A Dynamic Optimization Scheduling Algorithm for Photovoltaic Energy Storage System

Abstract

The present invention relates to the technical field of dynamic scheduling, and specifically to a dynamic optimization scheduling algorithm for a photovoltaic energy storage system. Based on the working environment of the photovoltaic energy storage system, it collects environmental light intensity data in real time, extracts the average power generation under the same light intensity, and receives the weather conditions in the current region in the future time period through weather forecast data, estimates the power generation in the future time period, and obtains power generation evaluation information. In the present invention, by capturing the environmental light conditions in real time and combining with historical power generation data, it can effectively predict the future power generation of the photovoltaic system, monitor the changes in the grid load in real time and consider the impact brought by specific activities, can make timely responses when the grid demand fluctuates, optimize the supply and scheduling of electric energy, continuously monitor the power generation equipment and handle abnormalities, and deal with equipment abnormalities by correcting the discharge amount in real time, reducing the risks in the operation of the energy storage system and providing solid technical support for the stable operation of the grid.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic scheduling, and particularly to a dynamic optimization scheduling algorithm for a photovoltaic energy storage system. Background Art

[0002] The technical field of dynamic scheduling involves technologies for real-time or near-real-time management and optimization of various resources. In the context of a photovoltaic energy storage system, dynamic scheduling focuses on maximizing the efficiency of power generation and storage facilities, especially in the field of renewable energy. The core content of this technical field involves the dynamic matching of energy demand and supply, including load balancing, energy storage management, and grid stability. Dynamic scheduling technologies not only support prediction-based scheduling strategies but also can adapt to real-time data feedback to optimize system performance and improve energy utilization efficiency.

[0003] Among them, the dynamic optimization scheduling algorithm for a photovoltaic energy storage system is to use methods and control strategies to adjust and optimize the working states of a photovoltaic system and energy storage devices, so as to achieve the optimal allocation and utilization of electric power. This algorithm covers the charge and discharge control of energy storage devices, the output regulation of photovoltaic power generation, and the dynamic balance between the two, including using power scheduling strategies to precisely control the photovoltaic output and energy storage state to adapt to grid demand and power supply safety requirements, and ensuring that the photovoltaic energy storage system can maintain high efficiency and stable performance under various operating conditions.

[0004] Traditional scheduling algorithms lack a sufficiently flexible prediction and adjustment mechanism, and there are obvious deficiencies in terms of reaction speed and accuracy when dealing with environmental changes. For example, traditional scheduling algorithms only execute based on preset patterns or limited historical data, and it is difficult to adapt to rapidly changing market demands and environmental conditions. In addition, traditional algorithms lack an effective real-time monitoring and immediate response mechanism when dealing with equipment abnormalities. Once equipment failures or drastic changes in the external environment occur, the stability and safety of traditional algorithms are difficult to guarantee, which will lead to the inability to immediately adjust the discharge efficiency, resulting in power supply interruptions, causing power supply instability and even grid accidents, bringing serious consequences to users and power supply safety. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a dynamic optimization scheduling algorithm for a photovoltaic energy storage system.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A dynamic optimization scheduling algorithm for a photovoltaic energy storage system, including the following steps:

[0007] S1: Based on the working environment of the photovoltaic energy storage system, collect real-time environmental light intensity data, extract the average power generation under the same light intensity, and receive the weather conditions in the current region in the future time period through weather forecast data, and predict the power generation in the future time period to obtain power generation evaluation information;

[0008] S2: Based on the power grid load data, extract the power grid load records within the target time period. According to the load changes within the target time, evaluate the power grid load fluctuation situation within the target time period, and according to the impact of activities in the future time period, evaluate the degree of power grid load fluctuation in the future time period to obtain the load fluctuation evaluation information;

[0009] S3: Based on the power generation evaluation information and the load fluctuation evaluation information, evaluate the available power of the photovoltaic energy storage system within the future time period according to the current stored power of the energy storage device. Combine the real-time load demand and the degree of load fluctuation of the power grid to adjust the discharge power of the energy storage device to obtain the real-time discharge adjustment information;

[0010] S4: Based on the real-time discharge adjustment information, monitor the working state of the power generation equipment of the photovoltaic energy storage system in real time, analyze the deviation of the power generation equipment from the normal working condition, evaluate the degree of abnormality of the photovoltaic power generation equipment, cut off the abnormal photovoltaic power generation equipment, and according to the expected power generation of the abnormal photovoltaic power generation equipment, correct the real-time discharge amount of the energy storage device to obtain the abnormal condition response information.

[0011] The improvement of the present invention is that the steps for obtaining the power generation evaluation information are as follows:

[0012] S111: Based on the working environment of the photovoltaic energy storage system, collect the ambient light intensity data in real time through a light intensity sensor, and extract the average power generation under the same light intensity through the power generation records of the photovoltaic energy storage system to obtain the power generation reference data;

[0013] S112: Receive the weather forecast data of the current area in the future time period, analyze the predicted change in light intensity to obtain the future light intensity prediction data;

[0014] S113: Based on the power generation reference data and the future light intensity prediction data, through the formula:

[0015] ;

[0016] Calculate the power generation in the predicted future time period to obtain the power generation evaluation information;

[0017] Wherein, is the power generation in the predicted future time period, is the historical average power generation, is the predicted light intensity, is the historical average light intensity, and are adjustment coefficients.

[0018] The improvement of the present invention is that the steps for evaluating the power grid load fluctuation situation within the target time period are as follows:

[0019] S211: Based on the power grid load data, collect the power grid load records within the target time period to obtain a load record data set;

[0020] S212: Based on the load record data set, conduct statistical analysis, calculate the average value of the load within the target time period to obtain load average value information;

[0021] S213: Based on the load average value information and the load record data set, through the formula:

[0022] ;

[0023] Calculate the standard deviation of the power grid load, and evaluate the power grid load fluctuation condition within the target time period according to the magnitude of the standard deviation of the power grid load;

[0024] Wherein, is the number of data points, is the th load value of the data point, is the load average value, represents the standard deviation of the power grid load.

[0025] The improvement of the present invention is that the steps for obtaining the load fluctuation evaluation information are as follows:

[0026] S221: Based on the large-scale event information in the future time period, including concerts, ball games and sports events, extract the number of people and time of the event to generate event impact data;

[0027] S222: Based on the event impact data, analyze the impact of the event on the power grid load, evaluate the increased amount of the power grid load during the event to obtain predicted load increase data;

[0028] S223: Based on the predicted load increase data and the standard deviation of the power grid load, through the formula:

[0029] ;

[0030] Calculate the power grid load fluctuation index, evaluate the power grid load fluctuation degree in the future time period to obtain load fluctuation evaluation information;

[0031] Wherein, is the power grid load fluctuation index, is the predicted load increase amount caused by the event, is the historical load average value, is the adjustment coefficient, represents the standard deviation of the power grid load.

[0032] The improvement of the present invention is that the steps for evaluating the available power of the photovoltaic energy storage system in the future time period are as follows:

[0033] S311: Extract the current stored electricity of the energy storage device based on the real-time operation data of the energy storage device, analyze the charge and discharge cycle efficiency of the energy storage device, and obtain the effective stored electricity data;

[0034] S312: Based on the effective stored electricity data and the power generation evaluation information, through the formula:

[0035] ;

[0036] Calculate the disposable electricity, and obtain the analysis result of the available electricity;

[0037] Wherein, is the current stored electricity of the energy storage device, is the electricity consumption for daily operation and maintenance, represents the disposable electricity, represents the power generation in the expected future period.

[0038] The improvement of the present invention is that the step of obtaining the real-time discharge adjustment information is:

[0039] S321: Based on the analysis result of the available electricity and the load fluctuation evaluation information, extract the real-time load demand of the power grid according to the real-time operation data of the power grid, and obtain the discharge correlation data;

[0040] S322: Based on the discharge correlation data, through the formula:

[0041] ;

[0042] Calculate the adjusted discharge power, and obtain the real-time discharge adjustment information;

[0043] Wherein, represents the length of the target period, is the adjustment coefficient, represents the adjusted discharge power, represents the disposable electricity, represents the real-time load demand of the power grid, represents the power grid load fluctuation index.

[0044] The improvement of the present invention is that the step of cutting off the abnormal photovoltaic power generation device is:

[0045] S411: Based on the real-time discharge adjustment information, monitor the real-time working parameters of the power generation device in real time, and obtain the real-time monitoring data of the power generation device;

[0046] S412: Based on the real-time monitoring data of the power generation device, compare it with the normal working condition, evaluate the deviation between the current working parameters and the normal working condition, and obtain the deviation data of the power generation device;

[0047] S413: Based on the deviation data of the power generation equipment, through the formula:

[0048] ;

[0049] Calculate the anomaly index of the photovoltaic power generation equipment, compare it with the preset anomaly threshold, cut off the photovoltaic power generation equipment whose anomaly index exceeds the preset anomaly threshold, and implement an anomaly notification to the staff to obtain the anomaly equipment cut-off result:

[0050] Wherein, represents the anomaly index of the photovoltaic power generation equipment, represents the current item of operating parameter, represents the standard item of operating parameter, is the weight coefficient of the item of operating parameter, is the total number of parameter items.

[0051] The improvement of the present invention is that the step of obtaining the abnormal condition response information is:

[0052] S421: Based on the abnormal equipment cut-off result, according to the number of abnormal photovoltaic power generation equipment, count the expected lost power generation of the abnormal photovoltaic power generation equipment to obtain the lost power generation data;

[0053] S422: Based on the lost power generation data and the real-time discharge adjustment information, through the formula:

[0054] ;

[0055] Calculate the corrected discharge power to obtain the abnormal condition response information;

[0056] Wherein, is the correction coefficient, represents the corrected discharge power, represents the adjusted discharge power, represents the expected power generation loss, represents the length of the target time period.

[0057] Compared with the prior art, the advantages and positive effects of the present invention are:

[0058] In the present invention, by collecting environmental light intensity data and grid load data in real time, adjusting the discharge power of the energy storage device in a timely manner and monitoring the power generation device, the response speed and adaptability of the photovoltaic energy storage system are improved. By capturing the environmental light conditions in real time and combining with historical power generation data, the future power generation of the photovoltaic system can be effectively predicted. By monitoring the changes in the grid load in real time and considering the impact brought by specific activities, timely responses can be made when the grid demand fluctuates, optimizing the power supply and dispatching. The continuous monitoring and abnormal handling of the power generation device ensure the continuity and safety of the energy storage system. By correcting the discharge amount in real time to deal with equipment anomalies, the risks in the operation of the energy storage system are reduced, providing solid technical support for the stable operation of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is the flowchart of the method of the present invention;

[0060] Figure 2 is the flowchart of obtaining the power generation evaluation information of the present invention;

[0061] Figure 3 is the flowchart of evaluating the grid load fluctuation situation within the target time period of the present invention;

[0062] Figure 4 is the flowchart of obtaining the load fluctuation evaluation information of the present invention;

[0063] Figure 5 is the flowchart of evaluating the available power of the photovoltaic energy storage system within the future time period of the present invention;

[0064] Figure 6 is the flowchart of obtaining the real-time discharge adjustment information of the present invention;

[0065] Figure 7 is the flowchart of cutting off the abnormal photovoltaic power generation device of the present invention;

[0066] Figure 8 is the flowchart of obtaining the abnormal condition response information of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0067] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0068] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0069] Please refer to Figure 1 , the present invention provides a technical solution: a dynamic optimization scheduling algorithm for a photovoltaic energy storage system, including the following steps:

[0070] S1: Based on the working environment of the photovoltaic energy storage system, through a light intensity sensor, collect environmental light intensity data in real time, extract the average power generation under the same light intensity through the power generation records of the photovoltaic energy storage system, receive the weather conditions in the current area in the future period through weather forecast data, and estimate the power generation in the future period according to weather changes to obtain power generation evaluation information;

[0071] S2: Based on the grid load data, extract the grid load records in the target time period, evaluate the grid load fluctuation in the target time period according to the load change in the target time, and evaluate the degree of grid load fluctuation in the future period according to the influence of future activities to obtain load fluctuation evaluation information;

[0072] S3: Based on the power generation evaluation information and the load fluctuation evaluation information, evaluate the available power of the photovoltaic energy storage system in the future period according to the current stored power of the energy storage device, and adjust the discharge power of the energy storage device in combination with the real-time load demand and the degree of load fluctuation of the grid to obtain real-time discharge adjustment information;

[0073] S4: Based on the real-time discharge adjustment information, monitor the working state of the power generation equipment of the photovoltaic energy storage system in real time, analyze the deviation of the power generation equipment from the normal working condition, evaluate the abnormality degree of the photovoltaic power generation equipment, cut off the abnormal photovoltaic power generation equipment, and correct the real-time discharge amount of the energy storage device according to the predicted power generation of the abnormal photovoltaic power generation equipment to obtain abnormal condition response information.

[0074] The power generation evaluation information includes the predicted maximum power generation, minimum power generation and average power generation. The load fluctuation evaluation information includes the predicted load during peak hours, the predicted load during valley hours and the average load during normal hours. The real-time discharge adjustment information includes the adjusted discharge start time, discharge end time and discharge power. The abnormal condition response information includes the predicted missing power generation of the cut-off equipment and the adjusted energy storage discharge strategy.

[0075] Please refer to Figure 2 , the steps for obtaining power generation evaluation information are as follows:

[0076] S111: Based on the working environment of the photovoltaic energy storage system, use a light intensity sensor to collect ambient light intensity data in real time, and extract the average power generation under the same light intensity through the power generation records of the photovoltaic energy storage system to obtain power generation reference data;

[0077] Based on the working environment of the photovoltaic energy storage system, use an environmental monitoring device to collect light intensity data in real time. The environmental monitoring device can capture light changes through a photosensitive sensor. The sensor is installed on the photovoltaic panel and can monitor the real-time changes in sunlight intensity. The monitoring data is updated every minute and transmitted to the central processing unit. The central processing unit records and analyzes the data. Through time series analysis, calculate the average power generation of historical data under the same light conditions. The calculation process involves data normalization and removal of outliers. For example, if the data of a certain minute deviates significantly from the average value by more than two standard deviations, then this data point is regarded as an outlier and excluded from the analysis. The statistical analysis of historical data is carried out based on the Pandas library in Python, and its DataFrame structure is used to store and operate time series data to ensure the efficiency and accuracy of data processing, and obtain the average power generation data under each light intensity.

[0078] S112: Receive the weather forecast data for the future period in the current area, analyze the expected change in light intensity, and obtain the future light intensity prediction data;

[0079] Receive the weather forecast data for the future period in the current area. The data is provided by the local meteorological station and includes hourly weather forecasts for the next few days, including information such as light intensity, temperature, cloud cover, etc. It is automatically obtained from the server of the meteorological station using a data interface. The obtained data is processed by analysis software. The software can analyze the changing trend of the expected light intensity. Based on statistical methods such as moving average or exponential smoothing method, predict the future light intensity. The prediction model is trained with historical weather data to ensure the accuracy and practicality of the prediction. Through analysis, obtain the future light intensity prediction data.

[0080] S113: Based on the power generation reference data and the future light intensity prediction data, through the formula:

[0081] ;

[0082] Calculate the expected power generation for the future period to obtain the power generation evaluation information;

[0083] Among them, is the expected power generation for the future period, is the historical average power generation, is the predicted light intensity, is the historical average light intensity, and is the adjustment coefficient;

[0084] Formula:

[0085] ;

[0086] The advantage of the formula is that by adjusting the coefficients and , the power generation prediction under different environmental conditions can be flexibly adapted, making the model more accurately reflect the sensitivity of the actual light change to power generation.

[0087] Detailed explanation of the formula and the derivation process of formula calculation:

[0088] In the formula, is the power generation in the predicted future period, is the average power generation calculated based on historical data, is the future light intensity predicted based on meteorological forecast data, is the average light intensity calculated from historical light data. The adjustment coefficients and are determined by regression analysis of historical data. For example, the least squares method can be used to fit these two parameters according to historical power generation and light intensity data.

[0089] The specific calculation process of the formula is as follows:

[0090] Suppose we obtain from the data , , , , , then:

[0091] ;

[0092] The results show that considering the increase in future light intensity, the predicted power generation will increase slightly compared to the historical average, which is consistent with the purpose of the prediction model, that is, to accurately predict the power generation based on future light conditions.

[0093] Please refer to Figure 3 for the steps to evaluate the grid load fluctuation during the target time period:

[0094] S211: Based on the grid load data, collect the grid load records within the target time period to obtain the load record dataset;

[0095] Based on the power grid load data, collect the power grid load records within the target time period. Through the power grid monitoring equipment, monitor the operating status of the power grid in real time to obtain the power grid load data set. The monitoring equipment records data at fixed intervals, including the current and voltage information of each connection point. The collected data is first transmitted to the data processing center. The data processing center uses automated software to perform preliminary cleaning and sorting on the collected data, removing obvious error readings or duplicate data entries. For the power grid load records, pay special attention to the load fluctuations during peak and trough periods to more accurately evaluate the operating efficiency and stability of the power grid, and obtain the load record data set.

[0096] S212: Based on the load record data set, conduct statistical analysis, calculate the average value of the load within the target time period, and obtain the load average value information;

[0097] Based on the load record data set, conduct statistical analysis, calculate the power grid load value of each recording point within the target time period, use statistical software to calculate the average value of the load values. The calculation process includes the calculation of the arithmetic mean, and also includes the analysis of the distribution characteristics of the data, such as calculating the median and mode, as well as the skewness and kurtosis of the data. Through statistical analysis, obtain more comprehensive power grid load average value information, which helps the power grid operator evaluate the operating efficiency of the power grid at different time periods.

[0098] S213: Based on the load average value information and the load record data set, through the formula:

[0099] ;

[0100] Calculate the standard deviation of the power grid load, and evaluate the power grid load fluctuation situation within the target time period according to the size of the standard deviation of the power grid load;

[0101] Among them, is the number of data points, is the th load value of the data point, is the load average value, represents the standard deviation of the power grid load;

[0102] Formula:

[0103] ;

[0104] The advantage of the formula is that by calculating the standard deviation of the power grid load data, the volatility of the power grid load can be effectively evaluated, which helps the power grid operator perform better load management and risk assessment.

[0105] Detailed explanation of the formula and the derivation process of the formula calculation:

[0106] Among them, is the number of data points, is the payload value of the th data point, is the average payload,

[0107] represents the standard deviation of the grid load. It is assumed that a total of 3 data points were collected during the monitoring period, and the payload values of the data points were 120MW, 150MW, and 180MW respectively. Calculate the average payload of these data points, and then use this average value to calculate the standard deviation: :

[0108] ;

[0109] Calculate the square of the deviation for each data point:

[0110] ;

[0111] ;

[0112] ;

[0113] Calculate the average of the squared deviations:

[0114] ;

[0115] Calculate the standard deviation :

[0116] ;

[0117] The calculation result, the standard deviation of the grid load is 24.49MW, which indicates that during the target period, the fluctuation of the grid load is relatively small, the load values are roughly concentrated around the average value of 150MW, and there is only a small fluctuation range. The calculation result helps the grid operator to evaluate the stability of the grid load and optimize the grid management strategy accordingly to ensure the stable operation of the grid under different load conditions.

[0118] Please refer to Figure 4 , the steps to obtain the load fluctuation evaluation information are as follows:

[0119] S221: Based on the large event information in the future period, including concerts, ball games, and sports events, extract the number of people and time of the event to generate event impact data;

[0120] Based on the information of large-scale events in future time periods, including concerts, ball games, and sports events, the event data is provided by the Urban Event Management Bureau. The data for each event includes the event type, the expected number of participants, the event date and time. The information is input into the central database, which can automatically classify and organize the data to generate detailed information about future events. This information is crucial for predicting the urban population flow. By analyzing the historical population flow data of different types of events, a population flow prediction model is established to predict the possible population flow generated by each type of event. The model uses linear regression analysis to predict the population flow based on the scale and type of the event, generating event impact data.

[0121] S222: Based on the event impact data, analyze the impact of the event on the power grid load, evaluate the increase in the power grid load during the event, and obtain the predicted load increase data;

[0122] Based on the event impact data, analyze the impact of the event on the power grid load, conduct data analysis, receive the event impact data and combine it with the historical load data of the power grid, and use machine learning methods to analyze the potential increase in the power grid load during the event. The evaluation model predicts the increase in the load based on the population flow of the event and the historical power grid load data during the same period. The model is trained with historical data and can automatically adjust its parameters to adapt to the impact of different types of events on the power grid load, outputting the predicted load increase data.

[0123] S223: Based on the predicted load increase data and the standard deviation of the power grid load, through the formula:

[0124] ;

[0125] Calculate the power grid load fluctuation index, evaluate the degree of power grid load fluctuation in the future time period, and obtain the load fluctuation evaluation information;

[0126] Among them, is the power grid load fluctuation index, is the predicted load increase caused by the event, is the historical load average value, is the adjustment coefficient, represents the standard deviation of the power grid load;

[0127] Formula:

[0128] ;

[0129] The advantage of the formula is that by adjusting the coefficient to accurately adjust the impact of the predicted load increment caused by a specific event on the standard deviation of the power grid, so as to more accurately evaluate the power grid load fluctuation.

[0130] Detailed explanation of the formula and the derivation process of the formula calculation:

[0131] Among them, is the standard deviation of the power grid load, is the predicted load increase caused by the event, is the historical load average value, is the adjustment coefficient. Let , , , , then the calculation formula is as follows:

[0132] ;

[0133] The results show that during the predicted large-scale event, the load fluctuation degree of the power grid will increase slightly, which is important reference information for power grid operators and helps with risk assessment and resource allocation to ensure the stable operation of the power grid during the event.

[0134] Please refer to Figure 5 , the steps to evaluate the available power of the photovoltaic energy storage system in the future period are as follows:

[0135] S311: Based on the real-time operation data of the energy storage device, extract the current stored power of the energy storage device, analyze the charge and discharge cycle efficiency of the energy storage device, and obtain the effective stored power data;

[0136] Based on the real-time operation data of the energy storage device, monitor the state of the energy storage device in real time. Through the sensors connected to the energy storage device, collect data such as the voltage, current, and temperature of the battery in real time. After the data is preliminarily processed, calculate the current stored power, and then analyze the charge and discharge cycle efficiency of the energy storage device. The analysis includes calculating the energy loss rate of the battery in each charge and discharge cycle, calculating using the energy loss rate formula, considering the aging factor of the battery, and periodically calibrating the measurement tool to ensure the accuracy of the data, so as to obtain the effective stored power data. The effective stored power is the actual available power considering the loss, and the data is of great significance for predicting the operation efficiency and life of the energy storage device.

[0137] S312: Based on the effective stored power data and the power generation evaluation information, through the formula:

[0138] ;

[0139] Calculate the available power to obtain the available power analysis result;

[0140] Among them, is the current stored power of the energy storage device, is the power consumption for daily operation and maintenance, represents the available power, represents the power generation in the predicted future period;

[0141] Formula:

[0142] ;

[0143] The benefit of the formula is that by integrating the expected future power generation, the effective storage capacity of the energy storage device, and the operation and maintenance consumption, the available power can be accurately calculated, thus providing decision-making support for the operation of the power system.

[0144] Detailed explanation of the formula and the derivation process of formula calculation:

[0145] Among them, is the current storage capacity of the energy storage device, is the daily operation and maintenance power consumption, represents the disposable power, represents the power generation in the expected future period. Set , , , and perform the calculation:

[0146] ;

[0147] The result shows that after considering the operation and maintenance consumption, there is 1250 kWh of power available for allocation in the future period, which helps the power system operator optimize resource allocation and improve the operation efficiency and reliability of the system.

[0148] Please refer to Figure 6 , and the steps to obtain the real-time discharge adjustment information are as follows:

[0149] S321: Based on the available power analysis result and the load fluctuation evaluation information, according to the real-time operation data of the power grid, extract the real-time load demand of the power grid to obtain the discharge correlation data;

[0150] Based on the available power analysis result and the load fluctuation evaluation information, monitor the power grid load in real time. The current and voltage data are recorded in real time through sensors installed at each key node of the power grid. The real-time data is transmitted to the central control room, and the total load demand of the power grid is calculated using these real-time data. By comparing and analyzing the historical data, the load fluctuations caused by specific activities or periods are identified. The analysis helps the operator understand the performance of the power grid during specific periods, thus making scheduling decisions to obtain the discharge correlation data. The discharge correlation data not only includes the current load demand but also predicts the load changes in the next few hours, providing important decision-making support for the operation and maintenance of the power grid.

[0151] S322: Based on the discharge correlation data, through the formula:

[0152] ;

[0153] Calculate the adjusted discharge power to obtain real-time discharge adjustment information;

[0154] Among them, represents the length of the target period, is the adjustment coefficient, represents the adjusted discharge power, represents the available power, represents the real-time load demand of the power grid, represents the power grid load fluctuation index;

[0155] Formula:

[0156]

[0157] The benefit of the formula is that it can optimize the operation efficiency and stability of the power grid by dynamically adjusting the power generation and discharge strategies to cope with the power grid load demand and fluctuations.

[0158] Detailed explanation of the formula and the derivation process of formula calculation:

[0159] Among them, represents the length of the target period (hours), is the adjustment coefficient used to adjust the change in power demand caused by load fluctuations, represents the adjusted discharge power, represents the available power, represents the real-time load demand of the power grid, represents the power grid load fluctuation index. Set hours, , , , :

[0160] ;

[0161] The results show that considering the factors of the real-time load and load fluctuations of the power grid, the system will discharge at a power of 1002.547 kW in the next hour to meet the actual demand of the power grid, which helps to balance the supply and demand relationship of the power grid and improve the operation efficiency and reliability of the power grid.

[0162] Please refer to Figure 7 , the steps to cut off the abnormal photovoltaic power generation equipment are as follows:

[0163] S411: Based on the real-time discharge adjustment information, monitor the real-time working parameters of the power generation equipment in real time to obtain the real-time monitoring data of the power generation equipment;

[0164] Based on real-time discharge adjustment information, the working parameters of the power generation equipment are monitored in real time. Key parameters such as temperature, voltage, and current are collected in real time through multiple sensors connected to the power generation equipment. The data is uploaded to the central database in real time and processed in real time. The processing process includes data cleaning, standardization, and anomaly detection. By comparing the real-time data with the set working parameter thresholds, any parameters that exceed the normal operating range can be immediately identified, ensuring that the power generation equipment can operate in the best state. The real-time monitoring data of the power generation equipment is obtained. The real-time monitoring data not only provides the first-hand equipment operation status for the operation and maintenance team, but also provides a basis for performance analysis and maintenance decision-making.

[0165] S412: Based on the real-time monitoring data of the power generation equipment, compare it with the normal working conditions, evaluate the deviation of the current working parameters from the normal working conditions, and obtain the deviation data of the power generation equipment;

[0166] Based on the real-time monitoring data of the power generation equipment, compare it with the normal working conditions. Through the data analysis system, analyze the deviation between the current working parameters and the set standard working parameters. The analysis includes calculating the percentage difference between the real-time value and the standard value of each key parameter, automatically calculating the deviation using a preset formula, and evaluating whether the deviation will affect the power generation efficiency or safe operation. In this way, the operation and maintenance team can quickly respond to any potential equipment problems, make adjustments or repairs in a timely manner to avoid greater equipment failures or losses, and obtain the deviation data of the power generation equipment. The accurate analysis of the deviation data helps the maintenance team optimize the equipment performance and extend the equipment life.

[0167] S413: Based on the deviation data of the power generation equipment, through the formula:

[0168] ;

[0169] Calculate the anomaly index of the photovoltaic power generation equipment, compare it with the preset anomaly threshold, cut off the photovoltaic power generation equipment whose anomaly index exceeds the preset anomaly threshold, and issue an anomaly notice to the staff to obtain the anomaly equipment cut-off result:

[0170] Among them, represents the anomaly index of the photovoltaic power generation equipment, represents the current th item of operating parameters, represents the standard th item of operating parameters, is the weight coefficient of the th item of operating parameters, is the total number of parameter items;

[0171] Formula:

[0172] ;

[0173] The advantage of the formula is that it synthesizes the deviations of multiple operating parameters through weighted summation to form a single anomaly index, which makes the evaluation of the overall health status of the equipment more intuitive and manageable.

[0174] Detailed explanation of the formula and the derivation process of formula calculation:

[0175] Among them, represents the anomaly index of the photovoltaic power generation equipment, represents the current item of operating parameter, represents the standard item of operating parameter, is the weight coefficient of the item of operating parameter, is the total number of parameter items. Set , , , , , , , , , , and perform the calculation:

[0176] ;

[0177] The result shows that the anomaly index of the photovoltaic power generation equipment is 0.08. If this value exceeds the preset anomaly threshold, such as 0.1, the equipment needs to be cut off and subsequent inspections and maintenance are required. The application of this formula ensures the reliability and efficiency of the equipment operation.

[0178] Please refer to Figure 8 , and the steps to obtain the response information for abnormal conditions are as follows:

[0179] S421: Based on the abnormal equipment cut-off result, according to the number of abnormal photovoltaic power generation equipment, statistically calculate the expected lost power generation of the abnormal photovoltaic power generation equipment to obtain the lost power generation data;

[0180] Based on the abnormal equipment cut-off result, statistically calculate the number of photovoltaic power generation equipment cut off due to abnormalities, and statistically include the model, location, and abnormal time of each equipment. Predict the power generation of the equipment under normal working conditions through a pre-set model, calculate the lost power generation of each equipment by comparing the model prediction value with the actual power generation, and summarize the lost power generation of all abnormal equipment to obtain the lost power generation data. The lost power generation data provides a basis for decision-making, such as whether to temporarily increase other power generation resources or adjust the power grid operation strategy to ensure the stable operation of the power grid and the continuity of power supply.

[0181] S422: Based on the lost power generation data and real-time discharge adjustment information, through the formula:

[0182] ;

[0183] calculate the corrected discharge power to obtain the abnormal condition response information;

[0184] wherein, is the correction coefficient, represents the corrected discharge power, represents the adjusted discharge power, represents the predicted power generation loss, represents the length of the target time period;

[0185] Formula:

[0186] ;

[0187] The advantage of the formula is that by considering the impact of lost power generation on the discharge power, the discharge strategy is dynamically adjusted to ensure the stability and reliability of the power grid supply.

[0188] Detailed explanation of the formula and the formula calculation derivation process:

[0189] wherein, is the correction coefficient used to adjust the discharge power to compensate for the impact of lost power generation, represents the corrected discharge power, represents the adjusted discharge power, represents the predicted power generation loss, represents the length of the target time period (hours). Set , , , :

[0190] ;

[0191] The results show that after considering the impact of lost power generation, the discharge will be carried out at a power of 927.547 kW in the next hour, which helps to balance the power grid supply and demand and reduce the instability risk caused by abnormal power generation equipment.

[0192] The above is only the preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A photovoltaic energy storage system dynamic optimization scheduling algorithm, characterized in that: The following steps are involved: S1: Based on the working environment of the photovoltaic energy storage system, the ambient light intensity data is collected in real time, the average power generation under the same light intensity is extracted, and the weather conditions in the future period of the current area are received through weather forecast data, the power generation in the future period is estimated, and the power generation evaluation information is obtained; S2: Based on the grid load data, extract the grid load records within the target time period, evaluate the grid load fluctuation within the target time period according to the load changes within the target time period, and evaluate the grid load fluctuation degree in the future time period according to the impact of activities in the future time period to obtain load fluctuation evaluation information; The steps for acquiring the load fluctuation assessment information are as follows: S221: Based on the information of large-scale events in the future period, including concerts, ball games and sports events, the flow of people and time of the events are extracted to generate event impact data; S222: Analyze the impact of the activity on the grid load based on the activity impact data, evaluate the increase in grid load during the activity, and obtain predicted load increase data; S223: Based on the predicted load increase data, by formula: ； Calculate the grid load fluctuation index, evaluate the grid load fluctuation degree in the future period, and obtain load fluctuation evaluation information; in, is the grid load fluctuation index, is the predicted load increase due to the activity, is the historical load average, is the adjustment factor, Represents the standard deviation of the grid load S3: Based on the power generation assessment information and the load fluctuation assessment information, the available power of the photovoltaic energy storage system in the future period is assessed according to the current storage capacity of the energy storage device, and the discharge power of the energy storage device is adjusted in combination with the real-time load demand and load fluctuation degree of the power grid to obtain real-time discharge adjustment information; The steps for evaluating the available power of the photovoltaic energy storage system in the future period are: S311: extracting the current storage capacity of the energy storage device based on the real-time operation data of the energy storage device, analyzing the charge and discharge cycle efficiency of the energy storage device, and obtaining effective storage capacity data; S312: Based on the effective power storage data and power generation evaluation information, the formula: ； Calculate the available power and obtain the available power analysis results; in, is the current storage capacity of the energy storage device, It is the power consumption of daily operation and maintenance. Represents available power. Represents the expected power generation in the future period; The step of acquiring the real-time discharge adjustment information is as follows: S321: Based on the available power analysis result and the load fluctuation assessment information, according to the real-time operation data of the power grid, extract the real-time load demand of the power grid to obtain discharge-related data; S322: Based on the discharge-related data, by formula: ； Calculate the adjusted discharge power and obtain real-time discharge adjustment information; in, represents the length of the target period, is the adjustment coefficient, represents the adjusted discharge power, Represents available power. Represents the real-time load demand of the power grid, Represents the grid load fluctuation index; S4: Based on the real-time discharge adjustment information, monitor the working status of the power generation equipment of the photovoltaic energy storage system in real time, analyze the deviation of the power generation equipment from the normal working condition, evaluate the abnormality of the photovoltaic power generation equipment, cut off the abnormal photovoltaic power generation equipment, and correct the real-time discharge amount of the energy storage equipment according to the expected power generation of the abnormal photovoltaic power generation equipment to obtain abnormal condition response information.

2. The photovoltaic energy storage system dynamic optimization scheduling algorithm according to claim 1 is characterized in that: The steps for obtaining the power generation evaluation information are as follows: S111: Based on the working environment of the photovoltaic energy storage system, the ambient light intensity data is collected in real time through the light intensity sensor, and the average power generation under the same light intensity is extracted through the power generation record of the photovoltaic energy storage system to obtain the power generation reference data; S112: Receive weather forecast data for the future period in the current area, analyze the expected change in light intensity, and obtain future light intensity prediction data; S113: Based on the power generation reference data and the future light intensity prediction data, the formula: ； Calculate the power generation expected in the future period and obtain power generation assessment information; in, To estimate the power generation in the future period, is the historical average power generation, is the predicted light intensity, is the historical average light intensity, and is the adjustment factor.

3. The photovoltaic energy storage system dynamic optimization scheduling algorithm according to claim 1 is characterized in that: The steps of evaluating the power grid load fluctuation during the target period are as follows: S211: Based on the power grid load data, collect power grid load records within a target time period to obtain a load record data set; S212: Performing statistical analysis based on the load record data set, calculating the average value of the load in the target period, and obtaining load average value information; S213: Based on the load average value information and the load record data set, the formula: ； Calculate the standard deviation of the grid load, and evaluate the grid load fluctuation within the target period based on the size of the standard deviation of the grid load; in, is the number of data points, It is The load value of the data point, is the load average, Represents the standard deviation of the grid load.

4. The photovoltaic energy storage system dynamic optimization scheduling algorithm according to claim 1 is characterized in that: The steps of cutting off abnormal photovoltaic power generation equipment are: S411: Based on the real-time discharge adjustment information, real-time operating parameters of the power generation equipment are monitored in real time to obtain real-time monitoring data of the power generation equipment; S412: Based on the real-time monitoring data of the power generation equipment, the real-time monitoring data is compared with the normal working condition, the deviation between the current working parameter and the normal working condition is evaluated, and the deviation data of the power generation equipment is obtained; S413: Based on the power generation equipment deviation data, the formula: ； Calculate the abnormal index of photovoltaic power generation equipment, compare it with the preset abnormal threshold, cut off the photovoltaic power generation equipment whose abnormal index exceeds the preset abnormal threshold, and implement abnormal notification to the staff to obtain the abnormal equipment cut-off result: in, Represents the abnormal index of photovoltaic power generation equipment, Represents the current Item operation parameters, Represents the standard Item operation parameters, It is The weight coefficient of the operating parameters, is the total number of parameter items.

5. The photovoltaic energy storage system dynamic optimization scheduling algorithm according to claim 4 is characterized in that: The steps for obtaining the abnormal situation response information are as follows: S421: Based on the abnormal equipment cut-off result and according to the number of abnormal photovoltaic power generation equipment, the estimated power generation loss of the abnormal photovoltaic power generation equipment is counted to obtain power generation loss data; S422: Based on the lost power generation data and the real-time discharge adjustment information, the formula: ； Calculate the corrected discharge power and obtain the information on dealing with abnormal conditions; in, is the correction factor, represents the corrected discharge power, represents the adjusted discharge power, represents the expected power generation loss, Represents the length of the target period.

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