A method for coordinated scheduling of photovoltaic storage and charging

Abstract

The present invention relates to the technical field of electric energy storage and power regulation. The present invention provides a method for coordinated scheduling of photovoltaic storage and charging. The present invention aims to solve the problem that the data of photovoltaic storage equipment is inaccurate, which leads to chaotic power scheduling and inability to accurately output the power required by the power user. The present invention obtains the operating data of photovoltaic storage equipment, establishes photovoltaic storage equipment partitions and monitors them in real time, performs time segmentation and anomaly detection on the monitoring data, uses a preset data error analysis model to repair erroneous data, improves data accuracy, combines real-time and historical power data on the power user side, analyzes power demand, generates power adjustment task commands, and realizes the adjustment and transmission of power in photovoltaic storage equipment. The present invention improves energy utilization efficiency, enhances system stability and reliability, meets diverse power needs, and improves the intelligent operation level of photovoltaic storage equipment power.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage and power regulation, and in particular to a photovoltaic storage and charging coordinated scheduling method. Background Art

[0002] The core of photovoltaic storage and charging synergy lies in the coordinated operation between photovoltaic power generation, energy storage system and charging facilities through intelligent means. Photovoltaic storage and charging synergy technology is like a smart energy steward. Photovoltaic storage and charging synergy technology can accurately allocate the electricity in photovoltaic storage to electricity users based on real-time energy supply and demand, electricity price fluctuations and equipment status.

[0003] Photovoltaic-storage-charging coordinated scheduling is an intelligent scheduling technology that integrates energy systems such as photovoltaics, energy storage and charging. Photovoltaic-storage-charging coordinated scheduling can monitor various parameters of the energy system in real time, such as photovoltaic power generation power, the power status of the energy storage system, and the load information of the charging facilities. Photovoltaic-storage-charging coordinated scheduling uses intelligent algorithms and optimization models to predict energy supply and demand trends and formulate optimal operating strategies.

[0004] Photovoltaic storage and charging coordinated dispatching technology can optimize energy collection, storage and dispatching according to energy demand and supply. When the sun is sufficient, photovoltaic power is used for charging first, and the power is stored to improve energy efficiency. Through real-time monitoring and intelligent regulation, photovoltaic storage and charging coordinated dispatching can maximize the use of photovoltaic power generation and avoid energy waste. Photovoltaic storage and charging coordinated dispatching technology can effectively alleviate the pressure of the power grid during peak hours, reduce power grid fluctuations, and ensure the stable operation of the power grid through flexible energy storage and charging control.

[0005] At present, the operating parameter configurations of photovoltaic storage equipment in different regions are different. The operating data of photovoltaic storage equipment changes in real time, and the power demand of electricity users also changes. It is necessary to adjust the electric energy in the photovoltaic storage equipment to the power matching the needs of each electricity user through the coordinated scheduling of photovoltaic storage and charging. When the data of photovoltaic storage equipment is inaccurate, it will lead to confusion in the scheduling of photovoltaic storage energy and the adjustment of power, making it impossible for electricity users to obtain the required power and energy in a continuous and stable manner. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a method for coordinated scheduling of photovoltaic storage and charging to solve the problem that when the data of photovoltaic storage equipment is inaccurate, it will cause confusion in the scheduling of photovoltaic storage energy and the adjustment of electric power, resulting in the inability of electricity users to obtain the required power and energy in a continuous and stable manner.

[0007] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0008] The present invention provides a photovoltaic storage and charging coordinated scheduling method, comprising:

[0009] Step S101, obtaining operation data of the optical storage device, determining the location of the optical storage device according to the operation data of the optical storage device, establishing an optical storage device partition according to the location of the optical storage device, monitoring the optical storage devices in the optical storage device partition, and obtaining real-time monitoring data of the optical storage device partition;

[0010] Step S102, time segmenting the real-time monitoring of the optical storage device partition, the time segmentation includes a first time segment, a second time segment, a third time segment and a fourth time segment, extracting the time information of the real-time monitoring data of the optical storage device partition to obtain the time information of the real-time monitoring data of the optical storage device partition, matching the time information of the real-time monitoring data of the optical storage device partition with the time segmentation to obtain the time segmentation data matching result, performing data comparison on the data of each time segment in the time segmentation data matching result to obtain the time period data difference comparison result, the time period data difference comparison result includes data comparison without abnormality and data comparison abnormality;

[0011] Step S103, establishing data processing partitions, the data processing partitions include a first data processing partition, a second data processing partition, a third data processing partition and a fourth data processing partition, establishing a corresponding relationship between the data processing partitions and the time segments, and when the time segment data difference comparison result is a data comparison abnormality, the time period data corresponding to the data comparison abnormality is transmitted to the corresponding data processing partition;

[0012] Step S104, in the data processing partition, substitute the time period data corresponding to the data comparison anomaly into the preset data error analysis model to obtain the data error type, match the data error type in the knowledge base, and obtain the data repair solution strategy;

[0013] Step S105, establishing time segment simulation data according to the data repair solution strategy, replacing the erroneous data in the original time segment with the time segment simulation data to obtain updated time segment data, integrating the updated time segment data with the real-time monitoring data of the photovoltaic storage device partition to obtain the real-time available electric energy of the photovoltaic energy storage;

[0014] Step S106, receiving electricity demand information on the electricity consumer side, determining the identity of the user on the electricity consumer side and the location of the electricity consumer side according to the electricity demand information, extracting the electricity power information on the electricity consumer side from the electricity demand information on the electricity consumer side, obtaining the real-time electricity power demand on the user side, retrieving the historical electricity power of the identity of the user on the electricity consumer side and the location of the electricity consumer side from the database, comparing the historical electricity power of the identity of the user on the electricity consumer side and the location of the electricity consumer side with the real-time electricity power demand on the user side, obtaining the user side electricity power comparison result, the user side electricity power comparison result includes stable electricity power demand and abnormal electricity power demand, marking the electricity demand information on the electricity consumer side with stable electricity power demand as executable electricity power demand information, summarizing the executable electricity power demand information on the electricity consumer side to obtain an executable electricity power demand information data set, and generating an electricity power adjustment task command according to the executable electricity power demand information set;

[0015] Step S107, according to the power adjustment task command, the power of the electric energy in the photovoltaic storage device is adjusted and transmitted to the corresponding power-consuming device on the power-consuming side.

[0016] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S101:

[0017] The operating data of photovoltaic storage equipment includes photovoltaic power generation, power status of energy storage system, load information of charging facilities, equipment status information, energy conversion efficiency, geographic location information, timestamp, error information and alarm information.

[0018] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S101:

[0019] Photovoltaic storage equipment includes solar panels, inverters, support systems, energy storage systems, and monitoring systems;

[0020] Determine, in the optical storage device partition, an optical storage device used by the optical storage device partition, and generate an optical storage device partition device information set;

[0021] Communication information is sent to the corresponding optical storage device in the optical storage device partition device information set according to a preset time period. If feedback information is obtained from the corresponding optical storage device, the communication status of the optical storage device is normal.

[0022] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S102:

[0023] The first time segment, the second time segment, the third time segment and the fourth time segment are divided into each time segment according to a 6-hour interval;

[0024] Collect monitoring data of the optical storage device partition in real time, where the monitoring data of the optical storage device partition includes a timestamp and a time point for identifying data collection;

[0025] Parse the timestamp in the monitoring data of each optical storage device partition, and extract the time information in the monitoring data of each optical storage device partition, the time information includes year, month, day, hour, minute, and second;

[0026] The parsed time information is matched with the defined time segment to obtain the time segment corresponding to the monitoring data of each optical storage device partition.

[0027] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S102:

[0028] In each time segment, the monitoring data of each optical storage device partition is compared;

[0029] No abnormality in data comparison means that there is no abnormal data in the monitoring data within the time segment;

[0030] Data comparison anomaly refers to the existence of data comparison anomaly between monitoring data within a time segment. Data comparison anomaly includes data mutation, data duplication, and data incompleteness.

[0031] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S103:

[0032] Each data processing partition is used to process data and compare abnormal data within the corresponding time segment;

[0033] The data comparison abnormal data of the first time segment is processed by the first data processing partition, the data comparison abnormal data of the second time segment is processed by the second data processing partition, the data comparison abnormal data of the third time segment is processed by the third data processing partition, and the data comparison abnormal data of the fourth time segment is processed by the fourth data processing partition.

[0034] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S103:

[0035] According to the correspondence between time segments and data processing partitions, the data of the time period with data comparison anomalies is transferred to the corresponding data processing partition;

[0036] In the corresponding data processing partition, the preset data error analysis model is used to analyze the data with data comparison abnormalities, and the corresponding data repair solution strategy is matched in the knowledge base.

[0037] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S104:

[0038] Preprocess the data of the time period corresponding to the data comparison anomaly to obtain the preprocessed data of the time period corresponding to the data comparison anomaly. According to the user needs of the photovoltaic storage and charging coordinated scheduling, match the preset data error analysis model in the knowledge base. Before substituting the data into the preset data error analysis model, configure the parameters of the preset data error analysis model. The parameters of the preset data error analysis model include error type and error threshold.

[0039] Input the preprocessed data into the configured data error analysis model, preset the parameters of the data error analysis model to analyze the data in the time period corresponding to the preprocessed data comparison anomaly, identify the error type in the data in the time period corresponding to the preprocessed data comparison anomaly, and output the data error recognition result, which includes data mutation, data missing and error position;

[0040] The recognition results of data errors are matched in the knowledge base to obtain the data repair strategy corresponding to the recognition results of data errors. The data repair solution strategy includes error correction methods, data cleaning rules, data reconstruction strategies and manual intervention suggestions.

[0041] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S106:

[0042] The historical power consumption data of each user on the power consumption side is retrieved from the database. The historical power consumption data of each user on the power consumption side includes a timestamp and power consumption information. The real-time power consumption data uploaded from the power consumption side equipment is collected in real time.

[0043] The historical power consumption data and the real-time power consumption data of each power consumption side user are time segmented according to the time information to obtain the time segmented data of the historical power consumption data and the real-time power consumption data, the time segmented data of the historical power consumption data and the real-time power consumption data include the time segmented data of the historical power consumption data and the real-time power consumption data in the first time period, the time segmented data of the historical power consumption data and the real-time power consumption data in the second time period, the time segmented data of the historical power consumption data and the real-time power consumption data in the third time period, and the time segmented data of the historical power consumption data and the real-time power consumption data in the fourth time period;

[0044] The time segment data of the historical power consumption data and the real-time power consumption data in the first time period are from 0:00 to 6:00 every day, the time segment data of the historical power consumption data and the real-time power consumption data in the second time period are from 6:00 to 12:00 every day, the time segment data of the historical power consumption data and the real-time power consumption data in the third time period are from 12:00 to 18:00 every day, and the time segment data of the historical power consumption data and the real-time power consumption data in the fourth time period are from 18:00 to 24:00 every day;

[0045] Calculate the average or median of the historical power consumption of each electricity user in each time period as the benchmark power of the time period, compare the real-time power consumption with the benchmark power of the corresponding time period, set the real-time power comparison error threshold of ±10%, determine whether the real-time power comparison error threshold is within the error threshold, and obtain the comparison result of the real-time power consumption with the benchmark power of the corresponding time period;

[0046] The genetic algorithm is used to optimize the comparison between the real-time power consumption and the benchmark power in the corresponding time period, and a group of individuals with different thresholds and time segment parameters are generated as the initial population;

[0047] Each individual is a set of parameters. Each set of parameters is used to compare the real-time power with the historical power, and the accuracy and recall rate are calculated as fitness indicators;

[0048] According to the fitness index, individuals with high fitness are selected to enter the next generation, and crossover operations are performed on the selected individuals to exchange some parameters and generate new individuals;

[0049] Perform mutation operations on some individuals, randomly change the values ​​of parameters, and repeat fitness evaluation, selection, and crossover mutation operations until the predetermined number of iterations or fitness is reached;

[0050] The parameters optimized by the genetic algorithm are used to perform the final comparison between historical power and real-time power, and the final comparison between historical power and real-time power is output. The final comparison between historical power and real-time power includes a list of users with stable power demand and abnormal power demand. The list of users with stable power demand, that is, the real-time power is within the reasonable fluctuation range of historical power, and the list of users with abnormal power demand, that is, the real-time power exceeds the reasonable fluctuation range of historical power.

[0051] Furthermore, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S107: parsing the power adjustment task command to obtain the power information of the power-consuming side equipment and the location information of the power-consuming side equipment; determining the scheduling target according to the power adjustment task command, the scheduling target including the power transmission destination and the power adjustment target;

[0052] Generate the execution parameters for adjusting the power of the photovoltaic storage device according to the scheduling target, the execution parameters for adjusting the power of the photovoltaic storage device include the execution parameters of the photovoltaic storage device, the start and stop sequence of the power equipment during scheduling, the transmission path of the electric energy during retrieval, and the output power of the photovoltaic storage device;

[0053] The optical storage device electric energy power adjustment execution parameters are sent to the optical storage device, and the optical storage device executes the optical storage device electric energy power adjustment execution parameters to complete the adjustment and transmission of the electric energy power in the optical storage device.

[0054] Beneficial effects of the invention: Through real-time monitoring and intelligent regulation, the invention can maximize the use of photovoltaic power generation, give priority to using photovoltaic power for charging and storage, avoid energy waste, and thus improve energy utilization efficiency. Through the coordinated scheduling of photovoltaic storage and charging, the invention can effectively alleviate the pressure of the power grid during peak hours, reduce power grid fluctuations, and ensure the stable operation of the power grid.

[0055] When the data of the photovoltaic storage device is inaccurate, the present invention can promptly discover and process the data anomaly, ensure the accuracy and stability of photovoltaic storage power scheduling and electric power adjustment, and avoid adverse effects on the power grid.

[0056] The present invention processes the real-time monitoring data in time segments and sets up special data processing partitions, thus realizing refined management of the data. Each partition is responsible for processing the data in the corresponding time period, thus improving the efficiency and pertinence of data processing.

[0057] Through the preset data error analysis model and knowledge base, the present invention can quickly identify the data error type and match the corresponding repair strategy, realize efficient data repair, and provide reliable data support for subsequent power scheduling.

[0058] The present invention generates power adjustment task commands based on the real-time power demand of the power user side and combines historical power data through comparative analysis, thereby ensuring the rationality and accuracy of power dispatch. Through intelligent dispatching methods, the present invention can meet the diverse power demands of different users and ensure that the power user obtains a continuous and stable power supply. The present invention integrates a variety of advanced intelligent algorithms and optimization models, such as genetic algorithms, data error analysis models, etc., which significantly improves the intelligence level of the system.

[0059] Through automated data processing and scheduling processes, the need for human intervention is reduced, operation and maintenance costs are reduced, and the overall efficiency of the system is improved.

[0060] In summary, the beneficial effects of the present invention are reflected in improving energy utilization efficiency, enhancing system stability and reliability, achieving refined management and efficient scheduling, meeting diverse electricity needs, and improving the level of system intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0064] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0065] See also Figure 1 The present invention provides a method for coordinated scheduling of photovoltaic storage and charging, comprising:

[0066] Step S101, obtaining operation data of the optical storage device, determining the location of the optical storage device according to the operation data of the optical storage device, establishing an optical storage device partition according to the location of the optical storage device, monitoring the optical storage devices in the optical storage device partition, and obtaining real-time monitoring data of the optical storage device partition;

[0067] The operating data of the photovoltaic storage equipment includes but is not limited to photovoltaic power generation, power status of the energy storage system, load information of the charging facilities, equipment status information (such as temperature, pressure, etc.), energy conversion efficiency, geographic location information, timestamp (recording the time of data collection), error information (such as fault warning) and alarm information.

[0068] By analyzing the geographic location information in the operation data, it is very important to determine the specific location of each photovoltaic storage device for subsequent partition management, monitoring and scheduling. According to the location of the device, adjacent or close photovoltaic storage devices are divided into the same partition to better manage and monitor the equipment and enable the dispatching commands to be efficiently communicated and executed.

[0069] In each zone, all photovoltaic storage devices are monitored in real time, including but not limited to the actual output of photovoltaic power generation, the charging and discharging status of the energy storage system, the load of the charging facilities, etc. The real-time monitoring data will be used for subsequent scheduling decisions. The monitoring system collects the operating data of each photovoltaic storage device in real time to ensure the accuracy and real-time nature of the data, which will be used in the subsequent analysis and scheduling process.

[0070] Step S102, time segmenting the real-time monitoring of the optical storage device partition, the time segmentation includes a first time segment, a second time segment, a third time segment and a fourth time segment, extracting the time information of the real-time monitoring data of the optical storage device partition to obtain the time information of the real-time monitoring data of the optical storage device partition, matching the time information of the real-time monitoring data of the optical storage device partition with the time segmentation to obtain the time segmentation data matching result, performing data comparison on the data of each time segment in the time segmentation data matching result to obtain the time period data difference comparison result, the time period data difference comparison result includes data comparison without abnormality and data comparison abnormality;

[0071] The real-time monitoring data is processed by time segmentation. The time segmentation can be set according to actual needs. For example, a day is divided into four time periods: the first time period (such as 0:00-6:00), the second time period (such as 6:00-12:00), the third time period (such as 12:00-18:00) and the fourth time period (such as 18:00-24:00). The specific length of each time period can be adjusted according to actual conditions.

[0072] Extract the timestamp information of each piece of data from the real-time monitoring data, including detailed information such as year, month, day, hour, minute, second, etc. Match the extracted time information with the defined time segment to determine which time period each piece of real-time monitoring data belongs to.

[0073] In each time segment, all real-time monitoring data in the segment are compared and analyzed. The purpose is to check the consistency and continuity of the data and identify whether there is abnormal data. Through data comparison, the data difference comparison results of the time period are obtained. This result is divided into two categories: data comparison without abnormality and data comparison abnormality.

[0074] No abnormality in data comparison: It means that within the time segment, all monitoring data are normal, and no obvious data fluctuations or errors are found.

[0075] Data comparison anomaly: indicates that within the time segment, there are abnormal conditions such as data mutation, data duplication, or data incompleteness. These abnormal conditions are caused by equipment failure, data transmission errors, or external interference.

[0076] Through the time segmentation and data comparison in step S102, the present invention can timely discover abnormal situations in real-time monitoring data, providing a basis for subsequent data processing and error repair. At the same time, through time segmentation processing, the operating conditions of the optical storage device in different time periods can be better understood, providing strong support for formulating more reasonable scheduling strategies.

[0077] Step S103, establishing data processing partitions, the data processing partitions include a first data processing partition, a second data processing partition, a third data processing partition and a fourth data processing partition, establishing a corresponding relationship between the data processing partitions and the time segments, and when the time segment data difference comparison result is a data comparison abnormality, the time period data corresponding to the data comparison abnormality is transmitted to the corresponding data processing partition;

[0078] In order to effectively handle abnormal situations in real-time monitoring data, the present invention establishes multiple data processing partitions, specifically including a first data processing partition, a second data processing partition, a third data processing partition, and a fourth data processing partition. These partitions correspond to time segments (first time period, second time period, third time period, fourth time period).

[0079] Each data processing partition is designated to be responsible for processing data in a corresponding time segment. For example, the first data processing partition specifically processes data in a first time segment, the second data processing partition processes data in a second time segment, and so on.

[0080] In step S102, by comparing the time segment of the real-time monitoring data with the data, data comparison anomalies have been identified. When data in a certain time segment is found to be abnormal, the system will automatically transfer the abnormal data to the corresponding data processing partition for processing.

[0081] In each data processing partition, the system will further analyze and process these abnormal data, including a series of operations such as data cleaning, verification, and correction to ensure the accuracy and reliability of the data.

[0082] Through the implementation of step S103, the present invention realizes rapid response and effective processing of abnormal situations in real-time monitoring data. By establishing data processing partitions and establishing a clear correspondence between each partition and the time segment, the present invention not only improves the efficiency of data processing, but also ensures the accuracy and pertinence of data processing. This provides a solid foundation for subsequent power scheduling and transmission, and helps to ensure the stable operation and efficient output of the entire photovoltaic storage and charging system.

[0083] Step S104, in the data processing partition, substitute the time period data corresponding to the data comparison anomaly into the preset data error analysis model to obtain the data error type, match the data error type in the knowledge base, and obtain the data repair solution strategy;

[0084] When the data processing partition receives data from a period with abnormal data comparison, it substitutes this data into the preset data error analysis model. The data error analysis model is trained and verified, and can automatically identify the types of errors in the data, such as data mutation, data missing, data duplication, etc.

[0085] The data error analysis model performs in-depth analysis on the input data and outputs the error type of the data. The error type is used to determine which repair strategy needs to be adopted.

[0086] After identifying the data error type, a matching search is performed in the knowledge base. The knowledge base is a database that stores a large number of data repair cases and strategies, including various error types and their corresponding repair methods.

[0087] Based on the matching results in the knowledge base, a set of data repair solutions for the current error type is generated. The strategies include correction of erroneous data, filling of missing data, deletion of duplicate data and other operations.

[0088] When a data repair solution strategy is formulated, the system will automatically execute the strategy to repair the erroneous data. The repaired data will be reintegrated into the real-time monitoring data set for subsequent analysis and scheduling.

[0089] Through the implementation of step S104, the present invention can automatically and quickly identify and repair errors in real-time monitoring data, ensuring the accuracy and reliability of the data. This provides strong data support for subsequent power dispatch and transmission, and helps to ensure the stable operation and efficient output of the entire photovoltaic storage and charging system. At the same time, by establishing a knowledge base and continuously updating and improving it, the present invention also has the ability of continuous learning and optimization, which can continuously improve the effect of data processing and repair.

[0090] Step S105, establish time segment simulation data according to the data repair solution strategy, replace the erroneous data in the original time segment with the time segment simulation data to obtain updated time segment data, integrate the updated time segment data with the real-time monitoring data of the photovoltaic storage device partition, and obtain the real-time available electric energy of the photovoltaic energy storage.

[0091] According to the data repair solution strategy obtained in step S104, corresponding time segment simulation data is generated for the erroneous data identified in each time segment. The simulation data is generated based on analyzing historical data, considering the equipment operating status and external environmental factors, and is intended to replace the original erroneous data.

[0092] Use the generated time segment simulation data to replace the erroneous data in the original time segment one by one. During the replacement process, the system will ensure the continuity, consistency and accuracy of the data, and avoid data breakpoints or inconsistencies caused by data repair. After the erroneous data is replaced, the data in each time segment will become more accurate and reliable. These updated time segment data will serve as the basis for subsequent analysis and scheduling.

[0093] The updated time segment data is integrated with the real-time monitoring data of the photovoltaic storage device partition. During the integration process, the system will comprehensively consider various factors (such as photovoltaic power generation, energy storage system status, charging facility load, etc.) to fully reflect the real-time operating status and available power of the photovoltaic storage system.

[0094] Based on the integrated data, the real-time available power of solar energy storage will be calculated through complex algorithms and optimization models. The power value will serve as an important basis for subsequent power scheduling and transmission, ensuring that power users can obtain continuous and stable power supply.

[0095] Through the implementation of step S105, the present invention not only fixes the errors in the real-time monitoring data, but also provides more accurate and reliable data support for subsequent power scheduling and transmission through data integration and real-time available power calculation. The completion of this step indicates that the photovoltaic storage and charging coordinated scheduling system is ready to fully respond to real-time energy supply and demand changes, and can ensure the stable operation and efficient output of the entire system.

[0096] Step S106, receiving electricity demand information on the electricity consumer side, determining the identity of the user on the electricity consumer side and the location of the electricity consumer side according to the electricity demand information, extracting the electricity power information on the electricity consumer side from the electricity demand information on the electricity consumer side, obtaining the real-time electricity power demand on the user side, retrieving the historical electricity power of the identity of the user on the electricity consumer side and the location of the electricity consumer side from the database, comparing the historical electricity power of the identity of the user on the electricity consumer side and the location of the electricity consumer side with the real-time electricity power demand on the user side, obtaining the user side electricity power comparison result, the user side electricity power comparison result includes stable electricity power demand and abnormal electricity power demand, marking the electricity demand information on the electricity consumer side with stable electricity power demand as executable electricity power demand information, summarizing the executable electricity power demand information on the electricity consumer side to obtain an executable electricity power demand information data set, and generating an electricity power adjustment task command according to the executable electricity power demand information set;

[0097] Receiving electricity demand information from the electricity user side: The system first receives electricity demand information from the electricity user side. The information changes dynamically and reflects the user's real-time electricity demand.

[0098] Determine the identity and location of the user on the power consumption side: Analyze the power demand information and determine the identity and location of the user on the power consumption side who sent the demand. This is crucial for subsequent power dispatching, because different users are located in different geographical locations and have different power demands and access conditions.

[0099] Extract power consumption information: Extract power consumption information on the power consumption side from power demand information to obtain the real-time power demand on the user side. This is the direct basis for power dispatching.

[0100] Retrieve historical power consumption data: In the database, retrieve the user's historical power consumption data based on the user's identity and location. This data includes power consumption records over a period of time, which helps analyze the user's power consumption habits and demand changes.

[0101] Compare real-time and historical power consumption: Compare the real-time power demand of the user side with the user's historical power consumption data. The comparison results help to determine whether the current power demand is stable or abnormal. When comparing, you can set a certain error threshold (such as ±10%) to take into account fluctuations within the normal range.

[0102] Classification of power consumption comparison results: Based on the comparison results, the power consumption demand is divided into two categories: stable demand and abnormal demand. Stable power consumption demand means that the real-time power is within the reasonable fluctuation range of the historical power, while abnormal power consumption demand exceeds this range.

[0103] Marking executable power demand information: Mark the power demand information of the power consumption side with stable power demand as executable power demand information. This demand information will serve as the basis for subsequent power scheduling.

[0104] Summarize executable power demand: Summarize executable power demand information of all power consumption sides to form an executable power demand information data set, which represents all current stable and reasonable power demand.

[0105] Generate power adjustment task commands: Based on the executable power demand information data set, the system generates specific power adjustment task commands. These commands describe in detail how to adjust the power of the electric energy in the photovoltaic storage device and transmit it to the corresponding power-consuming device on the power-consuming side. The command includes information such as the destination of power transmission, the specific power adjustment target, the order of opening and closing the power equipment, and the transmission path of the power.

[0106] Through the implementation of step S106, the present invention can accurately understand and respond to the real-time power demand of the power user side, ensuring the rationality and accuracy of power scheduling. At the same time, by comparing the real-time and historical power data, the system can timely discover and handle abnormal power demand, avoid unnecessary energy waste and equipment loss, and the power adjustment task command finally generated provides clear guidance for subsequent power transmission and scheduling.

[0107] Step S107, according to the power adjustment task command, the power of the electric energy in the photovoltaic storage device is adjusted and transmitted to the corresponding power-consuming device on the power-consuming side.

[0108] First, the power adjustment task command is parsed to clarify the power demand and specific location information of the power-consuming equipment. This is a key step to ensure that power can be accurately delivered to the target equipment.

[0109] According to the task command, the system determines the specific goals of power dispatch, including the destination of power transmission (i.e. the location of the equipment on the power consumption side) and the adjustment target of power (i.e. the power consumption value that needs to be achieved).

[0110] In order to achieve effective scheduling and transmission of electric energy, a series of execution parameters for adjusting the electric energy power of photovoltaic storage devices are generated according to the scheduling objectives. The execution parameters include the specific execution parameters of the photovoltaic storage devices (such as output power settings), the start-up and shutdown sequence of power equipment during scheduling, the transmission path planning of electric energy, and the dynamic adjustment strategy of the output power of photovoltaic storage devices.

[0111] The generated execution parameters are sent to the corresponding photovoltaic storage device, which will instruct the photovoltaic storage device on how to adjust its output power and transmit the electrical energy to the designated power-consuming device through a predetermined transmission path.

[0112] After receiving the execution parameters, the photovoltaic storage device will adjust and transmit the power according to these parameters, involving the coordinated work of multiple photovoltaic storage devices to ensure the stable supply and efficient transmission of electricity. During the power dispatch and transmission process, the operating status of the photovoltaic storage device and the power transmission situation are continuously monitored. If any abnormal situation or fault information is found, the system will respond in time and take corresponding treatment measures.

[0113] When the electric energy is successfully delivered to the power-consuming equipment and meets its power demand, the system marks the completion of this power dispatch task. At this time, the system can record relevant data and log information for subsequent analysis and optimization.

[0114] Through the implementation of step S107, the photovoltaic storage and charging coordinated scheduling method of the present invention can efficiently and accurately perform power scheduling and transmission tasks, ensuring that the power-consuming equipment can obtain stable and reliable power supply. At the same time, the method also has a real-time monitoring and feedback mechanism, which can timely discover and deal with potential problems and risks, and ensure the stable operation and efficient output of the entire system.

[0115] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S101:

[0116] The operating data of photovoltaic storage equipment includes photovoltaic power generation, power status of energy storage system, load information of charging facilities, equipment status information, energy conversion efficiency, geographic location information, timestamp, error information and alarm information.

[0117] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S101:

[0118] Photovoltaic storage equipment includes solar panels, inverters, support systems, energy storage systems, and monitoring systems;

[0119] Determine, in the optical storage device partition, an optical storage device used by the optical storage device partition, and generate an optical storage device partition device information set;

[0120] Communication information is sent to the corresponding optical storage device in the optical storage device partition device information set according to a preset time period. If feedback information is obtained from the corresponding optical storage device, the communication status of the optical storage device is normal.

[0121] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S102:

[0122] The first time segment, the second time segment, the third time segment and the fourth time segment are divided into each time segment according to a 6-hour interval;

[0123] Collect monitoring data of the optical storage device partition in real time, where the monitoring data of the optical storage device partition includes a timestamp and a time point for identifying data collection;

[0124] Parse the timestamp in the monitoring data of each optical storage device partition, and extract the time information in the monitoring data of each optical storage device partition, the time information includes year, month, day, hour, minute, and second;

[0125] The parsed time information is matched with the defined time segment to obtain the time segment corresponding to the monitoring data of each optical storage device partition.

[0126] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S102:

[0127] In each time segment, the monitoring data of each optical storage device partition is compared;

[0128] No abnormality in data comparison means that there is no abnormal data in the monitoring data within the time segment;

[0129] Data comparison anomaly refers to the existence of data comparison anomaly between monitoring data within a time segment. Data comparison anomaly includes data mutation, data duplication, and data incompleteness.

[0130] The real-time monitoring data is segmented by time, specifically into the first time segment, the second time segment, the third time segment and the fourth time segment. Each time segment is divided according to a 6-hour time interval, which ensures that the data in each segment maintains a certain degree of continuity and independence in time.

[0131] The monitoring data of the optical storage device partitions are collected in real time. The monitoring data of the optical storage device partitions includes a timestamp, which is used to identify the exact time point of data collection to ensure the timeliness and traceability of the data.

[0132] Parse the timestamp in each monitoring data and extract detailed time information from it, including year, month, day, hour, minute, and second.

[0133] The extracted time information is matched with the pre-defined time segments to determine the time segment to which each monitoring data belongs. This process ensures that the data can be reasonably organized and managed in chronological order.

[0134] In each time segment, the monitoring data belonging to the segment is comprehensively compared and analyzed. This process is aimed at checking the consistency and completeness of the data so as to detect and deal with potential problems in a timely manner.

[0135] If there are no anomalies (such as data mutation, data duplication, or data incompleteness) between the monitoring data within a time segment, it is considered that there is no anomaly in the data comparison within the segment. This means that the data within the segment is stable and reliable.

[0136] If there are any abnormalities in the monitoring data within a time segment, that is, the data is suddenly changed, repeated or incomplete, the data within the segment is considered abnormal. These abnormalities are caused by various reasons, such as equipment failure, communication interruption or human error.

[0137] Through the implementation of step S102, the present invention not only realizes the effective organization and management of real-time monitoring data, but also ensures the accuracy and reliability of the data through data comparison and anomaly detection mechanisms. This provides a solid foundation for subsequent data processing, analysis and decision-making. At the same time, through the time segmentation method, the system can more flexibly respond to changes in energy supply and demand in different time periods, and achieve more accurate and efficient energy scheduling.

[0138] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S103:

[0139] Each data processing partition is used to process data and compare abnormal data within the corresponding time segment;

[0140] The data comparison abnormal data of the first time segment is processed by the first data processing partition, the data comparison abnormal data of the second time segment is processed by the second data processing partition, the data comparison abnormal data of the third time segment is processed by the third data processing partition, and the data comparison abnormal data of the fourth time segment is processed by the fourth data processing partition.

[0141] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S103:

[0142] According to the correspondence between time segments and data processing partitions, the data of the time period with data comparison anomalies is transferred to the corresponding data processing partition;

[0143] In the corresponding data processing partition, the preset data error analysis model is used to analyze the data with data comparison abnormalities, and the corresponding data repair solution strategy is matched in the knowledge base.

[0144] Data processing partitions corresponding to time segments are set up. Each partition is responsible for processing data and abnormal data within its corresponding time segment. The division of labor and cooperation not only improves the efficiency of data processing, but also improves the precise positioning and rapid response of abnormal data.

[0145] Specifically, the data comparison abnormal data of the first time segment is processed by the first data processing partition, the data of the second time segment is processed by the second data processing partition, and so on, until the data of the fourth time segment is processed by the fourth data processing partition.

[0146] When data comparison anomalies are detected in a certain time segment, the abnormal data is automatically transferred to the corresponding data processing partition according to the corresponding relationship between the time segment and the data processing partition. This is achieved through efficient internal communication mechanisms and intelligent data routing strategies to improve the real-time and accuracy of data.

[0147] In the corresponding data processing partition, the received abnormal data is deeply analyzed using the preset data error analysis model. The preset data error analysis model is based on advanced algorithms and statistical methods, and can automatically identify the error types (such as data mutation, data duplication, incomplete data, etc.) and error causes in the data.

[0148] Next, the internal knowledge base is searched for data repair solutions that match the current error type. These strategies are summarized in advance based on historical data and expert experience, and can provide effective repair methods for different types of errors.

[0149] According to the data repair solution strategy found, the system will repair the abnormal data. The repair process includes data cleaning, data reconstruction, error correction and other means to restore the accuracy and integrity of the data.

[0150] After the repair is completed, the system will generate a detailed repair report and feedback to the relevant maintenance personnel. The report content includes information such as error type, repair method, and data comparison before and after the repair, which helps maintenance personnel understand the cause of the problem and optimize system performance.

[0151] Through the implementation of step S103, the photovoltaic storage and charging coordinated scheduling method of the present invention realizes the refined management and efficient processing of abnormal situations in real-time monitoring data. It not only improves the accuracy and reliability of the data, but also provides strong data support for subsequent energy scheduling and decision-making. At the same time, it also reflects the advantages of the present invention in terms of intelligence and automation, which helps to improve the operating efficiency and stability of the entire system.

[0152] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S104:

[0153] Preprocess the data of the time period corresponding to the data comparison anomaly to obtain the preprocessed data of the time period corresponding to the data comparison anomaly. According to the user needs of the photovoltaic storage and charging coordinated scheduling, match the preset data error analysis model in the knowledge base. Before substituting the data into the preset data error analysis model, configure the parameters of the preset data error analysis model. The parameters of the preset data error analysis model include error type and error threshold.

[0154] Input the preprocessed data into the configured data error analysis model, preset the parameters of the data error analysis model to analyze the data in the time period corresponding to the preprocessed data comparison anomaly, identify the error type in the data in the time period corresponding to the preprocessed data comparison anomaly, and output the data error recognition result, which includes data mutation, data missing and error position;

[0155] The recognition results of data errors are matched in the knowledge base to obtain the data repair strategy corresponding to the recognition results of data errors. The data repair solution strategy includes error correction methods, data cleaning rules, data reconstruction strategies and manual intervention suggestions.

[0156] The data of the time period corresponding to the data comparison anomaly is preprocessed. The preprocessing steps include data cleaning (removing noise, irrelevant data, etc.), data format unification, data normalization, etc., to ensure the consistency and effectiveness of the data in subsequent analysis.

[0157] According to the specific needs of PV-storage-charging coordinated scheduling, the system matches the appropriate preset data error analysis models in the knowledge base. These models have been pre-trained and verified and can effectively analyze different types of error data.

[0158] Before inputting the preprocessed data into the model, the system needs to configure the parameters of the preset data error analysis model. The parameters for configuring the preset data error analysis model include error types (such as data mutation, data missing, etc.) and error thresholds (i.e., specific standards or boundaries for judging data anomalies).

[0159] The pre-processed data is fed into the configured data error analysis model. The model uses the preset parameters to conduct an in-depth analysis of the input data to identify the types of errors present. The process involves complex algorithms and statistical methods to capture abnormal patterns and regularities in the data.

[0160] After the analysis is completed, the model outputs the identification results of data errors, which describe in detail the types of errors in the data (such as data mutation, data missing, etc.) and the specific locations of the errors (such as timestamps, data items, etc.). This information is crucial for the formulation of subsequent data repair strategies.

[0161] Based on the results of data error identification, a matching data repair strategy is searched in the knowledge base. The knowledge base contains a variety of data repair methods for different error types, such as error correction methods (used to correct specific data values), data cleaning rules (used to remove or replace abnormal data), data reconstruction strategies (used to regenerate missing or damaged data), and manual intervention suggestions (manual intervention or review is required in specific circumstances).

[0162] The abnormal data is repaired according to the matched data repair strategy. The repair process involves the collaborative work of multiple steps and tools to ensure that the repaired data is both accurate and reliable.

[0163] Through the implementation of step S104, the photovoltaic storage and charging coordinated scheduling method of the present invention realizes comprehensive analysis and effective repair of abnormal data. This process not only improves the accuracy and reliability of the data, but also provides strong support for subsequent energy scheduling and decision-making. At the same time, it also reflects the innovation and practicality of the present invention in intelligent data processing and repair.

[0164] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S106:

[0165] The historical power consumption data of each user on the power consumption side is retrieved from the database. The historical power consumption data of each user on the power consumption side includes a timestamp and power consumption information. The real-time power consumption data uploaded from the power consumption side equipment is collected in real time.

[0166] The historical power consumption data and the real-time power consumption data of each power consumption side user are time segmented according to the time information to obtain the time segmented data of the historical power consumption data and the real-time power consumption data, the time segmented data of the historical power consumption data and the real-time power consumption data include the time segmented data of the historical power consumption data and the real-time power consumption data in the first time period, the time segmented data of the historical power consumption data and the real-time power consumption data in the second time period, the time segmented data of the historical power consumption data and the real-time power consumption data in the third time period, and the time segmented data of the historical power consumption data and the real-time power consumption data in the fourth time period;

[0167] The time segment data of the historical power consumption data and the real-time power consumption data in the first time period are from 0:00 to 6:00 every day, the time segment data of the historical power consumption data and the real-time power consumption data in the second time period are from 6:00 to 12:00 every day, the time segment data of the historical power consumption data and the real-time power consumption data in the third time period are from 12:00 to 18:00 every day, and the time segment data of the historical power consumption data and the real-time power consumption data in the fourth time period are from 18:00 to 24:00 every day;

[0168] Calculate the average or median of the historical power consumption of each electricity user in each time period as the benchmark power of the time period, compare the real-time power consumption with the benchmark power of the corresponding time period, set the real-time power comparison error threshold of ±10%, determine whether the real-time power comparison error threshold is within the error threshold, and obtain the comparison result of the real-time power consumption with the benchmark power of the corresponding time period;

[0169] The genetic algorithm is used to optimize the comparison between the real-time power consumption and the benchmark power in the corresponding time period, and a group of individuals with different thresholds and time segment parameters are generated as the initial population;

[0170] Each individual is a set of parameters. Each set of parameters is used to compare the real-time power with the historical power, and the accuracy and recall rate are calculated as fitness indicators;

[0171] According to the fitness index, individuals with high fitness are selected to enter the next generation, and crossover operations are performed on the selected individuals to exchange some parameters and generate new individuals;

[0172] Perform mutation operations on some individuals, randomly change the values ​​of parameters, and repeat fitness evaluation, selection, and crossover mutation operations until the predetermined number of iterations or fitness is reached;

[0173] The parameters optimized by the genetic algorithm are used to perform the final comparison between historical power and real-time power, and the final comparison between historical power and real-time power is output. The final comparison between historical power and real-time power includes a list of users with stable power demand and abnormal power demand. The list of users with stable power demand, that is, the real-time power is within the reasonable fluctuation range of historical power, the list of users with stable power demand, that is, the real-time power is within the reasonable fluctuation range of historical power, and the list of users with abnormal power demand, that is, the real-time power exceeds the reasonable fluctuation range of historical power.

[0174] First, the historical power consumption data of each user on the power consumption side is retrieved from the database. These data contain timestamps and corresponding power consumption information. At the same time, the system collects real-time power consumption data uploaded from the power consumption side equipment in real time to ensure the timeliness and accuracy of the data.

[0175] The historical power consumption data and the real-time power consumption data are segmented according to the time information. The segmentation basis is usually different time periods of the day, such as 0:00 to 6:00 a.m. is the first time period, 6:00 to 12:00 is the second time period, and so on, forming four time period data segments.

[0176] Perform statistical analysis (such as calculating the average or median) on the historical power consumption data of each power user in each time period to obtain the benchmark power for that time period. The benchmark power represents the power demand level of the user under normal circumstances.

[0177] Compare the real-time power consumption with the benchmark power in the corresponding time period, and set a real-time power comparison error threshold (such as ±10%). If the real-time power is within the error threshold, the user's power demand is considered stable; otherwise, it is considered abnormal.

[0178] Genetic algorithm initialization: Genetic algorithm is used to further optimize the comparison results between real-time power consumption and benchmark power. The genetic algorithm starts the iterative optimization process by generating a set of individuals with different thresholds and time segment parameters as the initial population.

[0179] Fitness evaluation and selection: Each individual represents a set of parameter settings. These parameters are used to compare real-time power with historical power to calculate fitness indicators such as accuracy and recall. Based on the fitness indicators, individuals with higher fitness are selected to enter the next generation.

[0180] Crossover and mutation operations: Perform crossover operations on selected individuals, exchange some parameters to generate new individuals, and increase the diversity of the population. At the same time, perform mutation operations on some individuals, randomly change the values ​​of parameters, and explore better solutions.

[0181] The fitness evaluation, selection, and crossover mutation operations are repeated until a predetermined number of iterations is reached or the fitness reaches a certain level.

[0182] Final comparison result output: The parameters optimized by the genetic algorithm are used to compare the final historical power with the real-time power, and the output includes a list of users with stable power demand and abnormal power demand. Stable power demand means that the real-time power is within the reasonable fluctuation range of the historical power, while abnormal power demand indicates that the real-time power exceeds this range.

[0183] Scheduling command generation: Based on the output user list, the system marks users with stable power demand as executable power demand information, and summarizes this information to generate power adjustment task commands. The power adjustment task command will guide the photovoltaic storage device to adjust its power output to meet the real-time needs of the power-consuming equipment and ensure the stability and reliability of power supply.

[0184] Through the implementation of step S106, the photovoltaic storage and charging coordinated scheduling method of the present invention not only improves the prediction accuracy of the electricity demand of users on the electricity consumption side, but also realizes the fine control of the output power of the photovoltaic storage equipment through the intelligent optimization algorithm, reflecting the comprehensive capabilities and innovative advantages of the present invention in data processing, algorithm optimization and energy scheduling.

[0185] Specifically, the photovoltaic storage and charging coordinated scheduling method provided by the present invention includes, in step S107: parsing the power adjustment task command to obtain the power information and location information of the power-side equipment; determining the scheduling target according to the power adjustment task command, the scheduling target including the power transmission destination and the power adjustment target;

[0186] Generate the execution parameters for adjusting the power of the photovoltaic storage device according to the scheduling target, the execution parameters for adjusting the power of the photovoltaic storage device include the execution parameters of the photovoltaic storage device, the start and stop sequence of the power equipment during scheduling, the transmission path of the electric energy during retrieval, and the output power of the photovoltaic storage device;

[0187] The optical storage device electric energy power adjustment execution parameters are sent to the optical storage device, and the optical storage device executes the optical storage device electric energy power adjustment execution parameters to complete the adjustment and transmission of the electric energy power in the optical storage device.

[0188] After receiving the power adjustment task command, the system first parses these commands. The parsing process extracts the power information and location information of the power-side equipment. This is the basic data for subsequent scheduling operations.

[0189] Based on the power consumption information and location information obtained through analysis, the system determines the scheduling target. The scheduling target mainly includes the destination of power transmission (i.e. the location of the power consumption side equipment) and the power adjustment target (i.e. the power consumption side equipment required by the power consumption side equipment).

[0190] Parameter formulation: Based on the determined dispatching target, the system generates execution parameters for adjusting the power of the photovoltaic storage equipment. These execution parameters cover all aspects of the operation of the photovoltaic storage equipment to ensure that the power can be adjusted and transmitted according to demand.

[0191] The execution parameters specifically include: Execution parameters of photovoltaic storage equipment: define the specific operating specifications that photovoltaic storage equipment needs to follow when adjusting the power of electric energy. The start-up and shutdown sequence of power equipment: details the start-up and stop sequence of related power equipment in the process of adjusting the power of electric energy to ensure the continuity and safety of operation. The transmission path of electric energy: clarifies the specific path for the transmission of electric energy from photovoltaic storage equipment to the power-consuming equipment, including the lines, transformers and other power equipment involved.

[0192] Output power adjustment of photovoltaic storage equipment: According to the needs of the power-consuming equipment, the output power of the photovoltaic storage equipment is set to ensure the balance between supply and demand.

[0193] Execution parameter sending: After the execution parameters are generated, the system sends these parameters to the optical storage device. The sending process ensures the integrity and accuracy of the parameters and avoids operational errors caused by transmission errors.

[0194] Parameter execution: After receiving the execution parameters, the PV storage device will operate according to the requirements of these parameters, including starting or stopping related power equipment, adjusting output power, etc., to ensure that the electric energy can be adjusted and transmitted to the power-consuming equipment according to demand.

[0195] The present invention proposes a photovoltaic storage and charging coordinated scheduling method, which aims to solve the problem that when the data of photovoltaic storage equipment is inaccurate, photovoltaic storage power scheduling and electric power adjustment will be chaotic, thereby causing the electricity user to be unable to obtain continuous and stable power and energy.

[0196] First, the operating data of the photovoltaic storage equipment is obtained, which includes photovoltaic power generation, energy storage system power status, charging facility load information, etc. The location of the photovoltaic storage equipment is determined based on this data, and the photovoltaic storage equipment partition is established for monitoring to obtain real-time monitoring data.

[0197] The real-time monitoring data is segmented into time segments (e.g., every 6 hours as a segment), and the data of each time segment is extracted for comparison. The purpose of this step is to identify whether there are any anomalies in the data (such as data mutation, duplication, or incompleteness).

[0198] Establish data processing partitions, each partition corresponds to a time segment, and is used to process data anomalies within the segment. When data comparison anomalies are found, the abnormal data is transferred to the corresponding data processing partition.

[0199] In the data processing partition, the abnormal data is substituted into the preset data error analysis model to identify the error type. Then the corresponding data repair strategy is matched in the knowledge base to generate time segment simulation data to replace the erroneous data.

[0200] The updated time-segmented data is integrated with the real-time monitoring data to calculate the real-time available power of the photovoltaic storage system.

[0201] Receive power demand information from the power user side, compare it with the historical power data of the power user side, and determine whether the power demand is stable. For stable power demand, generate power adjustment task commands.

[0202] According to the power adjustment task command, the electric power in the photovoltaic storage device is adjusted and transmitted to the corresponding power-consuming equipment to ensure that the power user obtains a continuous and stable power supply.

[0203] Through the above steps, the present invention can timely discover and process data anomalies when the data of the photovoltaic storage device is inaccurate, ensure the accuracy and stability of photovoltaic storage power scheduling and power adjustment, and thus meet the continuous and stable power demand of the electricity user.

[0204] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for coordinated scheduling of photovoltaic storage and charging, characterized in that: include: Step S101, obtaining operation data of the optical storage device, determining the location of the optical storage device according to the operation data of the optical storage device, establishing an optical storage device partition according to the location of the optical storage device, monitoring the optical storage devices in the optical storage device partition, and obtaining real-time monitoring data of the optical storage device partition; Step S102, time segmenting the real-time monitoring of the optical storage device partition, the time segmentation includes a first time segment, a second time segment, a third time segment and a fourth time segment, extracting the time information of the real-time monitoring data of the optical storage device partition to obtain the time information of the real-time monitoring data of the optical storage device partition, matching the time information of the real-time monitoring data of the optical storage device partition with the time segmentation to obtain the time segmentation data matching result, performing data comparison on the data of each time segment in the time segmentation data matching result to obtain the time period data difference comparison result, the time period data difference comparison result includes data comparison without abnormality and data comparison abnormality; Step S103, establishing data processing partitions, the data processing partitions include a first data processing partition, a second data processing partition, a third data processing partition and a fourth data processing partition, establishing a corresponding relationship between the data processing partitions and the time segments, and when the time segment data difference comparison result is a data comparison abnormality, the time period data corresponding to the data comparison abnormality is transmitted to the corresponding data processing partition; Step S104, in the data processing partition, substitute the time period data corresponding to the data comparison anomaly into the preset data error analysis model to obtain the data error type, match the data error type in the knowledge base, and obtain the data repair solution strategy; Step S105, establish time segment simulation data according to the data repair solution strategy, replace the erroneous data in the original time segment with the time segment simulation data to obtain updated time segment data, integrate the updated time segment data with the real-time monitoring data of the photovoltaic storage device partition, and obtain the real-time available power of the photovoltaic energy storage. Step S106, receiving electricity demand information on the electricity consumer side, determining the identity of the user on the electricity consumer side and the location of the electricity consumer side according to the electricity demand information, extracting the electricity power information on the electricity consumer side from the electricity demand information on the electricity consumer side, obtaining the real-time electricity power demand on the user side, retrieving the historical electricity power of the identity of the user on the electricity consumer side and the location of the electricity consumer side from the database, comparing the historical electricity power of the identity of the user on the electricity consumer side and the location of the electricity consumer side with the real-time electricity power demand on the user side, obtaining the user side electricity power comparison result, the user side electricity power comparison result includes stable electricity power demand and abnormal electricity power demand, marking the electricity demand information on the electricity consumer side with stable electricity power demand as executable electricity power demand information, summarizing the executable electricity power demand information on the electricity consumer side to obtain an executable electricity power demand information data set, and generating an electricity power adjustment task command according to the executable electricity power demand information set; Calculate the average or median of the historical power consumption of each electricity user in each time period as the benchmark power of the time period, compare the real-time power consumption with the benchmark power of the corresponding time period, set the real-time power comparison error threshold of ±10%, determine whether the real-time power comparison error threshold is within the error threshold, and obtain the comparison result of the real-time power consumption with the benchmark power of the corresponding time period; The genetic algorithm is used to optimize the comparison between the real-time power consumption and the benchmark power in the corresponding time period, and a group of individuals with different thresholds and time segment parameters are generated as the initial population; Each individual is a set of parameters. Each set of parameters is used to compare the real-time power with the historical power, and the accuracy and recall are calculated as fitness indicators; According to the fitness index, individuals with high fitness are selected to enter the next generation, and crossover operations are performed on the selected individuals to exchange some parameters and generate new individuals; Perform mutation operations on some individuals, randomly change the values ​​of parameters, and repeat fitness evaluation, selection, and crossover mutation operations until the predetermined number of iterations or fitness is reached; Use the parameters optimized by the genetic algorithm to compare the final historical power with the real-time power, and output the final historical power and real-time power comparison, which includes a list of users with stable power demand and abnormal power demand, a list of users with stable power demand, that is, real-time power within the reasonable fluctuation range of historical power, and a list of users with abnormal power demand, that is, real-time power beyond the reasonable fluctuation range of historical power; Step S107, according to the power adjustment task command, the power of the electric energy in the photovoltaic storage device is adjusted and transmitted to the corresponding power-consuming device on the power-consuming side.

2. The photovoltaic storage and charging coordinated scheduling method according to claim 1, characterized in that: In step S101, it includes: The operating data of photovoltaic storage equipment includes photovoltaic power generation, power status of energy storage system, load information of charging facilities, equipment status information, energy conversion efficiency, geographic location information, timestamp, error information and alarm information.

3. The photovoltaic storage and charging coordinated scheduling method according to claim 2, characterized in that: In step S101, it includes: Photovoltaic storage equipment includes solar panels, inverters, support systems, energy storage systems, and monitoring systems; Determine, in the optical storage device partition, an optical storage device used by the optical storage device partition, and generate an optical storage device partition device information set; Communication information is sent to the corresponding optical storage device in the optical storage device partition device information set according to a preset time period. If feedback information is obtained from the corresponding optical storage device, the communication status of the optical storage device is normal.

4. The photovoltaic storage and charging coordinated scheduling method according to claim 1, characterized in that: In step S102, it includes: The first time segment, the second time segment, the third time segment and the fourth time segment are divided into each time segment according to a 6-hour interval; Collect monitoring data of the optical storage device partition in real time, where the monitoring data of the optical storage device partition includes a timestamp and a time point for identifying data collection; Parse the timestamp in the monitoring data of each optical storage device partition, and extract the time information in the monitoring data of each optical storage device partition, the time information includes year, month, day, hour, minute, and second; The parsed time information is matched with the defined time segment to obtain the time segment corresponding to the monitoring data of each optical storage device partition.

5. The photovoltaic storage and charging coordinated scheduling method according to claim 4, characterized in that: In step S102, it includes: No abnormality in data comparison means that there is no abnormal data in the monitoring data within the time segment; Data comparison anomaly refers to the existence of data comparison anomaly between monitoring data within a time segment. Data comparison anomaly includes data mutation, data duplication, and data incompleteness.

6. The photovoltaic storage and charging coordinated scheduling method according to claim 1, characterized in that: In step S103, it includes: Each data processing partition is used to process data and compare abnormal data within the corresponding time segment; The data comparison abnormal data of the first time segment is processed by the first data processing partition, the data comparison abnormal data of the second time segment is processed by the second data processing partition, the data comparison abnormal data of the third time segment is processed by the third data processing partition, and the data comparison abnormal data of the fourth time segment is processed by the fourth data processing partition.

7. The photovoltaic storage and charging coordinated scheduling method according to claim 6, characterized in that: In step S103, it includes: According to the correspondence between time segments and data processing partitions, the data of the time period with data comparison anomalies is transferred to the corresponding data processing partition; In the corresponding data processing partition, the preset data error analysis model is used to analyze the data with data comparison abnormalities, and the corresponding data repair solution strategy is matched in the knowledge base.

8. The photovoltaic storage and charging coordinated scheduling method according to claim 1, characterized in that: In step S104, it includes: Preprocess the data of the time period corresponding to the data comparison anomaly to obtain the preprocessed data of the time period corresponding to the data comparison anomaly. According to the user needs of the photovoltaic storage and charging coordinated scheduling, match the preset data error analysis model in the knowledge base. Before substituting the data into the preset data error analysis model, configure the parameters of the preset data error analysis model. The parameters of the preset data error analysis model include error type and error threshold. Input the preprocessed data into the configured data error analysis model, preset the parameters of the data error analysis model to analyze the data in the time period corresponding to the preprocessed data comparison anomaly, identify the error type in the data in the time period corresponding to the preprocessed data comparison anomaly, and output the data error recognition result, which includes data mutation, data missing and error position; The recognition results of data errors are matched in the knowledge base to obtain the data repair strategy corresponding to the recognition results of data errors. The data repair solution strategy includes error correction methods, data cleaning rules, data reconstruction strategies and manual intervention suggestions.

9. The photovoltaic storage and charging coordinated scheduling method according to claim 1, characterized in that: In step S106, it includes: Retrieve the historical power consumption data of each user on the power consumption side from the database. The historical power consumption data of each user on the power consumption side includes a timestamp and power consumption information. Real-time power consumption data uploaded from the power consumption side device is collected in real time. The historical power consumption data and the real-time power consumption data of each power consumption side user are time segmented according to the time information to obtain the time segmented data of the historical power consumption data and the real-time power consumption data, the time segmented data of the historical power consumption data and the real-time power consumption data include the time segmented data of the historical power consumption data and the real-time power consumption data in the first time period, the time segmented data of the historical power consumption data and the real-time power consumption data in the second time period, the time segmented data of the historical power consumption data and the real-time power consumption data in the third time period, and the time segmented data of the historical power consumption data and the real-time power consumption data in the fourth time period; The time segment data of historical power consumption data and real-time power consumption data in the first time period are from 0:00 to 6:00 every day, the time segment data of historical power consumption data and real-time power consumption data in the second time period are from 6:00 to 12:00 every day, the time segment data of historical power consumption data and real-time power consumption data in the third time period are from 12:00 to 18:00 every day, and the time segment data of historical power consumption data and real-time power consumption data in the fourth time period are from 18:00 to 24:00 every day.

10. The photovoltaic storage and charging coordinated scheduling method according to claim 1, characterized in that: In step S107, it includes: parsing the power adjustment task command to obtain the power information and location information of the power-side equipment; determining the scheduling target according to the power adjustment task command, the scheduling target includes the power transmission destination and the power adjustment target; Generate the execution parameters for adjusting the power of the photovoltaic storage device according to the scheduling target, the execution parameters for adjusting the power of the photovoltaic storage device include the execution parameters of the photovoltaic storage device, the start and stop sequence of the power equipment during scheduling, the transmission path of the electric energy during retrieval, and the output power of the photovoltaic storage device; The optical storage device electric energy power adjustment execution parameters are sent to the optical storage device, and the optical storage device executes the optical storage device electric energy power adjustment execution parameters to complete the adjustment and transmission of the electric energy power in the optical storage device.

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