with the grid : ; ; ; in, For photovoltaic modules in time The output power, and The photovoltaic modules are The voltage and current, For energy storage system in time The charge and discharge power, and are the voltage and current of the energy storage system, For power exchange between the system and the grid, and are the grid voltage and current respectively; S42. Calculate the power balance between the system and the power grid according to the current power distribution requirements of the system : ; in, Indicates that the system is at time The power difference between the power grid and the power grid is , indicating that the system needs to obtain additional power support from the grid. , indicating that the system delivers excess power to the grid; S43, when When the system obtains the required power from the grid through the grid-connected control strategy : ; According to the grid voltage Calculate the required current : ; in, is the power obtained by the system from the grid, The current required to obtain this power; S44, when When the system transmits excess power to the grid through the grid-connected control strategy : ; According to the grid voltage Calculate the required current : ; in, is the power delivered by the system to the grid, the current required to deliver that power; S45. Adjust the grid-connected control strategy in real time to ensure stable and efficient power exchange between the system and the grid. At the same time, dynamically adjust the power distribution ratio between photovoltaic power generation, energy storage system and the grid to achieve optimal energy management and control of the system.

[0020] S5. When the power grid fails or is planned to be disconnected from the grid, the system automatically switches to the island operation mode and maintains the power balance within the system through the island operation control strategy; In this implementation, S5 specifically includes: S51, real-time monitoring of grid status parameters, including grid voltage V grid (t), grid frequency f grid (t), grid current I grid (t) and power factor cos(φgrid (t)): P grid (t) = V grid (t)×I grid (t)×cos(φ grid (t)); Among them, V grid (t) is the voltage of the power grid at time t, I grid (t) is the current, φ grid (t) is the phase angle between voltage and current, cos(φ grid (t)) represents the power factor; S52: When an abnormal grid state is detected, the system determines that it is a grid failure or a planned grid disconnection event, and automatically triggers the system to switch to the island operation mode: ΔP island (t) = P load (t)-[P pv (t)+P bat (t)]; Among them, ΔP island (t) represents the power difference in island mode, which needs to be adjusted by adjusting the photovoltaic power generation P pv (t) and energy storage system output P bat (t) to meet the load requirements; S53, in the island operation mode, the system calculates the real-time photovoltaic power generation P pv (t), the state of charge SOC(t) of the energy storage system and the load demand P load (t), by dynamically adjusting the charging and discharging power P of the photovoltaic power generation and energy storage system bat (t), so that the power balance within the system: P bat (t) = ΔP island (t)×η bat (t); Among them, η bat (t) is the efficiency factor of the energy storage system, reflecting the current charging and discharging efficiency of the energy storage system; S54. During the island operation, the system monitors the power supply status of the critical loads in real time and ensures the continuous power supply of the critical loads through the priority control strategy. In this process, the system ensures the power supply stability of the critical loads by reducing the power supply of non-critical loads; S55. When it is detected that the power grid has returned to normal, the system will automatically switch from the island mode to the grid-connected mode. During the switching process, a seamless transition is achieved by gradually restoring the grid parallel connection to avoid the impact of instantaneous power fluctuations on the system and load. After the system re-enters the grid-connected mode, the power exchange between the system and the grid is recalculated, and the energy exchange strategy between the system and the grid is adjusted according to the power difference: ΔP grid (t) = P load (t)-[P pv (t)+P bat (t)]; Among them, ΔP grid (t) represents the power balance difference between the system and the grid at time t, P load (t) is the current load power demand of the system, P pv (t) is the output power of the PV module at time t, P bat (t) is the charging and discharging power of the energy storage system at time t. When ΔP grid (t)>0, indicating that the system load demand exceeds the supply of photovoltaic power generation and energy storage system, and additional power support is required from the grid. grid (t)<0, indicating that the supply of the system's photovoltaic power generation and energy storage system exceeds the load demand, and the excess power will be transmitted to the grid; S56. During the entire switching process, the system uses a combination of high-frequency sampling and control algorithms to prevent voltage surges or frequency drifts during the switching process; S57. When the system switches between the grid-connected mode and the island mode, the operating data during each switching process is recorded and analyzed for optimizing subsequent switching strategies.

[0021] S6. Deploy an intelligent sensing system to collect system operating status parameters in real time, and adjust energy management and power control strategies based on real-time data to adapt to changes in the environment and load; In this implementation, S6 specifically includes: S61. Deploy an intelligent sensing system to monitor the system operation status in real time, including the output power of photovoltaic modules. , the state of charge of the energy storage system , Load demand , Grid voltage , Grid frequency and other key parameters, and transmit these data to the energy management module in real time; S62. Based on real-time monitoring data, use prediction algorithms to predict future photovoltaic power generation. , Load demand And the power grid status is predicted: ; ; in, and Indicates the future time The predicted photovoltaic power generation and load demand at that time, and is the prediction coefficient, and are the time variation rates of PV power generation and load demand, respectively; S63, based on real-time monitoring data and forecast data, dynamically adjust the energy management strategy, when the photovoltaic power generation is predicted Higher than current load demand , the system will first store excess electricity in the energy storage system. When the predicted photovoltaic power generation is lower than the load demand, the system will start the discharge of the energy storage system in advance to supplement the power gap; S64. State of charge of energy storage system Perform dynamic management and calculate the current time The rate of change of state of charge : ; in, For energy storage system in time The charge and discharge power, is the charge and discharge efficiency, is the maximum charge capacity of the energy storage system, by adjusting , the system ensures Always stay within safe working range; S65. Based on real-time data and prediction results, the system divides the loads into critical loads and non-critical loads, and allocates energy to critical loads first. When an imbalance in power supply and demand is predicted, the system will dynamically adjust the power output of non-critical loads and partially shut down non-critical loads, so that critical loads can be continuously and stably powered. S66. The system continuously monitors the status of each operating link and promptly identifies potential abnormalities through fault diagnosis algorithms. Once an operating abnormality is detected, the system will immediately trigger a self-healing strategy, including automatically adjusting power distribution and switching to the backup control mode.

[0022] S7. When the system detects faults and anomalies, it uses fault diagnosis and self-healing technology to quickly identify the fault type and location, and restore the normal operation of the system through automated processing and manual intervention.

[0023] In this implementation, S7 specifically includes: S71. Deploy fault monitoring modules to collect real-time operating status data of key components of the system, including the output power of photovoltaic modules. , the charging and discharging power of the energy storage system ,Voltage , Current and the grid connection status; S72, based on the collected data, use the fault detection algorithm to identify abnormal conditions. For photovoltaic modules, compare the current output power With expected power Deviation Determine whether there is a fault: ; in, is the theoretical maximum output power based on the current solar radiation intensity and temperature conditions. When the set threshold is exceeded, the system determines that the PV module may be faulty; S73, perform fault detection on the energy storage system, and monitor the state of charge of the energy storage system in real time ,Voltage and current , calculate the health status parameters of the energy storage system : ; in, and are the rated voltage and current values ​​of the energy storage system respectively, is the maximum charge capacity of the energy storage system. When it drops below a certain threshold, the system determines that the energy storage system may have failed; S74. After the fault classification and location are completed, the system immediately starts the self-healing mechanism and automatically executes the corresponding repair and scheduling strategies according to the type and location of the fault. For minor faults, the system adjusts the energy management strategy, optimizes power distribution and operating parameters, and tries to restore the normal operation of the system. For serious faults, the system switches to the emergency control mode, isolates the faulty part, maintains stable power supply to the key loads, and transmits detailed fault information to the maintenance personnel; S75. After the self-healing strategy is executed, the system continuously monitors the fault repair process and evaluates the system's operating status. If the self-healing strategy successfully restores the normal operation of the system, the system will record the full process data of the fault handling. If the self-healing strategy fails to effectively restore the system, the system will promptly issue a high-level alarm and automatically enter a controlled operation mode, waiting for human intervention.

[0024] In order to verify the feasibility and effectiveness of the present invention, the intelligent photovoltaic integrated AC / DC control method based on energy management and control algorithm of the present invention is applied to the energy management system of a large industrial park. The park includes multiple photovoltaic power stations, energy storage systems and complex load networks. Due to the large fluctuations in the energy demand of the park and the complex system operating environment, the traditional energy management system cannot effectively cope with frequent load changes and power grid failures, resulting in low energy utilization efficiency and poor power supply reliability.

[0025] In this embodiment, the method based on the present invention is deployed for the industrial park to comprehensively manage and control the photovoltaic power generation, energy storage and loads in the park. After the system is installed, the following scenarios are mainly focused on: energy management of photovoltaic power generation and energy storage systems, seamless switching between grid-connected and islanded operation, fault detection and self-healing, and overall system stability and response speed.

[0026] During the peak period of photovoltaic power generation, the output power of photovoltaic modules reaches the highest value, exceeding the load demand at that time. The system calculates the energy difference through real-time monitoring data, and automatically stores the excess electricity in the energy storage system through intelligent sensing technology and dynamic energy management strategy. Through this strategy, the energy utilization rate of photovoltaic power generation in the park increased by about 15% during the 3-month test period. For example, in a certain monitoring, when the photovoltaic output reached 600 kW and the load demand was only 400 kW, the system quickly transferred the excess 200 kW of electricity to the energy storage system, ensuring efficient use of energy.

[0027] When the load demand exceeds the photovoltaic power generation, the system starts the discharge function of the energy storage system in advance through the prediction algorithm to ensure the continuous power supply of the load. For example, in a certain experiment, the load demand of the park suddenly increased from the conventional 350 kW to 500 kW, while the photovoltaic output at that time was 400 kW. Through real-time data and prediction results, the system starts the energy storage system discharge in advance, releases 100 kW of electricity to supplement the power gap, and ensures the normal operation of the load. After 3 months of observation, the power supply stability of the system has increased by 20%, especially during peak load periods, effectively avoiding power outages caused by insufficient power.

[0028] For seamless switching between grid-connected and islanded operation, the system achieves smooth switching by monitoring the grid voltage and frequency. For example, during a sudden grid failure, the system detected that the grid frequency fluctuation exceeded the safe range, and immediately switched to the islanded operation mode, and maintained the power balance within the park through the energy storage system and photovoltaic power generation. After the grid returned to normal, the system automatically switched back to the grid-connected mode. The entire switching process took less than 3 seconds, during which the load voltage fluctuation was controlled within 2%, and there was no impact on the park's production. This seamless switching is particularly important when the grid is unstable, and significantly improves the reliability of power supply.

[0029] In terms of fault detection and self-healing, the system uses fault detection algorithms to identify potential abnormal situations by monitoring the operating status of photovoltaic modules and energy storage systems in real time. For example, in a fault simulation test of an energy storage system, the system detected that the health status parameters of the energy storage system dropped below the set threshold, automatically triggered the self-healing mechanism, adjusted the power distribution and switched to the emergency control mode, isolating the faulty unit. The entire fault recovery process took about 2 minutes. The system automatically started the backup power supply as soon as the fault occurred, ensuring the continuous power supply to critical loads and avoiding a complete power outage caused by the energy storage system failure. According to data statistics, the system detected and handled a total of 5 minor faults and 2 serious faults during the test, all of which were repaired within 3 minutes, significantly shortening the fault handling time and improving the stability of the system.

[0030] During the implementation of the present invention, the response speed, energy utilization and power supply stability of the system have been significantly improved. The specific data are shown in Table 1 below: Table 1 Performance comparison of energy management systems in industrial parks

[0031] As can be seen from Table 1 above, the system of the present invention is superior to the traditional system in all indicators, especially in terms of energy utilization and power supply stability. During the 3-month test period, the system processed an average of about 50 energy scheduling tasks per month, the power supply failure rate dropped to 0.5%, and the response time was shortened from 5 minutes of the traditional system to less than 2 minutes. In a certain fault detection, the system successfully repaired a storage system failure through the self-healing mechanism, allowing the park to avoid production interruptions during peak power consumption periods.

[0032] From these data, it can be seen that the intelligent photovoltaic storage integrated AC / DC control method based on energy management and control algorithm of the present invention can effectively improve the efficiency of energy management and the operational stability of the system in practical applications, especially in dealing with complex energy demand changes and sudden failures. The present invention not only optimizes the energy utilization of the park, but also ensures the efficient and safe operation of the system through intelligent perception and self-healing mechanism, and has high practical value and promotion significance.

[0033] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0034] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0035] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0036] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0037] Finally: 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 in the protection scope of the present invention.

Claims

1. An intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm, characterized in that: The steps include: (1) Acquiring and monitoring first data in real time, wherein the first data includes output power of photovoltaic modules and charge state and load demand parameters of energy storage systems; (2) Through the maximum power point tracking algorithm, the working state of the photovoltaic module is dynamically adjusted so that the photovoltaic module can output maximum power under various environmental conditions; (3) dynamically allocating photovoltaic power generation according to the first data monitored in real time; (4) Obtain the grid fault status, switch the operation mode according to the grid fault status, and control the internal power balance of the system; (5) Deploy an intelligent sensing system to collect system operating status parameters in real time and adjust energy management and power control strategies based on real-time data to adapt to changes in the environment and load; (6) Monitor faults and abnormal conditions, identify fault types and locations, and intervene to restore normal system operation.

2. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 1 is characterized in that: Acquiring and real-time monitoring of first data, specifically including: Obtain the output power of the photovoltaic module by monitoring the output voltage of the photovoltaic module in real time and output current , calculate the photovoltaic module at any time The instantaneous output power ; ； In the formula, For photovoltaic modules in time The output voltage, For photovoltaic modules in time Output current; Obtain the state of charge of the energy storage system by monitoring the voltage of the energy storage system in real time , Current charge and charge and discharge current Data changes; ； ； In the formula, For the initial moment The amount of charge, Indicates time The current at For energy storage system in time The actual charge, is the maximum charge capacity of the energy storage system; Obtain load demand parameters by monitoring the power consumption of each load in the system and the total load power of the system. The formula is as follows: ； ； in, and Respectively Load at time Voltage and current Indicates Load at time The power consumption, is the total number of loads in the system.

3. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 2 is characterized in that: The maximum power point tracking algorithm is used to dynamically adjust the working state of the photovoltaic module so that the photovoltaic module can output the maximum power under various environmental conditions. The specific steps are as follows: Collect the output voltage and output current of the photovoltaic module in real time and calculate the current output power of the photovoltaic module; According to the current output power, the power deviation from the preset maximum power point is calculated; The incremental conductance method is used to determine the direction and magnitude of the power deviation, and the adjustment direction is determined by calculating the relationship between the admittance change rate and the current admittance; According to the size and direction of the power deviation, the voltage adjustment amount is determined, and the working voltage at the next moment is calculated; The updated voltage is fed back to the PV module to adjust its operating voltage so that the output power of the PV module approaches and remains at the maximum power point.

4. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 3 is characterized in that: The output power formula of the front photovoltaic module is: ； In the formula For photovoltaic modules in time The instantaneous output power, For photovoltaic modules in time The output voltage, For photovoltaic modules in time of output current.

5. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 4 is characterized in that: The incremental conductance method is used to determine the direction and magnitude of the power deviation, and the adjustment direction is determined by calculating the relationship between the admittance change rate and the current admittance, specifically including: ； ； in, Indicates at time The rate of change of the current and voltage of the photovoltaic module at a certain moment is called the incremental admittance. It's time The instantaneous admittance.

6. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 5 is characterized in that: The voltage adjustment amount is determined according to the magnitude and direction of the power deviation, and the working voltage at the next moment is calculated, specifically: ； ； in, It's in time The voltage adjustment at the moment is determined by the power deviation and current change Decide, It is the adjustment gain factor, which is used to control the amplitude of voltage adjustment. For time The adjusted voltage at that moment.

7. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 6 is characterized in that: The step of dynamically allocating photovoltaic power generation energy according to the first data monitored in real time specifically includes: (31) Real-time monitoring of photovoltaic power generation , the state of charge of the energy storage system and load requirements , calculate photovoltaic power generation and load demand The energy difference between ; when When Stored in energy storage system; when When To meet load requirements: In the formula, is the discharge efficiency; (32) According to the state of charge of the energy storage system and energy management constraints, dynamically adjust charging and discharging operations, and the state of charge of the energy storage system for: ； in, For time The actual charge of the energy storage system at is the maximum charge capacity of the energy storage system; The energy management constraints are: ； in, and are the minimum and maximum allowable state of charge of the energy storage system, respectively.

8. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 6 is characterized in that: It also includes the energy exchange between photovoltaic modules, energy storage systems and the power grid through the grid-connected control strategy when the system is connected to the grid, specifically: Real-time monitoring of photovoltaic power generation, charging and discharging power of energy storage systems, and power exchange with the power grid; Calculate the power balance between the system and the grid based on the current power distribution requirements of the system ; when When , the system obtains the required power from the grid through the grid-connected control strategy; when When the system transmits excess power to the grid through the grid-connected control strategy.

9. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 8 is characterized in that: The obtaining of the grid fault state, switching the operation mode according to the grid fault state, and controlling the internal power balance of the system specifically include: (41) Real-time monitoring of grid status parameters, including grid voltage, grid frequency, grid current and power factor; (42) When an abnormal grid status is detected, the system determines that it is a grid failure or a planned grid disconnection event, and automatically triggers the system to switch to island operation mode: (43) In the island operation mode, the system dynamically adjusts the charging and discharging power of the photovoltaic power generation and energy storage system according to the real-time photovoltaic power generation, the charge state of the energy storage system and the load demand to achieve power balance within the system; (44) During island operation, the system monitors the power supply status of critical loads in real time and ensures the continuous power supply of critical loads through priority control strategies. The system also reduces the power supply of non-critical loads to ensure the stability of power supply to critical loads. (45) When it is detected that the power grid has returned to normal, the system will automatically switch from island mode to grid-connected mode.

10. The intelligent photovoltaic energy storage integrated AC / DC control method based on energy management and control algorithm according to claim 5, characterized in that: The deployment of the intelligent sensing system collects system operating status parameters in real time and adjusts the energy management and power control strategies based on real-time data to adapt to changes in the environment and load, specifically including: (61) Deploy a fault monitoring module to collect real-time operating status data of key components of the system, including the output power of photovoltaic modules, the charging and discharging power, voltage, current and grid connection status of the energy storage system; (62) Based on the collected data, a fault detection algorithm is used to identify abnormal operating conditions. For photovoltaic modules, the deviation between the current output power and the expected power is compared to determine whether there is a fault: (63) Detect faults in the energy storage system and calculate the health status parameters of the energy storage system by real-time monitoring of the state of charge, voltage and current of the energy storage system; (64) After the fault classification and location are completed, the system starts the self-healing mechanism and automatically executes the corresponding repair and scheduling strategies according to the type and location of the fault; (65) After the self-healing strategy is executed, the system continuously monitors the fault repair process and evaluates the system's operating status.

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