A method and system for energy storage configuration of photovoltaic power generation

Abstract

The present invention relates to the technical field of energy storage configuration, and particularly to a method and system for energy storage configuration of photovoltaic power generation. The method includes the following steps: obtaining information data of a photovoltaic power generation energy site; constructing a distributed synchronous energy storage network for the information data of the photovoltaic power generation energy site to obtain a distributed synchronous energy storage network; collecting real-time power generation data of the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; performing photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; collecting historical electricity load data of the distributed synchronous energy storage network to obtain historical electricity load data; analyzing the curvature trend of the load curve for the historical electricity load data to generate load curve curvature trend data. The present invention improves the accuracy and flexibility of photovoltaic energy storage configuration through real-time photovoltaic power generation prediction, dynamic analysis of load demand, and optimization of adaptive energy storage strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage configuration, and particularly to a method and system for energy storage configuration of photovoltaic power generation. Background Art

[0002] In the early stage, the combination of photovoltaic power generation and energy storage systems mainly relied on traditional battery energy storage technologies, such as lead-acid batteries and nickel-metal hydride batteries. However, these batteries have disadvantages such as low energy density, short lifespan, and poor charge and discharge efficiency, which limit their application in large-scale photovoltaic power stations. With the progress of material science and electrochemistry technology, lithium batteries have become an important energy storage solution. They have high energy density, long lifespan, and fast charge and discharge characteristics, and have gradually become a widely used energy storage method in photovoltaic power generation systems. With the development of power electronics technology, intelligent energy storage systems have gradually emerged. By adopting advanced power conversion technology, intelligent control algorithms, and integrated equipment, the efficiency and reliability of energy storage systems can be effectively optimized. In addition, new energy storage technologies such as flow batteries and compressed air energy storage are also emerging continuously, providing more options for photovoltaic power generation. Currently, the integration of photovoltaic power generation and energy storage technology has gradually entered the stage of systematic and intelligent development. Especially with the application of big data, Internet of Things, and artificial intelligence technologies, the real-time monitoring and optimization management capabilities of photovoltaic energy storage systems have been greatly enhanced, promoting the further development of the photovoltaic power generation industry. However, currently, traditional photovoltaic energy storage systems cannot be flexibly adjusted according to the real-time energy supply-demand ratio. At the same time, energy storage performance testing and strategy adjustment are often lagging, making it difficult to adapt to the rapidly changing power consumption and power generation situations, and thus resulting in low accuracy and flexibility of photovoltaic energy storage configuration. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for energy storage configuration of photovoltaic power generation to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for energy storage configuration of photovoltaic power generation includes the following steps:

[0005] Step S1: Obtain information data of a photovoltaic power generation energy site; construct a distributed synchronous energy storage network for the information data of the photovoltaic power generation energy site to obtain a distributed synchronous energy storage network; collect real-time power generation data for the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data;

[0006] Step S2: Collect historical electricity load data for the distributed synchronous energy storage network to obtain historical electricity load data; perform load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data; classify the historical electricity load data based on the load curve curvature trend data to generate electricity load demand pattern data;

[0007] Step S3: Calculate the energy supply-demand ratio for the electricity load demand pattern data based on the real-time photovoltaic power generation prediction data to obtain energy supply-demand ratio data; optimize the energy storage system configuration of the distributed synchronous energy storage network through the energy supply-demand ratio data to generate photovoltaic power generation energy storage configuration optimization data; perform dynamic energy regulation on the distributed synchronous energy storage network according to the photovoltaic power generation energy storage configuration optimization data, thereby generating an energy storage configuration management and scheduling strategy;

[0008] Step S4: Perform energy storage performance testing on the distributed synchronous energy storage network through the energy storage configuration management and scheduling strategy to generate energy storage performance test data; adjust the adaptive energy storage strategy according to the energy storage performance test data to execute the energy storage configuration optimization operation for photovoltaic power generation.

[0009] The present invention can ensure that the photovoltaic power generation system can monitor and coordinate each energy storage unit in real time by collecting real-time data of photovoltaic power generation sites and establishing a distributed synchronous energy storage network, guarantee the real-time and accuracy of data, improve the reaction speed and coordination ability of the system, and provide basic data support for subsequent power generation prediction and energy storage optimization. By collecting and analyzing historical electricity load data, the changing trend of load demand can be accurately understood, and through the curvature trend analysis of the load curve, the fluctuation law of load demand can be identified. This process helps to classify the electricity load more accurately, so as to better predict the future electricity demand pattern, ensure that the energy storage system can match the load demand, and achieve more efficient energy utilization. By combining the real-time photovoltaic power generation prediction data with the electricity load demand pattern data, the energy supply-demand ratio is calculated, thereby providing an optimization basis for the energy storage system configuration. According to the change of the energy supply-demand ratio, the distributed synchronous energy storage network is dynamically configured and optimized, which can achieve the balance between energy supply and demand, improve the energy storage efficiency, avoid over-energy storage or energy waste, and ensure the efficient operation of the photovoltaic power generation system. By testing the performance of the energy storage system and making adaptive adjustments according to the test results, the energy storage strategy can be adjusted in real time to ensure that the energy storage system can operate efficiently under different load and power generation conditions. This step makes the energy storage system more flexible and intelligent, can adjust the configuration and strategy according to the actual operation data, improve the reliability, stability and lifespan of the system, and optimize the energy management of the photovoltaic power generation system. Therefore, the present invention improves the accuracy and flexibility of photovoltaic energy storage configuration through real-time photovoltaic power generation prediction, dynamic analysis of load demand and optimization of adaptive energy storage strategy.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain information data of photovoltaic power generation energy sites;

[0012] Step S12: Conduct site distribution topology analysis on the information data of photovoltaic power generation energy sites to generate energy site distribution topology data; Based on the energy site distribution topology data, construct a distributed synchronous energy storage network for the information data of photovoltaic power generation energy sites to obtain a distributed synchronous energy storage network;

[0013] Step S13: Collect real-time power generation data of the distributed synchronous energy storage network to obtain real-time photovoltaic power generation data; Perform data preprocessing on the real-time photovoltaic power generation data to generate standard real-time photovoltaic power generation data, where data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization;

[0014] Step S14: Perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data.

[0015] Through the distribution topology analysis of photovoltaic power generation energy sites, the present invention can clearly understand the positions of each site and its relationship with other sites. This is crucial for constructing a distributed synchronous energy storage network. Through this network, the transmission and storage of electric energy can be optimized in real time, avoiding electric energy waste and improving energy utilization efficiency. The collection of real-time photovoltaic power generation data enables the entire system to understand the power generation situation of photovoltaic sites in the first time, facilitating a quick response to any fluctuations in power demand. Data preprocessing (such as cleaning, denoising, missing value filling, etc.) helps to improve the quality of data, ensure the accuracy of subsequent analysis and prediction, and avoid decision-making errors caused by abnormal or incorrect data. Based on the standardized real-time photovoltaic power generation data for photovoltaic power generation prediction, not only can the prediction accuracy of future power generation be improved, but also a scientific basis can be provided for the power dispatching system to make early energy distribution and reserve. Especially when the weather changes or the power demand suddenly increases, the balance between energy supply and demand can be adjusted more precisely. This process realizes the real-time collection, processing, and prediction of energy site information. Combined with advanced distributed energy storage network management, it can improve the intelligent level of the system, enabling it to automatically adjust the operating state according to real-time data and prediction results, and maximizing the efficiency and stability of energy supply.

[0016] Preferably, constructing a distributed synchronous energy storage network for the information data of photovoltaic power generation energy sites based on the energy site distribution topology data includes:

[0017] Based on graph theory methods, topological modeling is performed on the topological data of the energy site distribution to generate energy site topological model data; node importance evaluation is carried out on the energy site topological model data to generate topological node importance data; according to the topological node importance data, node annotation is performed on the energy site topological model data to generate key nodes and secondary nodes.

[0018] According to the articulation points and secondary nodes, edge connection is performed on the energy site topological model data to generate an initial energy storage network; distributed node deployment is carried out on the initial energy storage network to generate a distributed energy storage network; three-layer data synchronization is performed on the distributed energy storage network using the information data of the photovoltaic power generation energy sites, thereby generating a distributed synchronous energy storage network, where the three-layer data synchronization includes time synchronization, power synchronization, and status synchronization.

[0019] Through energy site distribution topological modeling based on graph theory methods, the present invention can more accurately describe the relationships between energy sites, revealing the structural and functional characteristics of different sites in the entire network. Node importance evaluation further helps identify the sites (key nodes) that are crucial to the entire energy storage network and the relatively less important sites. This evaluation enables the energy storage network to concentrate resources and prioritize the optimization of key nodes, avoiding over-complication of the system or waste of resources. Labeling key nodes and secondary nodes makes the design of the energy storage network more targeted. Edge connection based on the roles of nodes generates an initial energy storage network, laying the foundation for subsequent distributed deployment. Such a design can better ensure the connectivity of the network and the stable flow of energy, ensuring that energy storage resources can be allocated according to actual needs. Through distributed node deployment in the network, the functions of energy storage and release can be provided at different locations, reducing the risk of single-point failures and enhancing the robustness and stability of the entire system. The distributed energy storage network enables each photovoltaic power generation site to be flexibly adjusted according to local demands, optimizing the overall energy management. Ensuring that each node in the energy storage network operates within a unified time frame, avoiding scheduling errors or data inconsistencies caused by time differences. Time synchronization improves the system response speed and accuracy. Through power synchronization, it can be ensured that the power outputs of each energy storage node are coordinated under the same standard, avoiding overcharging or over-discharging and enhancing the efficiency and lifespan of the energy storage system. Synchronizing the operating states of each node ensures that the entire network operates under the same operating conditions, maximizing the working efficiency and stability of the energy storage network. This distributed synchronous energy storage network design can perform real-time scheduling and optimization according to different time, demand, power, and status conditions, enhancing the system's adaptability to power demand fluctuations, faults, or unexpected events. The system can flexibly respond to various operating conditions, improving the accuracy and efficiency of overall energy management.

[0020] Preferably, step S14 includes the following steps:

[0021] Step S141: Extract basic photovoltaic power generation characteristics from the standard real-time photovoltaic power generation data to obtain basic photovoltaic power generation characteristic data; perform time series characteristic analysis on the basic photovoltaic power generation characteristic data to generate photovoltaic power generation time series characteristic data;

[0022] Step S142: Divide the photovoltaic power generation time series characteristic data into a data set to generate a model training set and a model test set; train the model training set through the long short-term memory neural network algorithm to generate a photovoltaic power generation prediction pre-model; optimize and iterate the photovoltaic power generation prediction pre-model through the model test set to generate a photovoltaic power generation prediction model;

[0023] Step S143: Import the standard real-time photovoltaic power generation data into the photovoltaic power generation prediction model for photovoltaic power generation prediction to generate real-time photovoltaic power generation prediction data.

[0024] The present invention extracts key power generation characteristics (such as sunlight intensity, temperature, humidity, etc.) from standard real-time photovoltaic power generation data, providing high-quality input data for subsequent prediction models. This step helps reduce the interference of irrelevant factors on the prediction results and ensures the accuracy and reliability of the model. By performing time series analysis on the basic photovoltaic power generation characteristic data, the laws and periodic characteristics (such as seasonal fluctuations, day-night variations, etc.) of the photovoltaic power generation data changing over time are extracted. These time series characteristics are crucial factors in photovoltaic power generation prediction. Accurately capturing the time series characteristics helps improve the long-term prediction ability of the prediction model, especially when facing seasonal fluctuations. By dividing the photovoltaic power generation time series characteristic data into a training set and a test set, the data independence during the training process can be ensured, avoiding overfitting. The training set is used for the learning of the model, while the test set is used to evaluate the generalization ability of the model, ensuring the actual application effect of the model. LSTM is a commonly used time series prediction model that can effectively handle the time-dependent relationships in photovoltaic power generation data, capturing long-term time dependencies and short-term change trends. By training the training set through the LSTM algorithm, the model can identify and learn the complex time series patterns in the photovoltaic power generation data, thereby improving the prediction accuracy. By optimizing and iterating the prediction pre-model on the model test set, the model can gradually adjust and correct its parameters, reducing the prediction error. This process can effectively improve the accuracy and robustness of the model, especially when dealing with different weather, seasons, or sudden changes, enabling the model to maintain a high prediction accuracy in different environments. The model can continuously adapt to new input data and environmental changes during the iteration process. With the accumulation of more historical data, the prediction accuracy and adaptive ability of the model will continuously improve. This enables the system to better handle the photovoltaic power generation prediction tasks in different regions and under different climate conditions. By inputting the standard real-time photovoltaic power generation data into the photovoltaic power generation prediction model for prediction, a real-time estimation of the current and future power generation situations can be provided. In this way, the photovoltaic power generation system can dynamically adjust the power distribution according to the prediction data, optimize the energy storage and discharge strategies, thereby improving the stability of the power grid and the balance of power supply and demand.

[0025] Preferably, step S2 includes the following steps:

[0026] Step S21: Collect historical electricity load data of the distributed synchronous energy storage network to obtain historical electricity load data;

[0027] Step S22: Extract spatio-temporal load characteristics from the historical electricity load data to obtain electricity spatio-temporal load characteristic data; analyze the seasonal change laws of the electricity spatio-temporal load characteristic data to generate a load characteristic data set; convert the historical electricity load data according to the load characteristic data set to generate a historical electricity load curve;

[0028] Step S23: Conduct a load curve curvature trend analysis on the historical electricity consumption load curve to generate load curve curvature trend data, where the load curve curvature trend data includes positive trend curvature, flat trend curvature, and negative trend curvature;

[0029] Step S24: Based on the positive trend curvature, flat trend curvature, and negative trend curvature, segment the historical electricity consumption load curve to generate a high-load trend curve, a flat-load trend curve, and a low-load trend curve; classify the historical electricity consumption load data according to the high-load trend curve, flat-load trend curve, and low-load trend curve to generate electricity consumption load demand pattern data.

[0030] By collecting historical electricity consumption load data, the present invention can comprehensively understand the change trend and periodic characteristics of the load, providing basic data for subsequent analysis and decision-making. The historical data provides real electricity consumption behaviors for the model, ensuring the reliability of the analysis. By extracting spatio-temporal features from the historical load data, the changing rules of electricity consumption load over time (such as daily, weekly, seasonal) and space (such as different regions, different user groups) can be revealed. This helps to deeply understand the dynamic changes in electricity consumption demand and supports more accurate load forecasting. By analyzing the seasonal change rules of electricity consumption load and identifying the volatility of the load in different seasons, the impact of seasonal changes on electricity consumption load can be accurately captured. For example, the air-conditioning load increases in summer and the heating load increases in winter. Accurately grasping these rules helps to make load scheduling in advance and optimize energy storage and power generation plans. The load characteristic data set generated by seasonal analysis provides a more detailed description of the load pattern for the system, helping to make more accurate load forecasting in different seasons or weather conditions. By performing curve conversion on the historical electricity consumption load data, the original load data can be transformed into a more regular load curve. This conversion helps the system to identify the load change trend and peak-valley fluctuations, further supporting subsequent trend analysis and classification. By analyzing the curvature trend of the load curve, different trends in load changes can be clearly identified: positive trend (load increasing), flat trend (load stable), and negative trend (load decreasing). This provides important guiding information for subsequent load demand forecasting and helps to predict the future change trend of electricity consumption demand. By segmenting the load curve and dividing the load into three trends: high load, flat load, and low load, it helps to independently analyze different load scenarios. This segmentation enables the system to adopt different energy storage and power dispatching strategies in different load states, enhancing the flexibility and adaptability of dispatching. Based on the segmentation of the load curve, the system can classify the load demand patterns to form clear electricity consumption load demand pattern data. These pattern data provide a basis for power dispatching and energy storage management, and the optimal load forecasting and dispatching schemes can be implemented according to different patterns to avoid power waste and improve energy utilization efficiency.

[0031] Preferably, step S3 includes the following steps:

[0032] Step S31: Calculate the energy supply - demand ratio for the electricity load demand pattern data based on the real - time photovoltaic power generation prediction data to obtain the energy supply - demand ratio data. The formula for calculating the energy supply - demand ratio is as follows:

[0033]

[0034] In the formula, represents the energy supply - demand ratio, represents the photovoltaic power generation at time represents the power output of the energy storage device at time represents the power obtained from the power grid at time represents the power demand of the electricity load at time

[0035] Step S32: Compare the energy supply - demand ratio data with the preset standard supply - demand ratio threshold. When the energy supply - demand ratio data is greater than the preset standard supply - demand ratio threshold, a power supply surplus mode is generated; when the energy supply - demand ratio data is less than the preset standard supply - demand ratio threshold, a power supply gap mode is generated.

[0036] Step S33: Optimize the energy storage system configuration of the distributed synchronous energy storage network based on the power supply surplus mode and the power supply gap mode to generate the optimized data for photovoltaic power generation energy storage configuration.

[0037] Step S34: Perform dynamic energy regulation on the distributed synchronous energy storage network according to the optimized data for photovoltaic power generation energy storage configuration, thereby generating an energy storage configuration management and scheduling strategy.

[0038] ​​​​The present invention calculates the energy supply and demand ratio R(t), and the system can evaluate the supply and demand balance of energy in real time. The calculation formula integrates the photovoltaic power generation power, the output of the energy storage device, the power obtained by the power grid and the power load demand, ensuring a comprehensive consideration of the supply and demand situation. This comprehensive calculation method helps to understand the energy supply situation in real time and provide more accurate basic data for subsequent decision-making. By combining the power output of photovoltaic power generation, energy storage system and power grid, the formula can accurately reflect the contribution of each energy source in meeting the power load demand. This can not only help identify the shortage of energy supply, but also effectively evaluate the role of energy storage and power grid under different load conditions. By comparing the energy supply and demand ratio calculated in real time with the preset standard supply and demand ratio threshold, the system can automatically generate a power supply surplus mode or a power supply gap mode. This automatic identification mechanism helps to quickly respond to the difference between power load demand and energy supply, and provides timely feedback for subsequent energy management and scheduling decisions. When the energy supply and demand ratio is greater than the preset threshold, it indicates that the system has a power supply surplus. At this time, the system can store excess energy to avoid energy waste or over-reliance on the power grid, and enhance the energy self-sufficiency of the system. When the energy supply-demand ratio is lower than the preset threshold, it indicates that the energy supply is insufficient and a power supply gap occurs. At this time, the system will trigger the corresponding adjustment strategy to activate the backup power supply or supplement energy to ensure that the user's power demand is met. Based on the power supply surplus mode and the power supply gap mode, the system can optimize the energy storage system configuration of the distributed synchronous energy storage network. This optimization process can ensure that the energy storage equipment has sufficient power storage capacity during peak load periods and does not waste too much power during low demand periods. Optimized configuration helps to improve the economy and efficiency of the energy storage system and reduce the situation of excessive or insufficient energy storage. By optimizing the configuration of the energy storage system, the system can ensure the efficient storage and release of energy, avoid overcharging of energy storage equipment when there is a power surplus or insufficient energy storage when there is a power supply gap, and improve the overall utilization of the energy storage system. Energy storage configuration optimization can be dynamically adjusted according to different power supply surplus modes and power supply gap modes to ensure that the energy storage system can achieve maximum benefits under different load demands. For example, when the load demand is high, the energy storage equipment can provide additional support to the system; when the load demand is low, the energy storage system can back up future load demand by charging. Through the generated energy storage configuration management and scheduling strategy, the system can dynamically adjust energy storage configuration and energy scheduling based on real-time data. This dynamic adjustment mechanism can flexibly respond to different power demand situations according to real-time changes in load demand, photovoltaic power generation, energy storage status and grid conditions, thereby improving the system's response speed and adjustment capabilities.

[0039] Preferably, step S33 includes the following steps:

[0040] Step S331: Confirm the mode time periods of the distributed synchronous energy storage network based on the power supply surplus mode and the power supply gap mode to obtain the power supply surplus time period and the power supply gap time period; conduct a buffer time period analysis on the power supply surplus time period and the power supply gap time period to generate a power supply change buffer time period;

[0041] Step S332: Set the energy discharge for the power supply surplus time period to generate an energy discharge curve; set the energy charge for the power supply gap time period to generate an energy charge curve; perform curve fitting on the energy discharge curve and the energy charge curve according to the power supply change buffer time period to generate buffer time period energy loss data;

[0042] Step S333: Optimize the energy storage battery configuration of the distributed synchronous energy storage network based on the buffer time period energy loss data, thereby generating optimized data for the photovoltaic power generation energy storage configuration.

[0043] By confirming the mode time periods for the power supply surplus mode and the power supply gap mode, the system can accurately identify the power supply status in different time periods. The confirmation of the power supply surplus time period and the power supply gap time period provides a clear time frame for subsequent energy storage scheduling, enabling the system to adopt different adjustment strategies in different time periods. This time period confirmation mechanism can respond in real time to the fluctuations of photovoltaic power generation and electrical load, and flexibly adjust the charge and discharge strategies of energy storage devices. When there is a power supply surplus, the energy storage device can be charged, and when there is a power supply gap, it can be discharged, ensuring the stability and economy of power supply. Between the power supply surplus and the gap, there are certain buffer periods. By analyzing these buffer periods, the system can effectively evaluate the energy transition period and perform fine energy regulation during this period. This process helps to alleviate power supply fluctuations, avoid over-reliance on the power grid or energy storage system, and ensure the smooth transition of the energy system. The analysis of buffer periods can identify potential losses during the energy discharge or charge process, optimize the energy flow during discharge and charge, and reduce unnecessary energy losses. This can further improve the utilization efficiency of energy storage devices and avoid energy waste during the transition period. By setting energy discharge for the power supply surplus time period and energy charge for the power supply gap time period, the system can generate accurate energy discharge curves and charge curves. These curves are dynamically adjusted based on the real-time load demand and the change of photovoltaic power generation, ensuring that the energy storage device can efficiently perform discharge and charge operations. During the buffer period, the energy discharge and charge curves are fitted to generate energy loss data for the buffer period. This process helps the system to identify the energy loss during the buffer period and minimize this loss through optimization strategies, improving the overall efficiency of the system. By generating energy loss data for the buffer period, the system can quantify the energy loss that occurs during the scheduling process. This data can be used to further optimize the charge and discharge strategies, especially during the adjustment of the buffer period, to reduce unnecessary energy losses. By optimizing the energy storage battery configuration based on the energy loss data for the buffer period, the system can achieve a more refined allocation of the energy storage battery capacity and charge and discharge capabilities. This optimization ensures that the energy storage device can work efficiently under different supply and demand modes, avoids overcharging or over-discharging, and improves the service life and energy storage efficiency of the battery.

[0044] Preferably, step S34 includes the following steps:

[0045] Step S341: Calculate the node energy redundancy of the distributed synchronous energy storage network according to the optimized data of photovoltaic power generation energy storage configuration to obtain node energy redundancy data; analyze the adjacent node status of the distributed synchronous energy storage network through the node energy redundancy data to generate adjacent node status data;

[0046] Step S342: Use the adjacent node status data to allocate energy scheduling weights to the node energy redundancy data, and generate energy scheduling weight allocation data;

[0047] Step S343: Screen the adjacent node status data through the energy scheduling weight allocation data to obtain emergency energy node data; Analyze the emergency scheduling energy storage path for the emergency energy node data according to the node energy redundancy data, and generate emergency node scheduling energy storage path data;

[0048] Step S344: Based on the emergency node scheduling energy storage path data, perform scheduling energy storage uniform control on the distributed synchronous energy storage network, so as to generate an energy storage configuration management scheduling strategy.

[0049] Through the node energy redundancy calculation, the present invention can analyze the remaining energy reserves of each node to ensure that the energy supply of each node in the system is not insufficient. This process helps to discover the surplus or shortage of resources in the network and adjust the energy storage strategy in a timely manner. By analyzing the status of adjacent nodes, the system can evaluate the energy sharing and support relationship with adjacent nodes. The generation of adjacent node status data can help the system understand the energy flow status between nodes and provide a reference for subsequent energy scheduling to ensure the efficient allocation and utilization of energy in the network. Using the adjacent node status data to allocate scheduling weights to the node energy redundancy can dynamically adjust the energy scheduling priority between nodes according to the energy sufficiency of each node and the energy demand in the network. Through this weight allocation, the system can flexibly schedule between different nodes and optimize the discharge and charge strategies of energy storage devices. Through the energy scheduling weight allocation data and the adjacent node status data, it is possible to screen out emergency nodes with sudden increase in energy demand and energy shortage. These emergency nodes are caused by factors such as insufficient photovoltaic power generation and grid load fluctuations, and need to be scheduled and optimized first. Analyze the energy storage path of the emergency energy node through the node energy redundancy data to generate the energy storage scheduling path data of the emergency node. This process helps the system accurately plan the energy scheduling path from different energy storage nodes to the emergency node to ensure that the emergency node can quickly obtain energy support and avoid system operation instability caused by energy shortage. Based on the emergency node scheduling energy storage path data, the distributed synchronous energy storage network can implement energy storage uniform control, that is, balance the allocation of energy storage capacity between different nodes to avoid overloading or overabundance of a single node. This balanced control helps to improve the overall efficiency and stability of the system and ensure that the energy storage device can reach the best state under various supply and demand conditions.

[0050] Preferably, step S4 includes the following steps:

[0051] Step S41: Execute the policy on the distributed synchronous energy storage network through the energy storage configuration management scheduling policy to generate the energy storage policy execution data for photovoltaic power generation; perform energy storage performance tests on the energy storage policy execution data for photovoltaic power generation to generate energy storage performance test data;

[0052] Step S42: According to the energy storage performance test data, adaptively adjust the energy storage policy for the energy storage policy execution data of photovoltaic power generation to perform the energy storage configuration optimization operation of photovoltaic power generation.

[0053] By executing the energy storage configuration management scheduling policy, the present invention can dynamically adjust the working mode of the energy storage system according to the real-time state and requirements of the distributed synchronous energy storage network. Through the execution of this policy, the energy reserve and utilization efficiency of the photovoltaic power generation system can be maximized, ensuring the stable energy supply of the system under different load demands. The execution of the energy storage policy can not only ensure the timely storage and release of photovoltaic power generation, but also coordinate the energy flow among photovoltaic power generation, energy storage devices and the power grid, optimize the energy utilization efficiency, thereby reducing energy waste and system operation costs. Through the test of the energy storage performance, various performance indicators of the energy storage system can be monitored in real time, including energy storage efficiency, discharge capacity, charging speed and system response speed. This test process ensures the stable operation of the energy storage system under different working conditions and timely discovers potential performance bottlenecks. The generation of the energy storage performance test data can help the system identify the weak links in the energy storage process, and timely optimize or adjust the links that do not meet the performance requirements, improving the stability and reliability of the entire system. The adaptive adjustment of the energy storage policy according to the energy storage performance test data can ensure that the energy storage system is always in the best operating state. This adaptive adjustment ability can intelligently optimize the energy storage policy according to the actual performance of the energy storage device, the volatility of photovoltaic power generation and the change of electricity demand, avoiding the situation of excessive or insufficient energy reserve. The adjustment of the adaptive energy storage policy not only improves the operation efficiency of the energy storage device, but also ensures that the system can still maintain a stable operating state in the face of load demand fluctuations or photovoltaic power generation fluctuations. Whether it is the change of light intensity or the fluctuation of the electrical load, the system can intelligently schedule to avoid the instability of power supply caused by the imbalance of the energy storage system.

[0054] In this specification, an energy storage configuration system for photovoltaic power generation is provided for executing the above-mentioned energy storage configuration method for photovoltaic power generation. The energy storage configuration system for photovoltaic power generation includes:

[0055] A power generation prediction module, which is used to obtain information data of photovoltaic power generation energy sites; construct a distributed synchronous energy storage network for the information data of photovoltaic power generation energy sites to obtain a distributed synchronous energy storage network; collect real-time power generation data for the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data;

[0056] An electricity consumption trend analysis module, which is used to collect historical electricity load data for the distributed synchronous energy storage network to obtain historical electricity load data; perform load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data; classify load demands for the historical electricity load data based on the load curve curvature trend data to generate electricity load demand pattern data;

[0057] An energy storage configuration control module, which is used to calculate the energy supply-demand ratio for the electricity load demand pattern data according to the real-time photovoltaic power generation prediction data to obtain energy supply-demand ratio data; optimize the energy storage system configuration for the distributed synchronous energy storage network through the energy supply-demand ratio data to generate optimized data for photovoltaic power generation energy storage configuration; perform dynamic energy regulation on the distributed synchronous energy storage network according to the optimized data for photovoltaic power generation energy storage configuration, thereby generating an energy storage configuration management and scheduling strategy;

[0058] An energy storage configuration optimization module, which is used to perform energy storage performance testing on the distributed synchronous energy storage network through the energy storage configuration management and scheduling strategy to generate energy storage performance test data; adjust the adaptive energy storage strategy according to the energy storage performance test data to execute the optimized operation of the energy storage configuration for photovoltaic power generation.

[0059] The beneficial effects of the present invention are as follows: By obtaining the information data of photovoltaic power generation energy sites and constructing a distributed synchronous energy storage network, the power generation data of each site can be collected in real time. This provides high-precision basic data support for subsequent energy scheduling and energy storage management. By predicting the real-time photovoltaic power generation data, the power generation plan can be pre-planned under unstable weather and electrical load conditions, reducing energy waste, optimizing energy storage and power scheduling, and ensuring stable power supply. By extracting the spatio-temporal characteristics of historical electrical load data and analyzing the curvature trend of the load curve, the fluctuation pattern of the electrical load can be revealed, helping to predict future electrical load demands, which is particularly important in the case of large seasonal fluctuations. By classifying the load curve trend data, different electrical load demand patterns can be identified, helping to optimize the allocation and scheduling of power resources, ensuring uninterrupted energy supply under different demand patterns, and avoiding energy waste. By combining the real-time photovoltaic power generation prediction data with the electrical load demand pattern data, the energy supply-demand ratio is calculated, helping to timely identify the imbalance between supply and demand in the power system. This can effectively predict the situation of power surplus or shortage and take measures in advance. According to the energy supply-demand ratio data, the energy storage system is optimized in real time, and the charge and discharge strategies of the energy storage devices are adjusted to ensure the balance between photovoltaic power generation and load demand. This not only improves the energy utilization efficiency but also avoids the overuse or inefficient operation of the energy storage devices. Through the energy storage performance test, the system can monitor the efficiency and status of the energy storage devices in real time, ensuring that the energy storage system is always in the best operating state. Through data feedback, potential problems of the energy storage devices can be quickly identified, reducing the probability of failures. According to the energy storage performance test data, the energy storage strategy is adaptively adjusted to ensure that the system can flexibly respond and optimize the power storage and scheduling strategies in the face of different electrical loads and power generation conditions. Therefore, the present invention improves the accuracy and flexibility of photovoltaic energy storage configuration through real-time photovoltaic power generation prediction, dynamic analysis of load demands, and optimization of adaptive energy storage strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic flow chart of the steps of an energy storage configuration method for photovoltaic power generation;

[0061] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in

[0062] Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in

[0063] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0065] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0066] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0067] To achieve the above object, please refer to Figures 1 to 3 , an energy storage configuration method for photovoltaic power generation, the method comprising the following steps:

[0068] Step S1: Obtain photovoltaic power generation energy site information data; construct a distributed synchronous energy storage network for the photovoltaic power generation energy site information data to obtain a distributed synchronous energy storage network; collect real-time power generation data for the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data;

[0069] Step S2: Collect historical power consumption load data for the distributed synchronous energy storage network to obtain historical power consumption load data; perform load curve curvature trend analysis on the historical power consumption load data to generate load curve curvature trend data; classify the historical power consumption load data based on the load curve curvature trend data to generate power consumption load demand pattern data;

[0070] Step S3: Calculate the energy supply-demand ratio for the electricity load demand pattern data based on the real-time photovoltaic power generation prediction data to obtain the energy supply-demand ratio data; optimize the energy storage system configuration of the distributed synchronous energy storage network through the energy supply-demand ratio data to generate the optimized data for photovoltaic power generation energy storage configuration; perform dynamic energy regulation on the distributed synchronous energy storage network according to the optimized data for photovoltaic power generation energy storage configuration, thereby generating the energy storage configuration management and scheduling strategy.

[0071] Step S4: Perform energy storage performance testing on the distributed synchronous energy storage network through the energy storage configuration management and scheduling strategy to generate the energy storage performance test data; adjust the adaptive energy storage strategy according to the energy storage performance test data to execute the optimized operation of the energy storage configuration for photovoltaic power generation.

[0072] The present invention collects the real-time data of the photovoltaic power generation site and establishes a distributed synchronous energy storage network, which can ensure that the photovoltaic power generation system can monitor and coordinate each energy storage unit in real time, guarantee the real-time and accuracy of the data, improve the reaction speed and coordination ability of the system, and provide basic data support for subsequent power generation prediction and energy storage optimization. By collecting and analyzing the historical electricity load data, the changing trend of the load demand can be accurately understood, and through the curvature trend analysis of the load curve, the fluctuation law of the load demand can be identified. This process helps to classify the electricity load more accurately, thereby better predicting the future electricity demand pattern, ensuring that the energy storage system can match the load demand, and realizing more efficient energy utilization. By combining the real-time photovoltaic power generation prediction data with the electricity load demand pattern data, the energy supply-demand ratio is calculated, thereby providing an optimization basis for the energy storage system configuration. According to the change of the energy supply-demand ratio, the dynamic configuration optimization of the distributed synchronous energy storage network can achieve the balance between energy supply and demand, improve the energy storage efficiency, avoid over-energy storage or energy waste, and ensure the efficient operation of the photovoltaic power generation system. By testing the performance of the energy storage system and making adaptive adjustments according to the test results, the energy storage strategy can be adjusted in real time to ensure that the energy storage system can operate efficiently under different load and power generation conditions. This step makes the energy storage system more flexible and intelligent, can adjust the configuration and strategy according to the actual operation data, improves the reliability, stability and lifespan of the system, and simultaneously optimizes the energy management of the photovoltaic power generation system. Therefore, the present invention improves the accuracy and flexibility of the photovoltaic energy storage configuration through real-time photovoltaic power generation prediction, dynamic analysis of load demand and optimization of adaptive energy storage strategy.

[0073] In the embodiment of the present invention, with reference to Figure 1 shown in the figure, it is a schematic flow chart of the steps of a method for energy storage configuration of photovoltaic power generation according to the present invention. In this example, the method for energy storage configuration of photovoltaic power generation includes the following steps:

[0074] Step S1: Obtain the information data of the photovoltaic power generation energy site; construct a distributed synchronous energy storage network for the information data of the photovoltaic power generation energy site to obtain a distributed synchronous energy storage network; collect real-time power generation data for the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data;

[0075] In the embodiment of the present invention, the basic information of each photovoltaic power generation site, such as geographical location, equipment type, capacity, historical power generation data, weather data, solar radiation amount, etc., is obtained from the photovoltaic power generation system management platform or the sensor network. According to different site types, a unified data structure is constructed, including information such as site number, installation date, location coordinates, system type, historical power generation curve, etc. A distributed energy storage system is constructed among multiple photovoltaic power generation sites, and real-time data synchronization is used for energy storage management. Use distributed data synchronization protocols (such as NTP, P2P protocol, etc.) to ensure that the data collected from different sites is real-time synchronized. This process can be completed by establishing a data exchange center or a central control system. According to the power generation situation and energy storage requirements of the site, dynamically adjust the charge and discharge strategies of the energy storage devices at each site. The management of the energy storage network needs to consider the load conditions of multiple sites, and use edge computing or cloud computing platforms for data processing and decision-making. A variety of sensors (such as current, voltage, temperature, radiation sensors, etc.) are arranged at each photovoltaic power generation site, and the data is transmitted to the central data processing platform through wireless transmission or wired network. The collected data should include power output, solar radiation intensity, temperature, etc., and be stored in a unified standard data format (such as JSON, CSV or time series database format) for subsequent analysis and processing. The power generation situation of each site is monitored in real time through the central monitoring platform, and the collected data is converted into real-time power output data. Preprocessing operations such as denoising, filling missing values, and standardization are performed on the collected real-time photovoltaic power generation data to ensure data quality. A machine learning model (such as LSTM, support vector machine, random forest, etc.) is used to predict the standard real-time photovoltaic power generation data. The model is trained according to input variables such as historical power generation data, weather forecast, solar radiation intensity, etc., and real-time prediction is performed. Since photovoltaic power generation is greatly affected by weather changes, LSTM (Long Short-Term Memory network) is an effective method for processing time series data and is suitable for capturing long-term dependencies. The prediction accuracy of the model is evaluated through methods such as cross-validation, and the model parameters are optimized. The trained prediction model is used to generate photovoltaic power generation prediction data for a future period of time (such as 1 hour, 6 hours, 24 hours). These data can include information such as the predicted power generation amount of each photovoltaic power generation site and the impact of expected weather changes on power generation.

[0076] Step S2: Collect historical electricity load data for the distributed synchronous energy storage network to obtain historical electricity load data; perform load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data; classify the historical electricity load data based on the load curve curvature trend data to generate electricity load demand pattern data;

[0077] In the embodiments of the present invention, historical power consumption load data of each photovoltaic power generation site and its supporting energy storage devices is collected through the monitoring platform or intelligent meter system of the distributed synchronous energy storage network. These data include daily, hourly or minute-by-minute power demands, as well as the charge and discharge states of the energy storage system. The historical power consumption load data should cover a certain time span (such as 1 month, 1 year, etc.) to ensure that it can reflect the periodic changes and load fluctuations of the power consumption load. The collected load data should include information such as current, voltage, power, and energy storage device status at each time point, and is usually stored in the form of time series data, and standard formats (such as CSV, JSON, etc.) are used for data recording. Noise or outliers in the data are removed through filtering methods (such as mean filtering, Kalman filtering, etc.) to ensure the smoothness and accuracy of the data. Interpolation processing is performed on the missing historical power consumption load data, such as using linear interpolation or model-based prediction to fill in the missing values. The historical load data is normalized or standardized so that data in different dimensions in subsequent analysis is comparable. Based on the historical power consumption load data, a load curve graph is generated to describe the load changes at different time scales (such as daily, weekly, monthly). Time series analysis methods (such as STL decomposition method, Holt-Winters seasonal smoothing method, etc.) are used to decompose the load curve into parts such as long-term trend, seasonal variation, periodic fluctuation, and random error. Polynomial fitting, B-spline fitting or other smooth curve fitting techniques are used to fit the historical load curve to obtain a smooth load curve. Calculate the curvature of the load curve, that is, the rate of change of the load change. Curvature is an important feature reflecting the change trend of the curve, which can help identify the severity of load fluctuations. By calculating the curvature trend of the load curve in different time periods, the volatility pattern of the load change is identified. For example, the curvature value is larger during some peak load periods, indicating that the demand fluctuates violently; while during low load periods, the curvature is smaller, indicating that the demand is relatively stable. Through curvature analysis, load curve curvature trend data is generated, and this data set contains the curvature values of load changes at different time points, as well as the time evolution trend of these curvature values. According to the curvature trend data of the load curve, the historical power consumption load data is divided into different demand patterns. For example, it can be divided into peak load pattern, stable load pattern, low valley load pattern, etc. according to the volatility of the curvature. Clustering analysis (such as K-means, DBSCAN and other algorithms) is used to classify the curvature trend data of the load curve, and different load demand patterns are assigned to different categories. The curvature trend is used as a feature and input into a machine learning classification model (such as decision tree, support vector machine, etc.) for automatic classification of the load demand pattern. The accuracy of the classification result is evaluated through methods such as cross-validation, and the model parameters are adjusted to optimize the classification effect. According to the classification result, power consumption load demand pattern data is generated. Each historical time period will be labeled as a specific demand pattern type (such as peak, stable, low valley, etc.).

[0078] Step S3: Calculate the energy supply - demand ratio for the electricity load demand pattern data based on the real - time photovoltaic power generation prediction data to obtain the energy supply - demand ratio data; optimize the energy storage system configuration of the distributed synchronous energy storage network through the energy supply - demand ratio data to generate the optimized data for photovoltaic power generation energy storage configuration; perform dynamic energy regulation on the distributed synchronous energy storage network according to the optimized data for photovoltaic power generation energy storage configuration, thereby generating the energy storage configuration management scheduling strategy.

[0079] In the embodiment of the present invention, by setting the ratio of photovoltaic power generation to electricity demand, the energy supply - demand ratio data for each time point is generated. With the goal of maximizing the overall energy efficiency of the system, the purpose of optimizing the energy storage system configuration is to rationally configure the charging and discharging strategies of energy storage devices according to the supply - demand ratio. The goal is to minimize energy losses while ensuring that the energy storage system can meet the load demand when photovoltaic power generation is insufficient and can be effectively charged when power generation is excessive. Use linear programming, dynamic programming, or other optimization algorithms to optimize the configuration of energy storage devices according to the energy supply - demand ratio data. Based on the optimized data for photovoltaic power generation energy storage configuration, the system needs to adjust the charging and discharging strategies of energy storage devices in real - time to achieve the optimal matching of energy supply and load demand. The goal of dynamic regulation is to dynamically adjust the operating state of energy storage devices according to the real - time supply - demand ratio and the optimized energy storage configuration data. In the power supply gap mode, the charging operation of energy storage devices is preferentially executed; in the power supply surplus mode, the discharging operation of energy storage devices is preferentially executed until the load demand is met. According to the real - time supply - demand ratio, dynamically adjust the charging and discharging power of energy storage devices. For example: when the photovoltaic power generation exceeds the load demand, the energy storage device starts to charge. When the photovoltaic power generation is insufficient to meet the load demand, the energy storage device discharges. When the photovoltaic power generation and the load demand are roughly balanced, the energy storage device is in a standby or charge / discharge balance state. According to the above - mentioned dynamic regulation model, formulate a detailed energy storage configuration management scheduling strategy. The scheduling strategy should include the energy storage charging and discharging plan for each time period to ensure the maximum efficiency of the energy storage system. Deploy the scheduling strategy in a real - time system and perform energy storage management operations based on the load demand and power generation prediction data for each time point. Use scheduling control algorithms (such as priority scheduling, time - sharing scheduling, etc.) to achieve the automatic regulation of the energy storage system and ensure the supply - demand balance.

[0080] Step S4: Perform energy storage performance testing on the distributed synchronous energy storage network through the energy storage configuration management scheduling strategy to generate energy storage performance test data; perform adaptive energy storage strategy adjustment according to the energy storage performance test data to execute the optimized operation of the energy storage configuration for photovoltaic power generation.

[0081] In the embodiments of the present invention, by evaluating the charging and discharging efficiency of the distributed synchronous energy storage network under the implementation of the energy storage management and scheduling strategy, it is ensured that the energy storage device can effectively cope with the fluctuations of photovoltaic power generation during actual operation and meet the load demand. Test the response time, charge and discharge rate, charge / discharge depth of the energy storage device, loss rate, system stability, and sustainable operation ability of the energy storage system. Based on the energy storage configuration management and scheduling strategy, a series of simulation tests are carried out to simulate the behavior of the energy storage device under different time periods and different photovoltaic power generation conditions. For the charging stage, test the charging speed and charging completion degree of the energy storage system. For the discharging stage, test the response ability of the energy storage system during the peak load demand. During actual operation, the real-time charge and discharge data of the energy storage device are collected through the installed smart meters and monitoring systems. During performance testing, the system continuously records key parameters such as the charge and discharge power of the energy storage device, the state of the energy storage battery (SOC, DoD), and the efficiency of the system. The energy storage performance test data includes but is not limited to: the charge and discharge power data of the energy storage device at each time point during the test period, the charge and discharge efficiency during the energy storage process, the discharge depth of the energy storage battery, reflecting the working state of the energy storage system, and the time delay for the energy storage system to start charging and discharging. Visualize the data obtained during the test process to generate various charts, such as real-time comparison charts of load demand and photovoltaic power generation, charge and discharge power change curves, efficiency and depth change charts, etc. Multiple chart methods such as line charts, bar charts, and heat maps can be used to facilitate the analysis of the performance of the energy storage system. According to the energy storage performance test results, the energy storage configuration management and scheduling strategy are optimized and adjusted in real time to improve the responsiveness, charge and discharge efficiency, and load matching effect of the energy storage system. When the charge and discharge efficiency of the energy storage device is low, optimize the charging and discharging rates to reduce losses. When the load demand fluctuates greatly, adjust the discharge depth of the energy storage device to ensure stable supply. When the performance of the energy storage system deteriorates (such as the efficiency is lower than the threshold, the response time is too long, etc.), adjust the energy storage configuration parameters to restore the system efficiency. Use machine learning (such as reinforcement learning, genetic algorithms, etc.) to achieve adaptive adjustment of the strategy. These algorithms can continuously adjust the operation mode of the energy storage device according to the real-time energy storage performance data. Through training on the historical behavior of the energy storage system, the reinforcement learning algorithm can automatically select the optimal charge and discharge strategy. By simulating the process of genetic selection, optimize the energy storage system configuration and generate a more efficient charge and discharge scheduling plan. During the operation of the energy storage system, monitor the real-time operation state of each energy storage device, including charge and discharge efficiency, energy storage depth, charging time, etc. According to the preset adjustment rules, if the efficiency of the energy storage device is lower than the set threshold or the response time exceeds the standard, automatically start the adaptive algorithm to adjust the energy storage strategy. If the discharge efficiency of the energy storage device is lower than 80%, increase the duration of the charging stage or reduce the discharge rate to reduce losses. After the adaptive energy storage strategy adjustment, the system re-executes the photovoltaic power generation energy storage configuration optimization task. The specific execution process includes: recalculating the charging and discharging strategies of the energy storage system according to the new energy storage configuration adjustment.Execute the optimized strategy to ensure that the energy storage system can be effectively regulated under different photovoltaic power generation levels and electricity load demands, and maintain the stable operation of the system.

[0082] Preferably, step S1 includes the following steps:

[0083] Step S11: Obtain the information data of the photovoltaic power generation energy site;

[0084] Step S12: Conduct a site distribution topology analysis on the information data of the photovoltaic power generation energy site to generate energy site distribution topology data; based on the energy site distribution topology data, construct a distributed synchronous energy storage network for the information data of the photovoltaic power generation energy site to obtain a distributed synchronous energy storage network;

[0085] Step S13: Collect real-time power generation data of the distributed synchronous energy storage network to obtain real-time photovoltaic power generation data; perform data preprocessing on the real-time photovoltaic power generation data to generate standard real-time photovoltaic power generation data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization;

[0086] Step S14: Perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data.

[0087] In the embodiments of the present invention, relevant data is collected from photovoltaic power generation sites through sensor networks, satellite remote sensing technology or ground collection devices, including the geographical location of the sites, the layout of the panels, the power generation capacity, historical power generation data, meteorological data, etc. The intelligent control system of the photovoltaic device can be docked through the API interface to obtain the operation status data of the device in real time. The geographical location of the photovoltaic sites is topologically analyzed using graph theory or spatial analysis methods to construct the grid connection relationship between the sites. This can be visually analyzed through a Geographic Information System (GIS) for spatial distribution to understand the distances, power generation potential, and power transmission paths between the sites. Based on the results of the topological analysis, a distributed synchronous energy storage network is designed. This network works synchronously with the photovoltaic power generation devices through energy storage devices (such as battery packs) to cope with the instability and volatility of photovoltaic power generation. Intelligent scheduling algorithms (such as load forecasting, energy storage capacity optimization, etc.) can be used to ensure the efficient utilization of the energy storage devices. The output data of photovoltaic power generation is collected in real time through sensors (such as power sensors, light sensors, temperature sensors, etc.) installed at each photovoltaic power generation site. The remote transmission and monitoring of data can be realized through the Internet of Things (IoT) technology. Duplicate data, outliers, and noise are removed. The collected real-time data is denoised using a filter (such as a Kalman filter) to ensure the data quality. Interpolation methods (such as linear interpolation or machine learning-based interpolation methods) are used to fill in the missing values in the data to ensure the data integrity. The data from different sources is standardized (such as min-max standardization or Z-Score standardization) so that the data is on the same scale for subsequent analysis and modeling. Machine learning-based prediction algorithms (such as LSTM networks, SVR regression, XGBoost, etc.) are used to model the preprocessed standard real-time photovoltaic power generation data. These models predict the future photovoltaic power generation output based on historical data and real-time collected data. The prediction model is trained using a training dataset and optimized using methods such as cross-validation to improve the prediction accuracy and generalization ability. Under real-time data input, the trained prediction model is used to predict the photovoltaic power generation in real time. The prediction results can be used as the input for energy storage management, scheduling, and load optimization, providing decision support for subsequent power grid scheduling and power management.

[0088] Preferably, constructing a distributed synchronous energy storage network for the information data of photovoltaic power generation energy sites based on the topological data of energy site distribution includes:

[0089] Performing topological modeling on the topological data of energy site distribution using graph theory methods to generate energy site topological model data; evaluating the importance of nodes for the energy site topological model data to generate topological node importance data; and labeling the nodes of the energy site topological model data according to the topological node importance data to generate key nodes and secondary nodes.

[0090] Edge connection is performed on the energy site topology model data according to the joint points and secondary nodes to generate an initial energy storage network; distributed node deployment is carried out on the initial energy storage network to generate a distributed energy storage network; three-layer data synchronization is performed on the distributed energy storage network by using the information data of the photovoltaic power generation energy site, so as to generate a distributed synchronous energy storage network, where the three-layer data synchronization includes time synchronization, power synchronization, and status synchronization.

[0091] In the embodiments of the present invention, by collecting information such as geographical location, power output, grid connection point, historical operation data, etc. from each photovoltaic power generation site, the geographical distribution of energy sites is constructed. Based on a graph theory model (such as an undirected graph or a weighted graph), each energy site is regarded as a node, and the power transmission lines (or grid connection lines) between sites are regarded as edges. By analyzing the geographical location and power transmission capacity of these sites, an energy site topology model is constructed. Each node of the graph represents a photovoltaic power generation site, and the weight of the edge can be set according to information such as the bandwidth and transmission capacity of the grid connection. Use importance measurement methods in graph theory (such as the degree of a node, betweenness centrality, closeness centrality, etc.) to evaluate the importance of each node. The specific methods may include: Degree centrality: The larger the degree of a node, the more connections it has with other nodes, and usually this node is considered more important. Betweenness centrality: Calculate the number of times a node acts as a mediator in the network. Nodes with high betweenness centrality play an important bridging role in the network. Closeness centrality: The average distance between a node and other nodes. Nodes with high closeness centrality usually have a greater impact on the entire network. According to the above evaluation methods, calculate the importance of each node and generate data, marking high-importance nodes (key nodes) and low-importance nodes (secondary nodes). According to the importance data of the nodes, label each node as a key node or a secondary node. Key nodes are those sites with large power output, connecting multiple sites or being in the core position of the grid, while secondary nodes are edge nodes or sites with small power demand, generating a set of topological node data to clarify which nodes are key nodes and which are secondary nodes for subsequent processing. Based on the connection relationship between key nodes and secondary nodes, use methods such as the shortest path algorithm and minimum spanning tree to construct an initial energy storage network. More edges need to be connected between key nodes to ensure the efficient transmission of power load and the full utilization of energy storage. Pair each node with its neighboring nodes through grid connections and set corresponding energy storage devices (such as battery energy storage systems) to ensure that each node in the network has sufficient energy storage resources and can perform charge and discharge operations when needed. According to the requirements of the energy storage network, deploy energy storage devices between the key nodes and secondary nodes of the network. The capacity of the energy storage device should be related to the power generation potential, power demand and network stability of the node to ensure that the energy site can maintain a stable power supply under different loads. In the distributed energy storage network, perform dynamic load distribution on the energy storage nodes according to real-time power demand and power generation data. Optimization algorithms (such as particle swarm optimization, genetic algorithm, etc.) can be used to determine the optimal deployment location and capacity of the energy storage device. To ensure the coordinated operation between all energy sites and energy storage devices, first, the time of all nodes needs to be synchronized. NTP (Network Time Protocol) or GPS synchronization technology can be used to ensure the same time accuracy of all devices. Based on the real-time collected photovoltaic power generation data, synchronize the power generation power of each node in the energy storage network.The power generation and storage power of all energy storage nodes can be ensured to match through distributed control algorithms (such as consensus algorithms), avoiding situations of over-storage or under-storage. The states of each node are synchronized through real-time monitoring and feedback mechanisms, including the state of charge (SOC) of the battery, the remaining energy storage capacity, the grid load, etc. Distributed synchronization algorithms (such as network-based synchronous control or consensus protocols) are adopted to ensure the coordinated state of the energy storage system, reducing scheduling conflicts and efficiency degradation caused by out-of-sync states. After three-layer data synchronization, a highly coordinated distributed synchronous energy storage network is formed. This network can dynamically adjust the energy storage capacity according to the photovoltaic power generation and load demand, balance the supply and demand relationship of the grid, and ensure that the energy storage system can respond efficiently when the photovoltaic power generation fluctuates or the grid load changes.

[0092] Preferably, step S14 includes the following steps:

[0093] Step S141: Extract the basic photovoltaic power generation characteristics from the standard real-time photovoltaic power generation data to obtain basic photovoltaic power generation characteristic data; perform time series characteristic analysis on the basic photovoltaic power generation characteristic data to generate photovoltaic power generation time series characteristic data;

[0094] Step S142: Divide the photovoltaic power generation time series characteristic data into a dataset to generate a model training set and a model test set; train the model training set through the long short-term memory neural network algorithm to generate a preliminary photovoltaic power generation prediction model; optimize and iterate the preliminary photovoltaic power generation prediction model through the model test set to generate a photovoltaic power generation prediction model;

[0095] Step S143: Import the standard real-time photovoltaic power generation data into the photovoltaic power generation prediction model for photovoltaic power generation prediction to generate real-time photovoltaic power generation prediction data.

[0096] In the embodiments of the present invention, by extracting basic features from standard real-time photovoltaic power generation data, which generally include but are not limited to: the actual power generation amount at each time step, environmental factors affecting the photovoltaic power generation efficiency such as light intensity, temperature, humidity, etc., for example, the working state of photovoltaic modules, the charging state of batteries, etc. Use data preprocessing methods (such as moving average method, standard deviation, maximum value, minimum value, etc.) to extract the key statistical features of photovoltaic power generation. Extract the features of seasonal variation and periodic fluctuation, and identify the periodic patterns affecting the power generation amount. Perform time series analysis on the extracted basic photovoltaic power generation feature data to analyze its time series characteristics, such as seasonal changes, trend changes, periodic fluctuations, etc. Through statistical methods such as the autocorrelation function (ACF) and partial autocorrelation function (PACF), identify the time series features in the data. Methods such as Fourier Transform or Wavelet Transform can be used to further analyze the frequency domain characteristics and change trends of photovoltaic power generation data. Based on the time series characteristic analysis, generate photovoltaic power generation time series characteristic data. Divide the photovoltaic power generation time series characteristic data into a training set and a test set in chronological order. The division method can be based on a time window (for example, the first 70% of the data is used as the training set, and the last 30% of the data is used as the test set), or the cross-validation method can be used for division. In particular, when dividing, it should be ensured that the time ranges of the training set and the test set do not overlap to avoid data leakage. The sliding window method can be used to dynamically generate the training set and the test set for different time periods, thereby improving the generalization ability of the model. Use the long short-term memory neural network (LSTM) to perform time series prediction on photovoltaic power generation data. LSTM is a neural network architecture particularly suitable for processing sequence data (especially time series data), and can effectively capture the long-term dependencies in the data. Input layer: Receive the feature data (such as historical power generation amount, meteorological data, etc.) extracted from the standard real-time photovoltaic power generation data. Hidden layer: Use LSTM units to capture the time series characteristics of the photovoltaic power generation amount. Multiple layers of LSTM can be set to enhance the expression ability of the model. Output layer: Output the predicted photovoltaic power generation amount, usually a continuous value (for example, the predicted power generation amount at the hour or minute level). Use the training set data to train the LSTM model through the backpropagation algorithm to optimize the weight parameters in the network. Use a loss function (such as mean square error MSE) to measure the prediction error of the model, and perform iterative updates through an optimization algorithm (such as the Adam optimizer). Test the LSTM model through the model test set to evaluate its prediction performance (such as through indicators such as root mean square error (RMSE), mean absolute error (MAE), etc.). After the model is trained, hyperparameter tuning (such as adjusting parameters such as the number of LSTM units, learning rate, time step, etc.) can be performed to optimize the model. According to the prediction results of the test set, iterate and optimize the model, continuously adjust the model parameters, and improve the prediction accuracy.Import the standard real-time photovoltaic power generation data into the already trained photovoltaic power generation prediction model (LSTM model) to predict the photovoltaic power generation in the future time period. Input the real-time data collected from the photovoltaic power generation site (such as real-time light intensity, meteorological data, historical power generation data, etc.) into the LSTM model. The LSTM model generates real-time photovoltaic power generation prediction data according to the input data, including the prediction of photovoltaic power generation in the next few hours or minutes.

[0097] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0098] Step S21: Collect historical electricity load data of the distributed synchronous energy storage network to obtain historical electricity load data;

[0099] Step S22: Extract spatio-temporal load characteristics from the historical electricity load data to obtain electricity spatio-temporal load characteristic data; Analyze the seasonal change law of the electricity spatio-temporal load characteristic data to generate a load characteristic data set; Convert the historical electricity load data according to the load characteristic data set to generate a historical electricity load curve;

[0100] Step S23: Analyze the curvature trend of the historical electricity load curve to generate load curve curvature trend data, where the load curve curvature trend data includes positive trend curvature, flat trend curvature, and negative trend curvature;

[0101] Step S24: Segment the historical electricity load curve based on the positive trend curvature, flat trend curvature, and negative trend curvature to generate a high-load trend curve, a flat-load trend curve, and a low-load trend curve; Classify the historical electricity load data according to the high-load trend curve, the flat-load trend curve, and the low-load trend curve to generate electricity load demand pattern data.

[0102] In the embodiments of the present invention, historical power consumption load data is collected from each power user in the distributed synchronous energy storage network. This data is usually power consumption data recorded based on time (e.g., hours, days, or months). Based on the historical power consumption load data, the following spatio-temporal features are extracted: the change trend of the load within a day, usually the diurnal change, manifested as the load difference between day and night. The weekly power consumption fluctuation, such as the power consumption difference between weekdays and weekends. Seasonal fluctuations, especially the impact of temperature changes on the power consumption load, for example, the increase in air-conditioning load in summer. Analyze the seasonal load changes in the historical data to generate the power consumption patterns for summer and winter, such as the increase in heating demand in winter and the increase in air-conditioning load in summer. Use statistical methods (such as periodic analysis, Fourier transform) to analyze the historical load data to reveal the change patterns of power demand with factors such as season, climate, and time. Seasonal model: Establish a seasonal fluctuation model, for example, use seasonal differencing to eliminate the influence of seasonal fluctuations, and generate a load characteristic data set to describe the load change characteristics in different seasons, weather, and time periods. Based on the load characteristic data set, smooth the original historical power consumption load data through interpolation methods or curve fitting to generate a more regular historical power consumption load curve. Conduct curvature trend analysis on the generated historical power consumption load curve, that is, identify its change trend by calculating the derivative or second derivative of the load curve: Positive trend curvature: The load curve gradually rises, indicating an increase in load demand. Gentle trend curvature: The change amplitude of the load curve is small, indicating a stable change in load demand. Negative trend curvature: The load curve gradually descends, indicating a decrease in load demand. Evaluate the trend of load change by calculating the first derivative (i.e., slope) and second derivative (i.e., curvature) of the load curve. Use numerical differencing or spline interpolation to calculate the curvature and mark the load change trend in each time period. The obtained load curve curvature trend data includes: Positive trend curvature: For example, when the load rises sharply, it is related to high-temperature weather or special events (such as holidays). Gentle trend curvature: For example, when the load changes stably, it represents the normal power consumption mode. Negative trend curvature: For example, when the load drops, it is related to factors such as equipment failures. According to the positive trend curvature, gentle trend curvature, and negative trend curvature, perform load curve segmentation on the historical power consumption load curve: When the rising speed of the load curve is fast, it belongs to the high load demand period, related to load peaks such as high temperature in summer or heating in winter. The load changes relatively smoothly, indicating the normal working-day power consumption load, usually occurring during the alternating periods of day and night. The load curve descends, indicating a decrease in power demand, such as at night, on holidays, or during the period of reduced seasonal air-conditioning load. According to the load curve segmentation results, divide the load demand into three main modes: High load demand mode: The time period represented by the high load trend curve, used for load scheduling optimization and energy storage scheduling. Gentle load demand mode: Suitable for conventional load management and demand response management. Low load demand mode: Suitable for load shaving and filling to reduce peak load.Integrate high-load, flat-load, and low-load trend data to generate electricity load demand pattern data, providing a basis for future load forecasting, energy scheduling, and energy storage management.

[0103] As an example of the present invention, refer to Figure 3 shown. In this example, step S3 includes:

[0104] Step S31: Calculate the energy supply-demand ratio for the electricity load demand pattern data based on the real-time photovoltaic power generation prediction data to obtain the energy supply-demand ratio data; the formula for calculating the energy supply-demand ratio is as follows:

[0105]

[0106] In the formula, represents the energy supply-demand ratio, represents the photovoltaic power generation at time , represents the power output of the energy storage device at time , represents the power obtained from the power grid at time , represents the electricity load demand power at time ;

[0107] Step S32: Compare the energy supply-demand ratio data with the preset standard supply-demand ratio threshold. When the energy supply-demand ratio data is greater than the preset standard supply-demand ratio threshold, a power supply surplus mode is generated; when the energy supply-demand ratio data is less than the preset standard supply-demand ratio threshold, a power supply gap mode is generated;

[0108] Step S33: Optimize the energy storage system configuration of the distributed synchronous energy storage network based on the power supply surplus mode and the power supply gap mode to generate the optimized data for photovoltaic power generation energy storage configuration;

[0109] Step S34: Perform dynamic energy regulation on the distributed synchronous energy storage network according to the optimized data for photovoltaic power generation energy storage configuration, thereby generating an energy storage configuration management and scheduling strategy.

[0110] In the embodiment of the present invention, by calculating the energy supply-demand ratio based on the real-time photovoltaic power generation prediction data, the power output of the energy storage device, the power obtained from the power grid, and the electricity load demand, the energy supply-demand balance state is evaluated. The formula for calculating the energy supply-demand ratio is as follows: In the formula, represents the energy supply-demand ratio, represents the photovoltaic power generation at time , represents the power output of the energy storage device at time ; Expressed as a moment The electric power obtained from the power grid, Expressed as a moment The power demand of the electrical load at that time. Compare the calculated energy supply - demand ratio R(t) with the preset standard supply - demand ratio threshold to identify the current power supply mode (surplus mode or deficit mode). Through historical data analysis and experience, set a reasonable supply - demand ratio threshold, which can be adjusted according to different electricity - using scenarios and seasons. For example, during the peak electricity - using period in summer, the threshold is set to 1.2, while during the low - peak period in winter, the threshold is set to 1.5. When the energy supply - demand ratio data is greater than the preset standard supply - demand ratio threshold, a power supply surplus mode is generated; when the energy supply - demand ratio data is less than the preset standard supply - demand ratio threshold, a power supply deficit mode is generated; in the power supply surplus mode, the goal is to store the excess energy for future use in the power supply deficit mode. The system should store the excess power from photovoltaic power generation, energy storage devices, and the power grid into the energy storage devices. Optimize the configuration of the energy storage devices according to the capacity and charge - discharge efficiency of the existing energy storage devices to ensure that the excess power can be efficiently absorbed and stored. In the power supply deficit mode, the task of the energy storage system is to supply the necessary power to the power grid to ensure that the load demand is met. At this time, the energy storage devices should have a high discharge capacity and provide supplementary power when the power grid power is insufficient. Adjust the configuration of the energy storage system according to the discharge capacity and stored power of the energy storage devices so that it can provide the required power when needed. Use heuristic methods such as genetic algorithms and particle swarm optimization algorithms to globally optimize the configuration of the energy storage system to ensure the best energy allocation effect in different power supply modes. During the optimization process, consider various constraints such as the capacity, charge - discharge efficiency, and system safety of the energy storage devices to ensure that the optimization scheme meets the actual application requirements. Dynamically adjust the charge - discharge strategy of the energy storage devices according to the changes in real - time photovoltaic power generation, the state of energy storage devices, grid power supply, and load demand. For example, if the system enters the power supply deficit mode, give priority to discharging from the energy storage devices; if it enters the power supply surplus mode, give priority to storing electrical energy. Use optimization algorithms (such as linear programming, dynamic programming, etc.) to optimize the scheduling of the energy storage system to ensure that in different supply - demand states, the energy storage devices can maximize the power use efficiency and reduce energy waste. Use a real - time monitoring system to monitor the state of the energy storage devices, and the dispatching system can adjust the energy distribution strategy in real - time to avoid over - charging or over - discharging of the energy storage devices and ensure the stability of power supply. Through intelligent dispatching algorithms, reasonably allocate energy storage resources during the peak load of the system to ensure uninterrupted power supply.

[0111] Preferably, step S33 includes the following steps:

[0112] Step S331: Confirm the mode time periods of the distributed synchronous energy storage network based on the power supply surplus mode and the power supply gap mode to obtain the power supply surplus time periods and the power supply gap time periods; conduct buffer period analysis on the power supply surplus time periods and the power supply gap time periods to generate power supply change buffer periods;

[0113] Step S332: Set energy discharge for the power supply surplus time periods to generate an energy discharge curve; set energy charging for the power supply gap time periods to generate an energy charging curve; perform curve fitting on the energy discharge curve and the energy charging curve according to the power supply change buffer periods to generate buffer period energy loss data;

[0114] Step S333: Optimize the buffer period energy storage battery configuration of the distributed synchronous energy storage network based on the buffer period energy loss data, thereby generating optimized data for photovoltaic power generation energy storage configuration.

[0115] In the embodiments of the present invention, by comparing the previously calculated energy supply - demand ratio data with the set standard supply - demand ratio threshold, power supply surplus time periods and power supply gap time periods are generated, and these time periods will be used for further energy storage configuration and scheduling. To avoid unnecessary losses caused by frequent charging and discharging of the energy storage system, it is necessary to analyze the buffer period between the power supply surplus and the power supply gap. The buffer period refers to the transition stage between the power supply surplus and the power supply gap, where there is a lag in energy conversion (for example, the gradual weakening of photovoltaic power generation after being blocked by clouds, or the lag in power grid scheduling, etc.). Through time - series analysis, the transition period between the power supply surplus and the power supply gap patterns is determined, and a reasonable buffer period is set. The buffer period will be used as the key time window for the energy storage system scheduling to ensure appropriate energy charging and discharging adjustments during this period. The length of the buffer period should be dynamically adjusted according to factors such as the volatility of historical data and the load change rate. During the power supply surplus time period, the energy storage system should discharge the excess electric energy to avoid energy waste. During the power supply surplus time period, the energy storage system starts the discharging process according to the excess photovoltaic power generation and the excess part of the grid power. The energy discharge curve reflects the discharging process of the energy storage system during the power supply surplus time period, and linear, non - linear or piece - wise linear models can be used to fit the change of the discharged amount over time. If the energy storage device fails to fully release the stored electric energy during the discharging process, the discharge curve shows a gentle decline. The energy storage discharge curve should be dynamically adjusted according to different energy storage technologies (such as batteries, supercapacitors, etc.) to avoid over - discharging of the device. During the power supply gap time period, the energy storage system should charge to replenish the energy in the battery to ensure that future electricity demands are met. During the power supply gap time period, the energy storage system should preferentially use grid power for charging according to the grid power and the remaining power of the energy storage device. The charging curve reflects the charging process of the energy storage system during the power supply gap time period. The charging curve usually shows an upward trend and is adjusted according to the battery charging efficiency and the grid power supply capacity. During the charging process, the charging curve should be adjusted according to factors such as the battery charging capacity limit, charging efficiency, and the upper limit of the battery charge. Based on the buffer period between the power supply surplus and the power supply gap patterns, curve fitting is performed on the energy discharge curve and the charging curve, considering factors such as energy loss, device response time, and efficiency loss. The energy loss amount during the buffer period is calculated through the fitted curve. These losses are usually related to factors such as the efficiency loss and response delay of the device during the charging and discharging processes. Input the energy loss data of the buffer period, combined with the technical characteristics of the energy storage system (such as battery capacity, charge - discharge efficiency, response time, etc.). An optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, or linear programming, etc.) is used to globally optimize the configuration of the energy storage system, with the goal of minimizing the energy loss during the buffer period. According to the results of the optimization algorithm, optimized data for the photovoltaic power generation energy storage configuration is generated to guide the energy storage and discharging strategies of the energy storage system in different time periods.

[0116] Preferably, step S34 includes the following steps:

[0117] Step S341: Calculate the node energy redundancy of the distributed synchronous energy storage network according to the optimized data of photovoltaic power generation energy storage configuration to obtain node energy redundancy data; analyze the adjacent node states of the distributed synchronous energy storage network through the node energy redundancy data to generate adjacent node state data;

[0118] Step S342: Use the adjacent node state data to allocate energy scheduling weights to the node energy redundancy data to generate energy scheduling weight allocation data;

[0119] Step S343: Screen the emergency energy nodes from the adjacent node state data through the energy scheduling weight allocation data to obtain emergency energy node data; analyze the emergency scheduling energy storage path of the emergency energy node data according to the node energy redundancy data to generate emergency node scheduling energy storage path data;

[0120] Step S344: Perform scheduling energy storage uniform control on the distributed synchronous energy storage network based on the emergency node scheduling energy storage path data, thereby generating an energy storage configuration management scheduling strategy.

[0121] In the embodiments of the present invention, the energy redundancy level of each node is calculated based on the optimized data of photovoltaic power generation energy storage configuration. The energy redundancy of a node refers to the extra energy available for scheduling in the current energy storage device of the node, which is usually determined by the following factors: the current energy storage capacity of the node, the energy input and output status of the node (such as photovoltaic power generation, energy storage discharge, charging, etc.), the load demand of the node, and other network parameters. Through the redundancy calculation, the node energy redundancy data of each node is obtained, which represents the extra power that the node can provide or consume in a future period of time. Based on the node energy redundancy data, the status analysis of adjacent nodes in the distributed synchronous energy storage network is carried out. Specifically, the energy redundancy levels of adjacent nodes are analyzed to identify potential areas of load overload or energy shortage. The adjacent node status data includes the energy redundancy status, load demand, energy storage status, etc. of each node. These data help to determine which nodes need more energy support and which nodes have excess energy to provide, generating adjacent node status data for subsequent energy scheduling and emergency handling. Energy scheduling weight allocation: The node energy redundancy data of each node is weighted and allocated according to the adjacent node status data to generate energy scheduling weight allocation data. The specific method is as follows: For each node, the weight of energy scheduling is allocated according to the energy redundancy status and importance of its adjacent nodes. For example, if a node has a high energy redundancy and is close to a node with a large load demand, the scheduling weight of this node is high and it is preferentially scheduled. Considering the charge and discharge capacity, geographical location, and power flow of the node, the scheduling weight of each node is adjusted to make the energy scheduling decision more intelligent and flexible, generating energy scheduling weight allocation data, which will be used for subsequent emergency energy node screening and scheduling decision. Based on the energy scheduling weight allocation data, the adjacent node status data is screened to identify emergency energy nodes. Emergency energy nodes refer to those nodes: whose load demand has increased sharply and the currently schedulable energy is insufficient to meet the demand. Nodes with energy storage devices about to run out, or nodes expected to have a power shortage in a short time. The selected nodes will be marked as emergency nodes and need to be preferentially scheduled. According to the emergency energy node data and the node energy redundancy data, an emergency scheduling energy storage path analysis is carried out for the emergency energy nodes. The goal of this analysis is to determine the best path for scheduling power from other nodes with higher energy redundancy. The factors considered in the path analysis include: the charge and discharge efficiency of the energy storage device, the physical path of power flow, scheduling priority, and constraints, generating emergency node scheduling energy storage path data, which describes the power scheduling path from a node with higher energy redundancy to an emergency energy node. Based on the emergency node scheduling energy storage path data, an energy storage uniform control is carried out for the distributed synchronous energy storage network. The uniform control aims to ensure the balance and efficiency of energy scheduling and avoid over-discharging or over-charging of a single node, resulting in system imbalance. The energy storage uniform control strategy includes: dynamically adjusting the charge and discharge behavior of the energy storage device according to the node energy redundancy level and load demand.Prioritize the scheduling of emergency energy nodes, and at the same time consider the power demands of other nodes to avoid wasting resources due to over-scheduling of a certain node. Based on uniform energy storage control and path analysis, generate an energy storage configuration management scheduling strategy, which includes: the charging and discharging timing and power of each node; the selection of energy storage paths and scheduling priorities; the prioritized scheduling plan for emergency energy nodes; and the energy balance management of the overall energy storage system.

[0122] Preferably, step S4 includes the following steps:

[0123] Step S41: Execute the strategy on the distributed synchronous energy storage network through the energy storage configuration management scheduling strategy to generate energy storage strategy execution data for photovoltaic power generation; conduct energy storage performance tests on the energy storage strategy execution data for photovoltaic power generation to generate energy storage performance test data;

[0124] Step S42: Adjust the energy storage strategy adaptively for the energy storage strategy execution data for photovoltaic power generation according to the energy storage performance test data to perform the optimization operation of the energy storage configuration for photovoltaic power generation.

[0125] In the embodiments of the present invention, according to the energy storage configuration management and scheduling strategy generated in the previous steps, the distributed synchronous energy storage network is executed with the strategy. When executing, the charging and discharging behaviors of each node are scheduled according to the energy storage strategy. Specifically, it includes: adjusting the discharging and charging behaviors of the energy storage device according to the energy redundancy, load demand, and scheduling weight of each node. Precisely controlling the charging and discharging time of the energy storage device to ensure the optimal allocation of energy storage resources. By executing the energy storage strategy, data such as the charging and discharging conditions, remaining battery power, and energy flow of each node are recorded in real time to form the energy storage strategy execution data for photovoltaic power generation energy storage. Perform energy storage performance testing on the data after the execution of the energy storage strategy. The purpose of the performance testing is to evaluate the actual operation performance of the energy storage system and ensure the effectiveness and efficiency of the strategy execution. The testing content includes: the energy utilization efficiency of the energy storage system, the charging and discharging efficiency of the energy storage device, the response speed and accuracy of the energy storage strategy, the system stability, and the load adaptation ability. Through the performance testing, the test results of each node, each energy storage device, and the entire system are obtained to generate the energy storage performance testing data. According to the energy storage performance testing data, perform adaptive energy storage strategy adjustment on the energy storage strategy execution data for photovoltaic power generation energy storage. The core goal of the adaptive adjustment is to optimize the energy storage strategy according to the real-time test results to better meet future energy demands and system changes. If the load demand fluctuates greatly in certain periods of the system, the charging and discharging rate or time of the energy storage device can be adjusted to ensure the fast response ability of the energy storage device. If it is found through testing that the efficiency of the energy storage device is insufficient or the charging and discharging process is unbalanced, the usage priority of the energy storage device can be adjusted to optimize the charging and discharging behaviors of the battery and improve the energy utilization efficiency. According to the test results, dynamically adjust the scheduling weight of each node, and preferentially schedule the nodes with high energy redundancy or emergency state nodes to reduce the response delay of the energy storage system during sudden load changes. According to the above adjustments, execute the energy storage configuration optimization operation. This includes reallocating the energy storage, discharging, and charging tasks, optimizing the power flow direction, adjusting the working state of the energy storage device, generating the adaptive energy storage strategy adjustment data, which serves as the basis for the next scheduling cycle, to ensure that the energy storage system can continuously optimize and adjust under changing conditions.

[0126] In this specification, an energy storage configuration system for photovoltaic power generation is provided, which is used to execute the above-mentioned energy storage configuration method for photovoltaic power generation. The energy storage configuration system for photovoltaic power generation includes:

[0127] A power generation prediction module, which is used to obtain the information data of the photovoltaic power generation energy site; construct a distributed synchronous energy storage network for the information data of the photovoltaic power generation energy site to obtain a distributed synchronous energy storage network; collect real-time power generation data for the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data;

[0128] An electricity consumption trend analysis module, which is used to collect historical electricity load data of a distributed synchronous energy storage network to obtain historical electricity load data; perform load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data; classify the historical electricity load data based on the load curve curvature trend data to generate electricity load demand pattern data;

[0129] An energy storage configuration control module, which is used to calculate the energy supply - demand ratio of the electricity load demand pattern data according to the real - time photovoltaic power generation prediction data to obtain energy supply - demand ratio data; optimize the energy storage system configuration of the distributed synchronous energy storage network through the energy supply - demand ratio data to generate photovoltaic power generation energy storage configuration optimization data; perform dynamic energy regulation on the distributed synchronous energy storage network according to the photovoltaic power generation energy storage configuration optimization data, thereby generating an energy storage configuration management and scheduling strategy;

[0130] An energy storage configuration optimization module, which is used to perform energy storage performance testing on the distributed synchronous energy storage network through the energy storage configuration management and scheduling strategy to generate energy storage performance test data; adjust the adaptive energy storage strategy according to the energy storage performance test data to execute the energy storage configuration optimization operation of photovoltaic power generation.

[0131] The beneficial effects of the present invention are as follows: By obtaining the information data of photovoltaic power generation energy sites and constructing a distributed synchronous energy storage network, the power generation data of each site can be collected in real time. This provides high-precision basic data support for subsequent energy scheduling and energy storage management. By predicting the real-time photovoltaic power generation data, power generation plans can be pre-planned under unstable weather and electricity load conditions, reducing energy waste, optimizing energy storage and power scheduling, and ensuring stable power supply. By extracting the spatio-temporal characteristics of historical electricity load data and analyzing the curvature trend of the load curve, the fluctuation patterns of electricity load can be revealed, helping to predict future electricity demand, which is particularly important in the case of large seasonal fluctuations. By classifying the load curve trend data, different electricity demand patterns can be identified, helping to optimize the allocation and scheduling of power resources, ensuring uninterrupted energy supply under different demand patterns, and avoiding energy waste. By combining the real-time photovoltaic power generation prediction data with the electricity load demand pattern data, the energy supply-demand ratio is calculated, helping to timely identify the imbalance between supply and demand in the power system. This can effectively predict the situation of power surplus or shortage and take measures in advance. According to the energy supply-demand ratio data, the energy storage system is optimized in real time, and the charge-discharge strategy of the energy storage device is adjusted to ensure the balance between photovoltaic power generation and load demand. This not only improves the energy utilization efficiency but also avoids the overuse or inefficient operation of the energy storage device. Through the energy storage performance test, the system can monitor the efficiency and status of the energy storage device in real time to ensure that the energy storage system is always in the best operating state. Through data feedback, potential problems of the energy storage device can be quickly identified, reducing the probability of failures. According to the energy storage performance test data, the energy storage strategy is adaptively adjusted to ensure that the system can flexibly respond and optimize the power storage and scheduling strategies in the face of different electricity loads and power generation conditions. Therefore, the present invention improves the accuracy and flexibility of photovoltaic energy storage configuration through real-time photovoltaic power generation prediction, dynamic analysis of load demand, and optimization of adaptive energy storage strategies.

[0132] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0133] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for configuring energy storage for photovoltaic power generation, characterized in that: The following steps are involved: Step S1: Acquire photovoltaic power generation energy site information data; construct a distributed synchronous energy storage network for the photovoltaic power generation energy site information data to obtain a distributed synchronous energy storage network; collect real-time power generation data for the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; Step S2: Collect historical power load data of the distributed synchronous energy storage network to obtain historical power load data; perform load curve curvature trend analysis on the historical power load data to generate load curve curvature trend data; classify load demand of the historical power load data based on the load curve curvature trend data to generate power load demand pattern data; Step S3: Calculate the energy supply and demand ratio of the power load demand pattern data according to the real-time photovoltaic power generation forecast data to obtain energy supply and demand ratio data; optimize the energy storage system configuration of the distributed synchronous energy storage network through the energy supply and demand ratio data to generate photovoltaic power generation energy storage configuration optimization data; dynamically adjust the energy of the distributed synchronous energy storage network according to the photovoltaic power generation energy storage configuration optimization data to generate an energy storage configuration management scheduling strategy; Step S4: Performing energy storage performance test on the distributed synchronous energy storage network through energy storage configuration management scheduling strategy to generate energy storage performance test data; Adaptively adjust the energy storage strategy according to the energy storage performance test data to perform the energy storage configuration optimization operation of photovoltaic power generation; step S3 includes the following steps: Step S31: Calculate the energy supply and demand ratio of the power load demand pattern data according to the real-time photovoltaic power generation forecast data to obtain energy supply and demand ratio data; wherein the formula for calculating the energy supply and demand ratio is as follows: Where R(t) represents the energy supply-demand ratio, P pv (t) represents the photovoltaic power generation at time t, P storage (t) represents the power output of the energy storage device at time t, P grid (t) represents the power obtained from the grid at time t, P load (t) represents the power demand of the electrical load at time t; Step S32: comparing the energy supply-demand ratio data with a preset standard supply-demand ratio threshold, when the energy supply-demand ratio data is greater than the preset standard supply-demand ratio threshold, a power supply surplus mode is generated; when the energy supply-demand ratio data is less than the preset standard supply-demand ratio threshold, a power supply gap mode is generated; Step S33: Optimizing the energy storage system configuration of the distributed synchronous energy storage network based on the power supply surplus mode and the power supply gap mode, and generating photovoltaic power generation energy storage configuration optimization data; Step S34: Dynamically adjust the energy of the distributed synchronous energy storage network according to the photovoltaic power generation energy storage configuration optimization data, thereby generating an energy storage configuration management scheduling strategy.

2. The energy storage configuration method for photovoltaic power generation according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire photovoltaic power generation energy site information data; Step S12: performing site distribution topology analysis on the photovoltaic power generation energy site information data to generate energy site distribution topology data; constructing a distributed synchronous energy storage network for the photovoltaic power generation energy site information data based on the energy site distribution topology data to obtain a distributed synchronous energy storage network; Step S13: collecting real-time power generation data of the distributed synchronous energy storage network to obtain real-time photovoltaic power generation data; performing data preprocessing on the real-time photovoltaic power generation data to generate standard real-time photovoltaic power generation data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S14: Perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data.

3. The energy storage configuration method for photovoltaic power generation according to claim 2, characterized in that: The distributed synchronous energy storage network construction based on the photovoltaic power generation energy site information data based on the energy site distribution topology data includes: Based on the graph theory method, the energy site distribution topology data is topologically modeled to generate energy site topology model data; the energy site topology model data is node-importantly evaluated to generate topology node importance data; the energy site topology model data is node-labeled according to the topology node importance data to generate key nodes and secondary nodes; The energy site topology model data is edge-connected according to the joint points and secondary nodes to generate an initial energy storage network; the initial energy storage network is deployed with distributed nodes to generate a distributed energy storage network; the distributed energy storage network is synchronized with three layers of data using the photovoltaic power generation energy site information data to generate a distributed synchronous energy storage network, where the three layers of data synchronization include time synchronization, power synchronization, and state synchronization.

4. The energy storage configuration method for photovoltaic power generation according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: extracting basic photovoltaic power generation characteristics from standard real-time photovoltaic power generation data to obtain basic photovoltaic power generation characteristic data; performing time series characteristic analysis on the basic photovoltaic power generation characteristic data to generate photovoltaic power generation time series characteristic data; Step S142: dividing the photovoltaic power generation time series characteristic data into data sets to generate a model training set and a model test set; training the model training set using a long short-term memory neural network algorithm to generate a photovoltaic power generation prediction pre-model; optimizing and iterating the photovoltaic power generation prediction pre-model using the model test set to generate a photovoltaic power generation prediction model; Step S143: Importing the standard real-time photovoltaic power generation data into the photovoltaic power generation prediction model to perform photovoltaic power generation prediction and generate real-time photovoltaic power generation prediction data.

5. The energy storage configuration method for photovoltaic power generation according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: collecting historical power load data of the distributed synchronous energy storage network to obtain historical power load data; Step S22: extracting spatiotemporal load characteristics from historical power load data to obtain spatiotemporal power load characteristic data; analyzing seasonal variation patterns of the spatiotemporal power load characteristic data to generate a load characteristic data set; converting historical power load data into a historical power load curve according to the load characteristic data set to generate a historical power load curve; Step S23: performing load curve curvature trend analysis on the historical power load curve to generate load curve curvature trend data, wherein the load curve curvature trend data includes positive trend curvature, gentle trend curvature and negative trend curvature; Step S24: Segment the historical power load curve based on the positive trend curvature, the gentle trend curvature and the negative trend curvature to generate a high load trend curve, a gentle load trend curve and a low load trend curve; classify the load demand of the historical power load data according to the high load trend curve, the gentle load trend curve and the low load trend curve to generate power load demand pattern data.

6. The energy storage configuration method for photovoltaic power generation according to claim 1, characterized in that: Step S33 includes the following steps: Step S331: confirming the mode time period of the distributed synchronous energy storage network based on the power supply surplus mode and the power supply gap mode to obtain the power supply surplus time period and the power supply gap time period; performing a buffer time period analysis on the power supply surplus time period and the power supply gap time period to generate a power supply change buffer time period; Step S332: setting energy discharge for the power surplus time period to generate an energy discharge curve; setting energy charging for the power shortage time period to generate an energy charging curve; fitting the energy discharge curve and the energy charging curve according to the power supply change buffer period to generate energy loss data for the buffer period; Step S333: Optimize the configuration of energy storage batteries in the buffer period of the distributed synchronous energy storage network based on the energy loss data in the buffer period, thereby generating photovoltaic power generation energy storage configuration optimization data.

7. The energy storage configuration method for photovoltaic power generation according to claim 1, characterized in that: Step S34 includes the following steps: Step S341: performing node energy redundancy calculation on the distributed synchronous energy storage network according to the photovoltaic power generation energy storage configuration optimization data to obtain node energy redundancy data; performing adjacent node status analysis on the distributed synchronous energy storage network according to the node energy redundancy data to generate adjacent node status data; Step S342: using the adjacent node status data to perform energy scheduling weight allocation on the node energy redundancy data to generate energy scheduling weight allocation data; Step S343: perform emergency energy node screening on adjacent node status data through energy scheduling weight distribution data to obtain emergency energy node data; perform emergency scheduling energy storage path analysis on the emergency energy node data according to node energy redundancy data to generate emergency node scheduling energy storage path data; Step S344: Dispatching and uniformly controlling the energy storage of the distributed synchronous energy storage network based on the emergency node scheduling energy storage path data, thereby generating an energy storage configuration management scheduling strategy.

8. The energy storage configuration method for photovoltaic power generation according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: executing the distributed synchronous energy storage network strategy through the energy storage configuration management scheduling strategy to generate photovoltaic power generation energy storage strategy execution data; performing energy storage performance test on the photovoltaic power generation energy storage strategy execution data to generate energy storage performance test data; Step S42: Adaptively adjust the photovoltaic power generation energy storage strategy execution data according to the energy storage performance test data to perform the photovoltaic power generation energy storage configuration optimization operation.

9. A photovoltaic power generation energy storage configuration system, characterized in that: The method for configuring energy storage for photovoltaic power generation according to claim 1 is used to implement the energy storage configuration method for photovoltaic power generation, and the energy storage configuration system for photovoltaic power generation comprises: The power generation prediction module is used to obtain photovoltaic power generation energy site information data; construct a distributed synchronous energy storage network based on the photovoltaic power generation energy site information data to obtain a distributed synchronous energy storage network; collect real-time power generation data from the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; perform photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; The power consumption trend analysis module is used to collect historical power consumption load data of the distributed synchronous energy storage network to obtain historical power consumption load data; perform load curve curvature trend analysis on the historical power consumption load data to generate load curve curvature trend data; perform load demand classification on the historical power consumption load data based on the load curve curvature trend data to generate power consumption load demand pattern data; The energy storage configuration control module is used to calculate the energy supply and demand ratio of the power load demand pattern data according to the real-time photovoltaic power generation forecast data to obtain the energy supply and demand ratio data; optimize the energy storage system configuration of the distributed synchronous energy storage network through the energy supply and demand ratio data to generate photovoltaic power generation energy storage configuration optimization data; dynamically adjust the energy of the distributed synchronous energy storage network according to the photovoltaic power generation energy storage configuration optimization data, thereby generating an energy storage configuration management scheduling strategy; The energy storage configuration optimization module is used to test the energy storage performance of the distributed synchronous energy storage network through the energy storage configuration management scheduling strategy and generate energy storage performance test data; and to adjust the energy storage strategy adaptively according to the energy storage performance test data to perform energy storage configuration optimization operations for photovoltaic power generation.

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