Optical storage micro-grid energy management system

Abstract

The invention belongs to the field of micro-grid management, and particularly relates to an optical storage micro-grid energy management system which comprises a micro-grid monitoring module, a fault prediction module, a data extraction module, a data analysis module, a strategy making module, an operation execution module and a synchronous operation module. The micro-grid monitoring module is used for monitoring the real-time operation data of the micro-grid, including the output condition of a photovoltaic power generation system, the charging and discharging state of an energy storage system, the load demand and the power grid parameters; the data extraction module is used for extracting the data monitored by the micro-grid monitoring module and transmitting the extracted data to the data analysis module; and the data analysis module is used for analyzing the data according to the received real-time operation data in combination with distributed power generation prediction and load prediction, and transmitting the analysis data to the strategy making module. According to the invention, efficient fault prediction and processing can be realized in the optical storage micro-grid, and the operation efficiency and safety of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid management, and particularly to a photovoltaic-storage microgrid energy management system. Background Art

[0002] The photovoltaic-storage microgrid energy management system is an energy management software with functions such as optimized power generation scheduling, load management, real-time monitoring, and automatic microgrid synchronization. The photovoltaic-storage microgrid energy management system can monitor the real-time operation data of the photovoltaic-storage microgrid, including key parameters such as voltage, current, and power. By collecting these data, the system can comprehensively understand the operation status of the microgrid, providing a basis for subsequent optimized scheduling. The system has the function of optimized power generation scheduling. It can optimize the power generation plan of distributed power sources according to the real-time load demand and renewable energy power generation situation, such as the output of photovoltaic power generation. Through intelligent algorithms, the system can calculate the optimal power generation strategy to improve energy utilization efficiency, reduce network losses and operating costs;

[0003] In the prior art, the photovoltaic-storage microgrid energy management system is not convenient for fault prediction and handling, which affects the normal operation of the microgrid. Therefore, we propose a photovoltaic-storage microgrid energy management system to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to solve the defect that the photovoltaic-storage microgrid energy management system in the prior art is not convenient for fault prediction and handling, which affects the normal operation of the microgrid, and to propose a photovoltaic-storage microgrid energy management system.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A photovoltaic-storage microgrid energy management system, comprising:

[0007] A microgrid monitoring module, a fault prediction module, a data extraction module, a data analysis module, a strategy formulation module, an operation execution module, and a synchronous operation module;

[0008] The microgrid monitoring module is used to monitor the real-time operation data of the microgrid, including the output of the photovoltaic power generation system, the charge and discharge status of the energy storage system, the load demand, and the grid parameters;

[0009] The data extraction module is used to extract the data monitored by the microgrid monitoring module and transmit the extracted data to the data analysis module;

[0010] The data analysis module is used to analyze the data according to the received real-time operation data, combined with distributed power generation prediction and load prediction, and transmit the analysis data to the strategy formulation module;

[0011] A strategy formulation module, which is used to formulate an optimized scheduling strategy under multiple constraints according to the received analysis data. During the strategy formulation process, the volatility of photovoltaic power generation, the charge and discharge capacity of the energy storage system, the predicted value of load demand, and the operating constraints of the power grid are comprehensively considered;

[0012] An operation execution module, which is used to adjust the output of the photovoltaic power generation system, the charge and discharge state of the energy storage system, and the load distribution according to the received strategy instructions;

[0013] A synchronous operation module, which is used to synchronously operate the microgrid with the large power grid;

[0014] A fault prediction module, which is used for data collection and preprocessing, model training and tuning, real-time monitoring and early warning, fault location and analysis, as well as fault handling and feedback.

[0015] Preferably, the working process of the fault prediction module is as follows: collect data from multiple sources at the device layer and the environment layer, clean the collected data, remove outliers and missing values, and perform annotation to provide a high-quality data set for subsequent model training. Select algorithms for model training, and the algorithms include LSTM and Transformer to adapt to different fault prediction scenarios. Use a distributed training framework for model training and use an automated tool for hyperparameter tuning to balance the prediction error and the inference speed. Deploy the trained AI model to the cloud or the edge side, and real-time monitor the operating state of the photovoltaic-storage microgrid. When the model predicts a potential fault, trigger the early warning mechanism and notify the operation and maintenance personnel in time for processing. Use the prediction results of the AI model and the real-time monitoring data to quickly locate the fault point, conduct a preliminary analysis of the fault, judge the fault type and possible causes, and provide a basis for subsequent fault handling. The operation and maintenance personnel quickly take corresponding measures to repair the fault according to the fault information and location provided by the AI model, and feedback the result of the fault handling to the AI model for continuous optimization and improvement of the model, so as to improve the prediction accuracy and reliability of the model.

[0016] Preferably, the working process of the synchronous operation module is as follows: Evaluate the scale, type, power generation capacity, and load demand of the microgrid to ensure that the microgrid has the basic conditions for connecting to the large grid. Connect the microgrid to the large grid through a dedicated grid connection interface device. Select appropriate transmission line materials and capacities, as well as necessary protection devices, according to the distance and load conditions. Establish a communication system with the large grid to ensure that information can be transmitted in a timely and accurate manner. Achieve precise regulation of the energy exchange between the microgrid and the large grid through an automated control system to ensure that the microgrid can act as a controllable power source or load during grid-connected operation and meet the dispatching requirements of the large grid. Follow the regulations of grid dispatching management for planned power generation and load management to ensure the matching operation of the microgrid and the large grid. Develop an emergency plan to deal with possible grid faults or other emergencies to ensure that the microgrid can quickly switch to the off-grid operation mode in case of emergencies and guarantee the power supply to critical loads. Use high-specification inverters and strictly detect the operating status according to the power quality standards to ensure the stable operation of the internal components of the microgrid. Conduct a comprehensive detection of the power quality of the microgrid, including voltage, current, frequency, harmonics, voltage fluctuation and flicker, and DC injection. Apply the comprehensive evaluation method of power quality to evaluate the power quality of the microgrid and adjust the operation strategy of the microgrid according to the evaluation results to ensure that the power quality meets the specified standards.

[0017] Preferably, the fault prediction module includes a data acquisition unit, which is connected to a data preprocessing unit. The data preprocessing unit is connected to a model training unit. The model training unit is connected to a monitoring and warning unit. The monitoring and warning unit is connected to a fault location unit. The fault location unit is connected to a fault handling unit.

[0018] Preferably, the microgrid monitoring module includes an output monitoring unit, which is connected to a charge and discharge state monitoring unit. The charge and discharge state monitoring unit is connected to a load demand monitoring unit. The load demand monitoring unit is connected to a grid parameter monitoring unit.

[0019] Preferably, the grid parameter monitoring unit includes a voltage monitoring unit, which is connected to a frequency monitoring unit. The frequency monitoring unit is connected to a harmonic monitoring unit. The harmonic monitoring unit is connected to a power factor monitoring unit. The power factor monitoring unit is connected to a power quality monitoring unit.

[0020] Preferably, the synchronous operation module includes an evaluation unit, which is connected to an access unit. The access unit is connected to a control system establishment unit. The control system establishment unit is connected to an operation management unit. The operation management unit is connected to a power quality detection unit. The power quality detection unit is connected to a power quality evaluation unit.

[0021] Preferably, the process of adjusting the output of the photovoltaic power generation system is as follows: The operation status of the photovoltaic power generation system is observed in real time through the monitoring system, including the output voltage and current of the photovoltaic modules, the output voltage and current of the inverter, and the grid voltage and frequency parameters. According to the results of system monitoring, the output of the photovoltaic power generation system is adjusted, including adjusting the tilt angle of the photovoltaic modules to maximize solar radiation reception, adjusting the parameters of the inverter to optimize the power conversion efficiency, or balancing the power supply and demand through the charge and discharge of the energy storage system. During the adjustment process, the economy, stability, and safety of the photovoltaic power generation system need to be comprehensively considered. After the adjustment is completed, the output of the photovoltaic power generation system is verified to ensure that the adjustment effect meets the expectations, and it is evaluated by comparing the power generation and system efficiency indicators before and after the adjustment. Continuously monitor the system operation status and promptly discover and handle possible problems.

[0022] Preferably, the process of adjusting the load distribution is as follows: Collect the operation data of the distributed energy storage system in the microgrid, consider factors such as the average load power loss rate and the state of charge balance coefficient, set a multi-objective distribution function for the distributed energy storage system, and use the genetic algorithm to obtain the optimal solution of the multi-objective function under the constraints of SOC, charge and discharge amount, charge and discharge current, etc., so as to obtain the optimal dynamic distribution scheme of the load power of the distributed energy storage system in the microgrid.

[0023] Preferably, the data analysis module includes data analysis methods, and the data analysis methods include time series analysis, regression analysis, clustering analysis, and classification analysis.

[0024] In the present invention, the beneficial effects of the optical storage microgrid energy management system are as follows:

[0025] Collect data from multiple sources at the device layer and the environment layer, clean the collected data, remove outliers and missing values, and perform annotation to provide a high-quality data set for subsequent model training. Select algorithms for model training, and the algorithms include LSTM and Transformer to adapt to different fault prediction scenarios. Use a distributed training framework for model training and use an automated tool for hyperparameter tuning to balance the prediction error and the inference speed. Deploy the trained AI model to the cloud or the edge side, and monitor the operation status of the optical storage microgrid in real time. When the model predicts a potential fault, trigger an early warning mechanism and promptly notify the operation and maintenance personnel for processing. Use the prediction results of the AI model and the real-time monitoring data to quickly locate the fault point, conduct a preliminary analysis of the fault, judge the fault type and possible causes, and provide a basis for subsequent fault handling. The operation and maintenance personnel quickly take corresponding measures to repair the fault according to the fault information and location provided by the AI model, and feedback the results of the fault handling to the AI model for continuous optimization and improvement of the model, so as to improve the prediction accuracy and reliability of the model;

[0026] The present invention can achieve efficient fault prediction and handling in a photovoltaic-storage microgrid, improving the operating efficiency and security of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a block diagram of an energy management system for a photovoltaic-storage microgrid proposed by the present invention;

[0028] Figure 2 It is a block diagram of a fault prediction module of an energy management system for a photovoltaic-storage microgrid proposed by the present invention;

[0029] Figure 3 It is a block diagram of a microgrid monitoring module of an energy management system for a photovoltaic-storage microgrid proposed by the present invention;

[0030] Figure 4 It is a block diagram of a microgrid parameter monitoring unit of an energy management system for a photovoltaic-storage microgrid proposed by the present invention;

[0031] Figure 5 It is a block diagram of a synchronous operation module of an energy management system for a photovoltaic-storage microgrid proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0033] Embodiment 1

[0034] Refer to Figures 1 - 5 , an energy management system for a photovoltaic-storage microgrid, including:

[0035] A microgrid monitoring module, a fault prediction module, a data extraction module, a data analysis module, a strategy formulation module, an operation execution module, and a synchronous operation module;

[0036] The microgrid monitoring module is used to monitor the real-time operation data of the microgrid, including the output of the photovoltaic power generation system, the charge and discharge status of the energy storage system, the load demand, and the grid parameters;

[0037] The data extraction module is used to extract the data monitored by the microgrid monitoring module and transmit the extracted data to the data analysis module;

[0038] The data analysis module is used to analyze the data according to the received real-time operation data in combination with distributed generation prediction and load prediction, and transmit the analyzed data to the strategy formulation module;

[0039] A strategy formulation module, which is used to formulate an optimized scheduling strategy under multiple constraints according to the received analysis data. During the strategy formulation process, the volatility of photovoltaic power generation, the charge and discharge capacity of the energy storage system, the predicted value of load demand, and the operation constraints of the power grid are comprehensively considered;

[0040] An operation execution module, which is used to adjust the output of the photovoltaic power generation system, the charge and discharge state of the energy storage system, and the load distribution according to the received strategy instructions;

[0041] A synchronous operation module, which is used to synchronously operate the microgrid with the large power grid;

[0042] A fault prediction module, which is used for data acquisition and preprocessing, model training and tuning, real-time monitoring and early warning, fault location and analysis, and fault handling and feedback.

[0043] In this embodiment, the working process of the fault prediction module is as follows: collect data from multiple sources at the device layer and the environment layer, clean the collected data, remove outliers and missing values, and perform annotation to provide a high-quality data set for subsequent model training. Select algorithms for model training. The algorithms include LSTM and Transformer to adapt to different fault prediction scenarios. Use a distributed training framework for model training and use an automated tool for hyperparameter tuning to balance the prediction error and the inference speed. Deploy the trained AI model to the cloud or the edge. Real-time monitor the operation status of the photovoltaic-energy storage microgrid. When the model predicts a potential fault, trigger an early warning mechanism and notify the operation and maintenance personnel in time for processing. Use the prediction results of the AI model and real-time monitoring data to quickly locate the fault point, conduct a preliminary analysis of the fault, judge the fault type and possible causes, and provide a basis for subsequent fault handling. The operation and maintenance personnel quickly take corresponding measures to repair the fault according to the fault information and location provided by the AI model, and feedback the result of the fault handling to the AI model for continuous optimization and improvement of the model, so as to improve the prediction accuracy and reliability of the model.

[0044] In this embodiment, the working process of the synchronous operation module is as follows: Evaluate the scale, type, power generation capacity, and load demand of the microgrid to ensure that the microgrid meets the basic conditions for connecting to the large power grid. Connect the microgrid to the large power grid through a dedicated grid connection interface device. Select appropriate transmission line materials and capacities, as well as necessary protection devices, according to the distance and load conditions. Establish a communication system with the large power grid to ensure that information can be transmitted in a timely and accurate manner. Achieve precise regulation of the energy exchange between the microgrid and the large power grid through an automated control system to ensure that the microgrid can act as a controllable power source or load when operating in parallel and meet the dispatching requirements of the large power grid. Follow the regulations of grid dispatching management to conduct planned power generation and load management to ensure the matching operation of the microgrid and the large power grid. Develop an emergency plan to deal with possible grid failures or other emergencies to ensure that the microgrid can quickly switch to the off-grid operation mode in case of emergencies and guarantee the power supply to critical loads. Adopt high-specification inverters and strictly detect the operating status according to the power quality standards to ensure the stable operation of the internal components of the microgrid. Conduct a comprehensive detection of the power quality of the microgrid, including voltage, current, frequency, harmonics, voltage fluctuations and flicker, and DC injection. Apply the comprehensive evaluation method of power quality to evaluate the power quality of the microgrid and adjust the operation strategy of the microgrid according to the evaluation results to ensure that the power quality meets the specified standards.

[0045] In this embodiment, the fault prediction module includes a data acquisition unit, which is connected to a data preprocessing unit. The data preprocessing unit is connected to a model training unit. The model training unit is connected to a monitoring and warning unit. The monitoring and warning unit is connected to a fault location unit. The fault location unit is connected to a fault handling unit.

[0046] In this embodiment, the microgrid monitoring module includes an output situation monitoring unit, which is connected to a charge and discharge state monitoring unit. The charge and discharge state monitoring unit is connected to a load demand monitoring unit. The load demand monitoring unit is connected to a grid parameter monitoring unit.

[0047] In this embodiment, the grid parameter monitoring unit includes a voltage monitoring unit, which is connected to a frequency monitoring unit. The frequency monitoring unit is connected to a harmonic monitoring unit. The harmonic monitoring unit is connected to a power factor monitoring unit. The power factor monitoring unit is connected to a power quality monitoring unit.

[0048] In this embodiment, the synchronous operation module includes an evaluation unit, which is connected to an access unit. The access unit is connected to a control system establishment unit. The control system establishment unit is connected to an operation management unit. The operation management unit is connected to a power quality detection unit. The power quality detection unit is connected to a power quality evaluation unit.

[0049] In this embodiment, the process of adjusting the output of the photovoltaic power generation system is as follows: The operation status of the photovoltaic power generation system is observed in real time through the monitoring system, including the output voltage and current of the photovoltaic modules, the output voltage and current of the inverter, and the grid voltage and frequency parameters. According to the results of system monitoring, the output of the photovoltaic power generation system is adjusted, including adjusting the tilt angle of the photovoltaic modules to maximize solar radiation reception, adjusting the parameters of the inverter to optimize the power conversion efficiency, or balancing power supply and demand through the charge and discharge of the energy storage system. During the adjustment process, the economy, stability, and safety of the photovoltaic power generation system need to be comprehensively considered. After the adjustment is completed, the output of the photovoltaic power generation system is verified to ensure that the adjustment effect meets the expectations. It is evaluated by comparing the power generation and system efficiency indicators before and after the adjustment, and the system operation status is continuously monitored to promptly discover and handle possible problems.

[0050] In this embodiment, the process of adjusting the load distribution is as follows: The operation data of the distributed energy storage system of the microgrid is collected. Considering factors such as the average load power loss rate and the state of charge balance coefficient, a multi-objective distribution function for the distributed energy storage system is set. Under the constraints of SOC, charge and discharge amount, and charge and discharge current, the genetic algorithm is used to obtain the optimal solution of the multi-objective function, thereby obtaining the optimal dynamic distribution plan for the load power of the distributed energy storage system of the microgrid.

[0051] In this embodiment, the data analysis module includes data analysis methods, and the data analysis methods include time series analysis, regression analysis, clustering analysis, and classification analysis.

[0052] Embodiment 2

[0053] The difference between this embodiment and Embodiment 1 is that a photovoltaic-storage microgrid energy management system includes:

[0054] A microgrid monitoring module, a fault prediction module, a data extraction module, a data analysis module, a strategy formulation module, an operation execution module, a synchronous operation module, a data recording module, and a display module;

[0055] The microgrid monitoring module is used to monitor the real-time operation data of the microgrid, including the output of the photovoltaic power generation system, the charge and discharge status of the energy storage system, the load demand, and the grid parameters;

[0056] The data extraction module is used to extract the data monitored by the microgrid monitoring module and transmit the extracted data to the data analysis module;

[0057] The data analysis module is used to analyze the data according to the received real-time operation data, combined with distributed power generation prediction and load prediction, and transmit the analysis data to the strategy formulation module;

[0058] A strategy formulation module, which is used to formulate an optimized scheduling strategy under multiple constraints according to the received analysis data. During the strategy formulation process, the volatility of photovoltaic power generation, the charge and discharge capacity of the energy storage system, the predicted value of load demand, and the operation constraints of the power grid are comprehensively considered;

[0059] An operation execution module, which is used to adjust the output of the photovoltaic power generation system, the charge and discharge state of the energy storage system, and the load distribution according to the received strategy instructions;

[0060] A synchronous operation module, which is used to synchronously operate the microgrid with the large power grid;

[0061] A fault prediction module, which is used for data collection and preprocessing, model training and tuning, real-time monitoring and early warning, fault location and analysis, and fault handling and feedback;

[0062] A data recording module, which is used to record the instructions executed by the operation execution module;

[0063] A display module, which is used to display the execution status of the synchronous operation module.

[0064] The rest is the same as that of the first embodiment.

[0065] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A photovoltaic energy storage microgrid energy management system, characterized in that: include: Microgrid monitoring module, fault prediction module, data extraction module, data analysis module, strategy formulation module, operation execution module, and synchronous operation module; Microgrid monitoring module, used to monitor the real-time operation data of the microgrid, including the output of the photovoltaic power generation system, the charging and discharging status of the energy storage system, load demand and grid parameters; A data extraction module is used to extract the data monitored by the microgrid monitoring module and transmit the extracted data to the data analysis module; The data analysis module is used to analyze the data according to the received real-time operation data in combination with the distributed generation forecast and load forecast, and transmit the analysis data to the strategy formulation module; The strategy formulation module is used to formulate an optimal dispatching strategy under multiple constraints based on the received analysis data. During the strategy formulation process, the volatility of photovoltaic power generation, the charging and discharging capacity of the energy storage system, the predicted value of load demand, and the operation constraints of the power grid are comprehensively considered; The execution module is used to adjust the output of the photovoltaic power generation system, the charging and discharging state of the energy storage system, and the distribution of the load according to the received strategy instructions; Synchronous operation module, used to synchronize the microgrid with the large power grid; The fault prediction module is used for data collection and preprocessing, model training and tuning, real-time monitoring and early warning, fault location and analysis, as well as fault handling and feedback.

2. A photovoltaic energy storage microgrid energy management system according to claim 1, characterized in that: The specific workflow of the fault prediction module is as follows: collect data from multiple sources at the device layer and the environment layer, clean the collected data, remove outliers and missing values, and annotate them to provide a high-quality data set for subsequent model training, select algorithms for model training, including LSTM and Transformer to adapt to different fault prediction scenarios, use a distributed training framework for model training, and use automated tools to tune hyperparameters to balance prediction error and inference speed, deploy the trained AI model to the cloud or edge, monitor the operating status of the photovoltaic storage microgrid in real time, and when the model predicts a potential fault, trigger the early warning mechanism to promptly notify the operation and maintenance personnel to handle it, use the prediction results of the AI ​​model and real-time monitoring data to quickly locate the fault point, conduct a preliminary analysis of the fault, determine the fault type and possible causes, and provide a basis for subsequent fault handling. The operation and maintenance personnel quickly take corresponding measures to repair the fault based on the fault information and location provided by the AI ​​model, and feed back the results of the fault handling to the AI ​​model for continuous optimization and improvement of the model to improve the prediction accuracy and reliability of the model.

3. A photovoltaic energy storage microgrid energy management system according to claim 2, characterized in that: The specific workflow of the synchronous operation module is as follows: evaluate the scale, type, power generation capacity and load demand of the microgrid to ensure that the microgrid has the basic conditions for access to the large power grid, connect the microgrid to the large power grid through a special grid-connected interface device, select appropriate transmission line materials and capacity, and necessary protection devices according to the distance and load conditions, establish a communication system with the large power grid, ensure that information can be transmitted in a timely and accurate manner, and achieve precise regulation of energy exchange between the microgrid and the large power grid through an automated control system to ensure that the microgrid can act as a controllable power source or load when connected to the grid, meet the dispatching needs of the large power grid, follow the regulations of the grid dispatching management, and carry out planned power generation and Load management ensures that the operation of the microgrid matches that of the large grid, formulates emergency plans to deal with possible grid failures or other emergencies, ensures that the microgrid can quickly switch to off-grid operation mode in an emergency, guarantees the power supply of key loads, uses high-specification inverters, and strictly detects the operating status in accordance with power quality standards to ensure the stable operation of the internal components of the microgrid. Comprehensively detect the power quality of the microgrid, including voltage, current, frequency, harmonics, voltage fluctuations and flicker, and DC injection. Apply a comprehensive power quality assessment method to assess the power quality of the microgrid, and adjust the microgrid's operating strategy based on the assessment results to ensure that the power quality meets the specified standards.

4. A photovoltaic energy storage microgrid energy management system according to claim 3, characterized in that: The fault prediction module includes a data acquisition unit, the data acquisition unit is connected to a data preprocessing unit, the data preprocessing unit is connected to a model training unit, the model training unit is connected to a monitoring and early warning unit, the monitoring and early warning unit is connected to a fault locating unit, and the fault locating unit is connected to a fault processing unit.

5. A photovoltaic energy storage microgrid energy management system according to claim 4, characterized in that: The microgrid monitoring module includes an output monitoring unit, the output monitoring unit is connected to a charge and discharge status monitoring unit, the charge and discharge status monitoring unit is connected to a load demand monitoring unit, and the load demand monitoring unit is connected to a grid parameter monitoring unit.

6. A photovoltaic energy storage microgrid energy management system according to claim 5, characterized in that: The grid parameter monitoring unit comprises a voltage monitoring unit, the voltage monitoring unit is connected to a frequency monitoring unit, the frequency monitoring unit is connected to a harmonic monitoring unit, the harmonic monitoring unit is connected to a power factor monitoring unit, and the power factor monitoring unit is connected to a power quality monitoring unit.

7. A photovoltaic energy storage microgrid energy management system according to claim 6, characterized in that: The synchronous operation module includes an evaluation unit, the evaluation unit is connected to an access unit, the access unit is connected to a control system establishment unit, the control system establishment unit is connected to an operation management unit, the operation management unit is connected to a power quality detection unit, and the power quality detection unit is connected to a power quality evaluation unit.

8. A photovoltaic energy storage microgrid energy management system according to claim 7, characterized in that: The process of adjusting the output of the photovoltaic power generation system is as follows: observe the operating status of the photovoltaic power generation system in real time through the monitoring system, including the output voltage and current of the photovoltaic components, the output voltage and current of the inverter, and the voltage and frequency parameters of the power grid. According to the results of system monitoring, adjust the output of the photovoltaic power generation system, including adjusting the tilt angle of the photovoltaic components to maximize the reception of solar radiation, adjusting the parameters of the inverter to optimize the power conversion efficiency, or balancing the supply and demand of electricity through the charging and discharging of the energy storage system. During the adjustment process, it is necessary to comprehensively consider the economy, stability and safety of the photovoltaic power generation system. After the adjustment is completed, verify the output of the photovoltaic power generation system to ensure that the adjustment effect meets expectations. Evaluate by comparing the power generation and system efficiency indicators before and after the adjustment, continuously monitor the system operation status, and promptly discover and deal with possible problems.

9. A photovoltaic energy storage microgrid energy management system according to claim 8, characterized in that: The load distribution adjustment process is as follows: collect the operating data of the microgrid distributed energy storage system, consider the average load power loss rate and the charge state balance coefficient factors, set a distributed energy storage system multi-objective allocation function, and use the genetic algorithm to obtain the optimal solution of the multi-objective function under the constraints of SOC, charge and discharge capacity, and charge and discharge current, so as to obtain the optimal dynamic allocation plan of the load power of the microgrid distributed energy storage system.

10. A photovoltaic energy storage microgrid energy management system according to claim 9, characterized in that: The data analysis module includes data analysis methods, which include time series analysis, regression analysis, cluster analysis and classification analysis.

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