A photovoltaic storage and charging integrated optimization control method

Abstract

The present invention discloses a method for optimizing and controlling the integration of light storage and charging, and relates to the technical field of photovoltaic power generation. In step S3, by setting different charging depths Scz, the system can test the battery charging and discharging state under different depths, and generate corresponding battery performance coefficients Dxxs. Combining historical data with current test data, the system can scientifically optimize the charging depth of energy storage equipment, and further avoid excessive use of batteries and extend the life of batteries by limiting deep charging and discharging behaviors, thereby ensuring that the system does not cause damage to battery health while operating at high efficiency. In S4 and S5, based on the constraint range of charging depth and photovoltaic power generation and electricity demand data, a balance evaluation index Pgzs between photovoltaic utilization rate Gfyz and battery health is constructed. By comparing and analyzing with a preset evaluation threshold Q, the system can further accurately judge the degree of balance between current photovoltaic utilization and battery health.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a photovoltaic storage and charging integrated optimization control method. Background Art

[0002] Photovoltaic storage and charging integration refers to improving the utilization rate of renewable energy by integrating photovoltaic power generation systems, energy storage systems and charging systems, especially in scenarios with large power demand such as industrial parks and commercial buildings. This method aims to reasonably dispatch the power generated by photovoltaic power generation and achieve optimal energy management through the charge and discharge control of energy storage equipment. However, there is an inevitable contradiction between maximizing photovoltaic utilization and optimizing battery life: photovoltaic power generation hopes to store excess electrical energy to improve utilization, but frequent charging and discharging of batteries and high depth of charging and discharging will shorten their life.

[0003] The core of this contradiction is that in order to improve the utilization rate of photovoltaic power generation, the system tends to charge and discharge frequently, which may shorten the life of the battery, especially in the case of excessive deep charging and discharging. In the process of pursuing the maximum utilization rate of photovoltaic power generation, the health management of the battery is often ignored, which will lead to the existing photovoltaic storage and charging system usually being difficult to remember to make a timely assessment of the balance between photovoltaic utilization and battery life, making it difficult to maintain the health of the battery while improving the utilization rate of photovoltaic power generation. Secondly, the shortcoming of the existing method is the lack of effective constraints on the depth of charge and discharge, resulting in insufficient refinement of battery life management, which further causes problems such as early degradation or reduced efficiency of energy storage equipment. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a photovoltaic storage and charging integrated optimization control method to solve the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a photovoltaic storage and charging integrated optimization control method, comprising the following steps:

[0006] S1. Monitor and obtain relevant power generation status data information of photovoltaic panels in the industrial park, and monitor relevant power demand data information of local industrial equipment and office buildings;

[0007] S2. Filter and correct the relevant power generation status data information and relevant power demand data information to remove noise and outliers, so as to construct a park power data set, and establish a photovoltaic storage and charging balance prediction model in combination with historical data;

[0008] S3. Preset different charging depth values ​​Scz to test the relevant charging and discharging state data information of the energy storage device under different charging depth values ​​Scz, and generate the battery performance coefficient Dxxs under the corresponding situation. At the same time, combine historical data to construct the historical battery performance coefficient Ldxs under different situations. Based on test analysis and historical analysis, optimize the constraint range of the charging depth of the energy storage device ;

[0009] S4: Based on the constraint range of the charging depth of the energy storage device obtained in step S3 , combined with relevant power generation status data information and relevant electricity demand data information, the photovoltaic utilization rate Gfyz is constructed, and the balance evaluation index Pgzs is obtained by fitting according to the trained photovoltaic storage and charging balance prediction model;

[0010] S5. Pre-set an evaluation threshold Q, and comprehensively judge the balance between current photovoltaic utilization and battery health by comparing and analyzing the balance evaluation index Pgzs with the evaluation threshold Q.

[0011] Preferably, S1 specifically comprises the following steps:

[0012] S11, pre-install photovoltaic panels in the industrial park, and monitor relevant power generation status data information of the photovoltaic panels in real time, wherein the relevant power generation status data information includes photovoltaic power generation in each monitoring period, and count the photovoltaic power generation in a fixed period to obtain the total photovoltaic power generation Zgfz in the fixed period;

[0013] S12. Real-time monitoring of relevant electricity demand data information of local industrial equipment and office buildings, wherein the relevant electricity demand data information includes the power consumed by each production equipment, heating equipment, cooling equipment and control equipment in the industrial park during each monitoring period, and the power consumed by each production equipment, heating equipment, cooling equipment and control equipment in the industrial park during each monitoring period is counted to obtain the local electricity consumption Bdhz within a fixed period.

[0014] Preferably, S2 specifically includes the following steps:

[0015] S21, preprocessing the relevant power generation status data information and relevant power demand data information obtained in step S1, the preprocessing includes noise removal, missing value filling and data smoothing operations, wherein a low-pass filter is used to remove the noise in the data to construct a park power data set, and the dimensionless processing technology is used to scale the preprocessed relevant data information so that its range falls within Inside;

[0016] S22. Construct a photovoltaic storage and charging balance prediction model based on convolutional neural network technology, and input relevant data information and historical data in the park power data set into the photovoltaic storage and charging balance prediction model.

[0017] Preferably, S3 specifically includes the following steps:

[0018] S31. Taking the upper and lower safety limits of the depth of charge of the energy storage device provided by the manufacturer as the preliminary constraint range, and randomly setting a number of different groups of depth of charge values ​​Scz within the preliminary constraint range, wherein the randomly set number of different depth of charge values ​​Scz include the upper and lower safety limits of the depth of charge of the energy storage device provided by the manufacturer, and testing the performance of the energy storage device under different depth of charge values ​​Scz, so as to respectively obtain relevant charge and discharge state data information of the energy storage device under different conditions, wherein the relevant charge and discharge state data information includes the energy storage charge amount Xncz, the depth of charge value Scz, the battery power percentage SOC of the energy storage device in each monitoring period, and the battery efficiency Dcx, the battery internal resistance value Dnzz, the discharge rate Fdps and the temperature value Wdz of the energy storage device under different depth of charge values ​​Scz;

[0019] S32, according to the relevant charging and discharging state data information of the energy storage device under different conditions in the historical data, to construct the historical battery performance coefficient Ldxs under different conditions, which is specifically obtained in the following manner:

[0020] ;

[0021] In the formula, Dnzz represents the internal resistance of the battery, Dcx represents the battery efficiency, Fdps represents the discharge rate, and Wdz represents the temperature value. , , and are all weight values, and C represents the first correction constant.

[0022] Preferably, S3 also includes:

[0023] S33. Pre-build exception range The specific method is as follows: sort the historical battery performance coefficients Ldxs of several groups under different conditions obtained in step S32 by numerical value to form a set of data columns, and determine the first quartile q1, the third quartile q2 and the interquartile range IQR by the interpolation method in quantile statistics;

[0024] ;

[0025] In the formula, Expressed as the maximum historical battery performance coefficient in the historical period, Expressed as the minimum historical battery performance coefficient in the historical period, It is expressed as the average historical battery performance coefficient during the historical period, where .

[0026] Preferably, S3 also includes:

[0027] S34, based on the calculation method of the historical battery performance coefficient Ldxs obtained in step S32, calculate the battery performance coefficient Dxxs under different charging depth values ​​Scz randomly set in step S31, and combine the abnormal range , filter out abnormal charging depth value Scz, the specific screening content is as follows:

[0028] If the battery performance coefficient Dxxs falls into the abnormal range When the battery performance coefficient Dxxs is within 100%, the depth of charge value Scz corresponding to the battery performance coefficient Dxxs will not be screened out;

[0029] If the battery performance coefficient Dxxs does not fall within the abnormal range When the battery performance coefficient Dxxs is within the range of 0, the depth of charge value Scz corresponding to the battery performance coefficient Dxxs will be screened out and marked.

[0030] Preferably, S3 also includes:

[0031] S341, collect statistics on the depth of charge values ​​Scz that are screened out, and use the marked depth of charge values ​​Scz as the center value to spread them around, so as to re-extract different depth of charge values ​​Scz, and repeat step S34 for the re-extracted depth of charge values ​​Scz to obtain the constraint range .

[0032] Preferably, S4 specifically comprises the following steps:

[0033] S41. Analyze whether the current energy storage equipment can accept charging operation. The specific analysis content is as follows:

[0034] S411. Obtain a discharge depth value Fcc according to the battery power percentage SOC of the energy storage device. The discharge depth value Fcc is obtained by the following formula: ;

[0035] S412: If the discharge depth value Fcc falls within the constraint range When the total photovoltaic power generation Zgfz obtained by the photovoltaic panels is used to charge the energy storage equipment;

[0036] S413: If the discharge depth value Fcc does not fall within the constraint range When the energy storage device is charged, the energy storage device will not be charged.

[0037] S42, based on the relevant power generation status data information and relevant power demand data information, combined with the constraint range , construct the photovoltaic utilization rate Gfyz, and the photovoltaic utilization rate Gfyz is obtained by the following formula:

[0038] ;

[0039] Where Xncz represents the energy storage charge, Bdhz represents the local electricity consumption, and Zgfz represents the total photovoltaic power generation.

[0040] Preferably, S4 also includes:

[0041] S43, by correlating the photovoltaic utilization rate Gfyz with the battery performance coefficient Dxxs, and after dimensionless processing, fitting to obtain the balance evaluation index Pgzs, the balance evaluation index Pgzs is obtained by the following formula:

[0042] ;

[0043] Where i=1, 2, 3, ..., n, n represents the monitoring period, It is expressed as the photovoltaic utilization rate in the i-th monitoring period, It is expressed as the average photovoltaic utilization rate during the monitoring period, It is expressed as the battery performance coefficient in the i-th monitoring period, It is expressed as the average battery performance coefficient during the monitoring period, V is expressed as the second correction constant, and All are weight values.

[0044] Preferably, S5 specifically includes the following steps:

[0045] S51, by comparing and analyzing the balance evaluation index Pgzs with the evaluation threshold Q, comprehensively judge the balance between the current photovoltaic utilization and the battery health, and the specific comparison and analysis content is as follows:

[0046] If the balance evaluation index Pgzs is greater than the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are not in a balanced state, and a first warning instruction will be issued;

[0047] If the balance evaluation index Pgzs is equal to the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are in a relatively balanced state, and a second warning instruction will be issued at this time;

[0048] If the balance evaluation index Pgzs is less than the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are not in a balanced state, and a third warning instruction will be issued;

[0049] S52: According to the No. 1 warning instruction, No. 2 warning instruction and No. 3 warning instruction issued in step S51, corresponding control measures are respectively adopted, and the specific contents are as follows:

[0050] If the No. 1 warning command is issued, the battery charge and discharge rate will be adjusted to 80% of the original charge and discharge rate to smooth the use of photovoltaic power generation, and local load consumption will be increased first to dispatch loads to consume excess photovoltaic power generation;

[0051] If the second warning command is issued, the existing control strategy will be maintained and the system status will continue to be monitored to maintain balance;

[0052] If a No. 3 warning instruction is issued, the control on battery charge and discharge depth will be relaxed. Specifically, the control content will be relaxed to limit the charge depth of energy storage equipment to Adjust to .

[0053] The present invention provides a photovoltaic storage and charging integrated optimization control method, which has the following beneficial effects:

[0054] (1) In step S3, by setting different charging depths Scz, the system can test the battery charging and discharging status under different depths and generate the corresponding battery performance coefficient Dxxs. Combining historical data with current test data, the system can scientifically optimize the charging depth of energy storage equipment, further avoid excessive use of batteries and extend the battery life by limiting the deep charging and discharging behavior, thereby ensuring that the system does not cause damage to battery health while operating at high efficiency. In S4 and S5, based on the constraint range of charging depth and photovoltaic power generation and electricity demand data, a balance evaluation index Pgzs between photovoltaic utilization rate Gfyz and battery health is constructed. By comparing and analyzing with the preset evaluation threshold Q, the system can further accurately judge the balance between current photovoltaic utilization and battery health. In this way, the control strategy can be automatically optimized in actual applications, which not only meets the energy needs of the industrial park, but also extends the battery life as much as possible, so as to further achieve the dual goals. In general, this method further optimizes the effective use of photovoltaic power and the extension of battery life through systematic optimization control, avoiding the traditional system that ignores battery protection in order to improve photovoltaic utilization, and the lack and untimely judgment of the balance between photovoltaic utilization and battery life.

[0055] (2) By initially constraining the range and setting several groups of different depth of charge values ​​Scz within the range, the performance of the energy storage device under different depths of charge can be tested in detail. This method can collect multiple key parameters of the energy storage device under different Scz values, thereby understanding the performance of the energy storage device under different charging and discharging conditions in multiple dimensions. This test method provides an important data basis for further optimizing the depth of charge management of the energy storage device. By testing and analyzing the performance of the energy storage device under different depths of charge Scz, the safe range of its depth of charge can be effectively evaluated. Combined with historical data, the constraint range of the depth of charge can be further optimized. , which can extend the service life of energy storage equipment as much as possible while ensuring safety during the charging and discharging process. This analysis method based on a combination of test and historical data provides a safer and more reliable charging depth management strategy for energy storage equipment under different operating conditions.

[0056] (3) By numerically sorting the historical battery performance coefficient Ldxs under different conditions and using the interpolation method in quantile statistics to determine the first quartile q1, the third quartile q2 and the interquartile range IQR, this method can scientifically construct the abnormal range. This method effectively takes into account the distribution of historical data to determine abnormal values ​​outside the normal range. This process ensures accurate analysis of historical data and helps to identify and eliminate possible abnormal situations, thereby improving the reliability of battery performance evaluation. And through screening operations, those charging depth values ​​that do not meet the normal performance range are effectively excluded, further ensuring that the selection in charging depth management is more accurate. After the screening operation, the marked charging depth value Scz is diffused as the central value, and different charging depth values ​​Scz are re-extracted. This method can adjust and optimize the constraint range of charging depth according to actual conditions. , by constraining the scope based on the preliminary constraint scope The optimization ensures that the final selected charging depth value Scz meets the performance requirements and safety standards of the energy storage equipment. This process effectively narrows the selection range of charging depth, thereby improving the safety and efficiency of the system.

[0057] (4) In S413, if the discharge depth value Fcc does not fall within the constraint range , the system will automatically stop charging. This mechanism effectively avoids potential safety hazards caused by charging depth not meeting equipment safety requirements, thereby improving the operational safety of energy storage equipment. By combining power generation status data information, power demand data information and charging constraint range, this method can maximize the use of photovoltaic power generation resources while meeting equipment safety requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1This is a block diagram of a photovoltaic storage and charging integrated optimization control method of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Example 1

[0061] See also Figure 1 The present invention provides a photovoltaic storage and charging integrated optimization control method, comprising the following steps:

[0062] S1. Monitor and obtain relevant power generation status data information of photovoltaic panels in the industrial park, and monitor relevant power demand data information of local industrial equipment and office buildings;

[0063] S2. Filter and correct the relevant power generation status data information and relevant power demand data information to remove noise and outliers, so as to construct a park power data set, and establish a photovoltaic storage and charging balance prediction model in combination with historical data;

[0064] S3. Preset different charging depth values ​​Scz to test the relevant charging and discharging state data information of the energy storage device under different charging depth values ​​Scz, and generate the battery performance coefficient Dxxs under the corresponding situation. At the same time, combine historical data to construct the historical battery performance coefficient Ldxs under different situations. Based on test analysis and historical analysis, optimize the constraint range of the charging depth of the energy storage device ;

[0065] S4: Based on the constraint range of the charging depth of the energy storage device obtained in step S3 , combined with relevant power generation status data information and relevant electricity demand data information, the photovoltaic utilization rate Gfyz is constructed, and the balance evaluation index Pgzs is obtained by fitting according to the trained photovoltaic storage and charging balance prediction model;

[0066] S5. Pre-set an evaluation threshold Q, and comprehensively judge the balance between current photovoltaic utilization and battery health by comparing and analyzing the balance evaluation index Pgzs with the evaluation threshold Q.

[0067] In this method, steps S1 and S2 monitor the power generation status of photovoltaic panels and the electricity demand in the industrial park in real time, and use filtering and correction technology to remove noise and outliers to build an accurate park power data set. This method effectively improves the accuracy and reliability of the data and lays a solid foundation for the construction of the subsequent photovoltaic storage and charging balance prediction model. In step S3, different charging depth values ​​Scz are pre-set and their impact on battery performance is tested. At the same time, historical data is combined for analysis, so that the method can evaluate the charging and discharging status of the energy storage equipment according to different historical and current charging depths, and optimize the constraint range of the charging depth by generating the battery performance coefficient Dxxs and the historical battery performance coefficient Ldxs. , thereby further extending the battery life and ensuring its long-term health. In step S4, by comprehensively considering the photovoltaic power generation status and electricity demand, the photovoltaic utilization rate Gfyz is constructed, and based on the trained light storage and charging balance prediction model, the balance evaluation index Pgzs is fitted to obtain the balance evaluation index Pgzs. This process effectively estimates the contradiction between photovoltaic utilization and battery life, so that the system can further achieve higher energy utilization in practical applications, while ensuring the health of energy storage equipment. Finally, step S5 sets the evaluation threshold Q, compares and analyzes the balance evaluation index Pgzs with the threshold, judges the balance between photovoltaic utilization and battery health, and takes corresponding optimization measures according to the comparison results. This dynamic adjustment mechanism further ensures that the system can make intelligent responses according to actual conditions, thereby improving the efficiency and reliability of the overall energy system.

[0068] It should be noted that the goal of maximizing photovoltaic utilization is to use photovoltaic power generation for local loads or energy storage as much as possible without feeding electricity back to the grid or wasting it. However, this goal conflicts with the optimization of battery life, which is mainly reflected in the following aspects:

[0069] Frequent charging and discharging leads to battery degradation: In order to maximize photovoltaic utilization, the system may frequently charge and discharge the battery. However, the life of the battery is usually limited by the number of charge and discharge cycles. Too frequent charging and discharging will accelerate the battery degradation and shorten its life.

[0070] Battery charge and discharge depth: In order to maximize the utilization of photovoltaic power, the system may charge deeply, that is, fill the battery as much as possible. However, deep charging (close to full charge) and deep discharging (close to low charge) may increase battery loss and be detrimental to battery life.

[0071] Example 2

[0072] Please refer to Figure 1 Specifically: S1 specifically includes the following steps:

[0073] S11, pre-install photovoltaic panels in the industrial park, and monitor relevant power generation status data information of the photovoltaic panels in real time, wherein the relevant power generation status data information includes photovoltaic power generation in each monitoring period, and count the photovoltaic power generation in a fixed period to obtain the total photovoltaic power generation Zgfz in the fixed period;

[0074] S12. Real-time monitoring of the relevant power demand data information of local industrial equipment and office buildings, wherein the relevant power demand data information includes the power consumed by each production equipment, heating equipment, cooling equipment and control equipment in the industrial park during each monitoring period, and the power consumed by each production equipment, heating equipment, cooling equipment and control equipment in the industrial park during each monitoring period is counted to obtain the local power consumption Bdhz within a fixed period. The fixed period can be one week or one month, etc.;

[0075] S2 specifically includes the following steps:

[0076] S21, preprocessing the relevant power generation status data information and relevant power demand data information obtained in step S1, the preprocessing includes noise removal, missing value filling and data smoothing operations, wherein a low-pass filter is used to remove the noise in the data to construct a park power data set, and the dimensionless processing technology is used to scale the preprocessed relevant data information so that its range falls within Inside;

[0077] S22. Construct a photovoltaic storage and charging balance prediction model based on convolutional neural network technology, and input relevant data information and historical data in the park power data set into the photovoltaic storage and charging balance prediction model.

[0078] It should be noted that: the original model is first constructed using convolutional neural network technology, and the original model is trained and tested with relevant power generation status data information, relevant electricity demand data information, relevant charging and discharging status data information and historical data, and the trained original model is used as a state recognition model, and the feature information in the state recognition model is obtained respectively, and the state recognition model is trained and tested with the obtained feature information, and the trained state recognition model is used as a light-storage-charging balance prediction model.

[0079] In this embodiment, in steps S11 and S12, by pre-installing photovoltaic panels in the industrial park and monitoring the power demand of industrial equipment and office buildings in real time, the system can accurately collect the photovoltaic power generation and local power consumption in each monitoring period. The statistics of the total photovoltaic power generation Zgfz and the local power consumption Bdhz in a fixed period further ensure the comprehensiveness and accuracy of the data, and provide reliable data support for subsequent energy efficiency analysis. In step S21, the system pre-processes the collected data in many aspects. By using low-pass filters to remove noise, fill missing values, and smooth data, the system improves the quality and stability of the data. At the same time, the introduction of dimensionless processing technology enables data of different dimensions to be effectively compared and analyzed, ensuring that the data can be uniformly processed in subsequent models. This process further improves the accuracy of data analysis and reduces the interference caused by data anomalies. In S22, the system constructs a light storage and charging balance prediction model through convolutional neural network technology, and inputs the pre-processed data into the model. The convolutional neural network can automatically extract key features from complex power data, thereby improving the accuracy and robustness of the prediction model. By combining historical data, the system can make intelligent predictions about future photovoltaic power generation and electricity demand, further ensuring that the park's power system can better balance photovoltaic power generation and electricity demand. In general, this method makes full preparations for improving the energy efficiency of industrial parks and achieving an effective balance between photovoltaic power and battery health through accurate data collection, intelligent processing and scientific application of prediction models.

[0080] Example 3

[0081] Please refer to Figure 1 Specifically: S3 specifically includes the following steps:

[0082] S31. Taking the upper and lower safety limits of the depth of charge of the energy storage device provided by the manufacturer as the preliminary constraint range, and randomly setting a number of different groups of depth of charge values ​​Scz within the preliminary constraint range, wherein the randomly set number of different depth of charge values ​​Scz include the upper and lower safety limits of the depth of charge of the energy storage device provided by the manufacturer, and testing the performance of the energy storage device under different depth of charge values ​​Scz, so as to respectively obtain relevant charge and discharge state data information of the energy storage device under different conditions, wherein the relevant charge and discharge state data information includes the energy storage charge amount Xncz, the depth of charge value Scz, the battery power percentage SOC of the energy storage device in each monitoring period, and the battery efficiency Dcx, the battery internal resistance value Dnzz, the discharge rate Fdps and the temperature value Wdz of the energy storage device under different depth of charge values ​​Scz;

[0083] S32, according to the relevant charging and discharging state data information of the energy storage device under different conditions in the historical data, to construct the historical battery performance coefficient Ldxs under different conditions, which is specifically obtained in the following manner:

[0084] ;

[0085] In the formula, Dnzz represents the internal resistance of the battery, Dcx represents the battery efficiency, Fdps represents the discharge rate, and Wdz represents the temperature value. , , and are weight values, where 0 < ≤1,0< ≤1,0< ≤1,0< ≤1, and + + + =1, C is represented as the first correction constant.

[0086] The above-mentioned battery internal resistance value Dnzz refers to the resistance of the battery to the flow of current during the charge and discharge process. The internal resistance of the battery increases with the increase of the depth of charge and discharge. Deep discharge will cause chemical reactions inside the battery and aging of materials, increase the internal resistance. High internal resistance means that the battery will have more energy loss during the charge and discharge process, reduce the overall efficiency, accelerate the aging of the battery, and reduce its service life. The DC internal resistance test can be used to measure the voltage drop and current of the battery during the charge and discharge process to calculate the internal resistance.

[0087] Battery efficiency Dcx refers to the energy conversion efficiency of the battery during the charging and discharging process, usually expressed as a percentage. Battery efficiency may fluctuate with changes in charge and discharge depth and rate. Deeper discharge and high charge and discharge rates increase internal resistance, resulting in increased energy loss, which reduces battery efficiency, accelerates battery aging, and reduces its service life. A dedicated efficiency tester can be used to measure the energy conversion efficiency during the charging and discharging process.

[0088] The discharge rate Fdps is a measure of the ratio of the current size to the battery capacity during the discharge process. The higher the discharge rate, the greater the current, resulting in more obvious power loss and heat generation, which in turn affects the battery life. A power sensor or BMS can be used to monitor the real-time discharge current of the battery. By recording the change in current, the discharge rate of the battery can be calculated. The specific calculation formula for the discharge rate of the battery is: ; Wherein, I represents the discharge current of the battery; Expressed as the nominal capacity of the battery, the nominal capacity of the battery It refers to the total amount of electricity that a battery can output under standard test conditions. It represents the total amount of electricity that a battery can provide at a specified discharge current after being fully charged.

[0089] The temperature value Wdz can be monitored by thermocouples or thermistors to monitor the temperature changes of the energy storage equipment.

[0090] In this embodiment, by implementing step S31, several groups of different depth of charge values ​​Scz are randomly set, and the test is performed within the safety upper limit and safety lower limit range provided by the manufacturer. The charge and discharge state of the energy storage device under different depth of charge values ​​Scz can be analyzed in all aspects, and the working state and efficiency of the energy storage device are reflected in multiple dimensions. This test method can identify the relatively optimal working point of the energy storage device under different depths of charge, which helps to extend the battery life and improve the charge and discharge efficiency. Step S32 constructs the historical battery performance coefficient Ldxs under different conditions through analysis based on historical data. The performance coefficient integrates the key performance parameters of multiple dimensions such as the internal resistance, battery efficiency, discharge rate, temperature, etc. of the battery, and performs weighted calculation according to the pre-set weight value, which can reflect the comprehensive performance of the energy storage device under different depths of charge. This method combines historical data with actual test data, so that the optimization decision is not only based on current data, but also can draw experience from historical data, thereby improving the system's accurate evaluation of battery performance. By introducing weight values ​​and correction constants, the invention can dynamically adjust different battery performance parameters according to actual application scenarios. This technology ensures that when comprehensively evaluating the performance of energy storage equipment, the importance of each performance indicator can be adjusted according to specific needs, further improving the flexibility and adaptability of charge and discharge control. Through the combined analysis of the above-mentioned tests and historical data, this method can further accurately identify the relatively optimal charge and discharge state of the energy storage equipment under the constraints of different charging depths, thereby ensuring that the battery operates efficiently within a safe range, in preparation for extending battery life and reducing damage caused by overcharging or deep discharge. This optimization control not only improves the utilization rate of the photovoltaic system, but also reduces the loss of energy storage equipment, so that the health of the battery is effectively guaranteed. In general, this method enables more refined control of the photovoltaic storage system during operation by introducing tests of different charging depths and performance evaluation based on historical data.

[0091] Example 4

[0092] Please refer to Figure 1 , specifically: S3 also includes:

[0093] S33. Pre-build exception range The specific method is as follows: sort the values ​​of the historical battery performance coefficients Ldxs under different conditions obtained in step S32 by numerical value, and the sorting method is: sort the values ​​from small to large in order to form a set of data columns, and determine the first quartile q1, the third quartile q2 and the interquartile range IQR by the interpolation method in quantile statistics;

[0094] ;

[0095] In the formula, Expressed as the maximum historical battery performance coefficient in the historical period, Expressed as the minimum historical battery performance coefficient in the historical period, It is expressed as the average historical battery performance coefficient during the historical period, where .

[0096] S3 also includes:

[0097] S34, based on the calculation method of obtaining the historical battery performance coefficient Ldxs in step S32, calculating the battery performance coefficient Dxxs under different charging depth values ​​Scz randomly set in step S31;

[0098] The battery performance coefficient Dxxs under randomly set different charging depth values ​​Scz is calculated in the same way as the historical battery performance coefficient Ldxs, and combined with the abnormal range , filter out abnormal charging depth value Scz, the specific screening content is as follows:

[0099] If the battery performance coefficient Dxxs falls into the abnormal range When the battery performance coefficient Dxxs is within 100%, the depth of charge value Scz corresponding to the battery performance coefficient Dxxs will not be screened out;

[0100] If the battery performance coefficient Dxxs does not fall within the abnormal range When the battery performance coefficient Dxxs is within the range of 0, the depth of charge value Scz corresponding to the battery performance coefficient Dxxs will be screened out and marked.

[0101] S3 also includes:

[0102] S341, collect statistics on the depth of charge values ​​Scz that are screened out, and use the marked depth of charge values ​​Scz as the center value to spread them around, so as to re-extract different depth of charge values ​​Scz, and repeat step S34 for the re-extracted depth of charge values ​​Scz to obtain the constraint range .

[0103] In this embodiment, by sorting and quartile analysis of the historical battery performance coefficient Ldxs under different conditions in step S33, the first quartile q1, the third quartile q2 and the interquartile range IQR can be accurately determined. This process can clearly define the abnormal range. , and effectively identify outliers. The marking and screening of outliers helps to eliminate performance data that show obvious abnormalities at different charging depth values ​​Scz, thereby ensuring that the data used in the optimization process is more reliable and consistent. Step S34 is to calculate the battery performance coefficient Dxxs consistent with the historical performance coefficient Ldxs, and combine the abnormal range Screening the depth of charge value Scz, this method ensures that the evaluation of battery performance at different depths of charge is highly consistent and accurate. By marking and screening out the depth of charge values ​​that do not meet the abnormal range, potential unstable factors are further eliminated, which helps to optimize the selection of the depth of charge and thus extend the battery life. In step S341, by counting the marked abnormal depth of charge values ​​Scz and spreading them around as the central value, different depth of charge values ​​are re-extracted. This dynamic adjustment method can flexibly adjust the constraint range in actual operation and adapt to changes in different environments and operating conditions. This method not only improves the adaptability of the optimization process, but also can more accurately find a relatively good range of depth of charge values ​​and optimize the performance of energy storage equipment. Combined with the abnormal range The screening and dynamic adjustment can manage the charging depth more accurately. This refined management method can effectively avoid the impact of abnormal data, thereby extending the battery life and improving the efficiency of the overall system. Ultimately, through this optimization control method, the normal operation of the photovoltaic storage and charging system can be further realized, thereby improving battery performance and photovoltaic utilization. In summary, by establishing an abnormal range, screening abnormal charging depth values, and dynamically adjusting the constraint range, the accuracy and adaptability of charging depth management in the photovoltaic storage and charging system are effectively improved. This optimization control method not only improves the stability and reliability of battery performance, but also better realizes the long-term operation of the system and enhances the comprehensive benefits of photovoltaic and energy storage equipment.

[0104] Example 5

[0105] Please refer to Figure 1 Specifically: S4 specifically includes the following steps:

[0106] S41. Analyze whether the current energy storage equipment can accept charging operation. The specific analysis content is as follows:

[0107] S411. Obtain a discharge depth value Fcc according to the battery power percentage SOC of the energy storage device. The discharge depth value Fcc is obtained by the following formula: ;

[0108] S412: If the discharge depth value Fcc falls within the constraint range When the total photovoltaic power generation Zgfz obtained by the photovoltaic panels is used to charge the energy storage equipment;

[0109] The battery capacity percentage SOC mentioned above is usually expressed as the remaining power percentage of the battery relative to its maximum capacity. The battery capacity percentage SOC reflects the energy currently stored in the battery and is related to the capacity of the battery. It usually ranges from 0% to 100%.

[0110] The discharge depth value Fcc is the percentage of energy that the battery has released. It describes the degree of discharge of the battery. The discharge depth value Fcc and the battery power percentage SOC are complementary concepts: the larger the battery discharge depth value Fcc, the lower the battery power percentage SOC; and vice versa.

[0111] S413: If the discharge depth value Fcc does not fall within the constraint range When the energy storage device is charged, the energy storage device will not be charged.

[0112] S42, based on the relevant power generation status data information and relevant power demand data information, combined with the constraint range , construct the photovoltaic utilization rate Gfyz, and the photovoltaic utilization rate Gfyz is obtained by the following formula:

[0113] ;

[0114] In the formula, Xncz represents the energy storage charge, Bdhz represents the local electricity consumption, and Zgfz represents the total photovoltaic power generation; among them, the value of the energy storage charge Xncz is equal to zero.

[0115] The photovoltaic utilization rate Gfyz measures whether photovoltaic power generation can be fully utilized locally instead of being wasted, such as being sent back to the grid through an inverter or not being stored;

[0116] The energy storage charge Xncz mentioned above refers to the part of photovoltaic power generation used to charge the energy storage device;

[0117] Local electricity consumption Bdhz refers to the portion of electricity generated by photovoltaic power directly used for local load consumption;

[0118] Among them, the energy storage charging amount Xncz and the local electricity consumption Bdhz can be monitored and obtained through the electric energy meter;

[0119] The total photovoltaic power generation Zgfz can be monitored and obtained through photovoltaic inverters or photovoltaic meters;

[0120] In this embodiment, the current battery power percentage SOC of the energy storage device is analyzed to obtain the discharge depth value Fcc, and the value is compared with the preset constraint range. By comparison, this method can intelligently decide whether to perform charging operation. If the discharge depth value Fcc falls within the constraint range Within the range, the system will use the total photovoltaic power generation Zgfz obtained by the photovoltaic panel to charge the energy storage device; if it does not fall within the range, the charging will be suspended, and the charging amount can be controlled to prevent the possibility of overcharging. This decision-making process based on real-time data ensures that the timing of the charging operation meets the safe operation requirements of the energy storage device, effectively avoiding overcharging or potential damage to the device. By constructing the photovoltaic utilization rate Gfyz, combined with the relevant power generation status data information and power demand data information, the system can comprehensively evaluate the effective utilization of photovoltaic power generation. The present invention ensures that the charging operation is only carried out within the safe range through intelligent control of the charging operation of the energy storage device, thereby effectively protecting the health and life of the battery and avoiding unnecessary charging operations. This measure helps to extend the service life of the energy storage device and reduce maintenance costs and potential failure risks. By clarifying the constraint range and real-time monitoring of photovoltaic power generation, the system can dynamically adjust the charging strategy according to the actual power generation and power demand. This method enhances the stability and adaptability of the system, so that the integrated light storage and charging method can effectively respond to different power generation and power consumption conditions, and ensure the reliability and continuity of power supply. In summary, this method further improves the operating efficiency and stability of the system by intelligently analyzing the charging needs of energy storage equipment, optimizing the utilization of photovoltaic resources, and adopting a data-driven charging decision-making method. At the same time, this method effectively maintains the health of the battery, extends the service life of the equipment, and provides a reliable energy management solution for industrial parks.

[0121] Example 6

[0122] Please refer to Figure 1 Specifically, S4 also includes:

[0123] S43, by correlating the photovoltaic utilization rate Gfyz with the battery performance coefficient Dxxs, and after dimensionless processing, fitting to obtain the balance evaluation index Pgzs, the balance evaluation index Pgzs is obtained by the following formula:

[0124] ;

[0125] Where i=1, 2, 3, ..., n, n represents the monitoring period, It is expressed as the photovoltaic utilization rate in the i-th monitoring period, It is expressed as the average photovoltaic utilization rate during the monitoring period, It is expressed as the battery performance coefficient in the i-th monitoring period, It is expressed as the average battery performance coefficient during the monitoring period, V is expressed as the second correction constant, and are weight values, where 0 < ≤1,0< ≤1, and + =1.

[0126] S5 specifically includes the following steps:

[0127] S51, by comparing and analyzing the balance evaluation index Pgzs with the evaluation threshold Q, comprehensively judge the balance between the current photovoltaic utilization and the battery health, and the specific comparison and analysis content is as follows:

[0128] If the balance evaluation index Pgzs is greater than the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are not in a balanced state, which means that most of the energy generated by photovoltaic power is directly supplied to the load or the battery is fully charged, thereby achieving a higher utilization rate. However, this may put a greater pressure on the battery, especially in the case of frequent deep discharge or charging; at this time, a No. 1 warning instruction will be issued;

[0129] If the balance evaluation index Pgzs is equal to the evaluation threshold Q, it is judged that the current photovoltaic utilization and battery health are in a relatively balanced state, which means that the photovoltaic power generation in the system is reasonably utilized, the battery is in a suitable charging and discharging state, and the life span is optimized; at this time, a second warning instruction will be issued;

[0130] If the balance evaluation index Pgzs is less than the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are not in a balanced state. This indicates that the photovoltaic utilization rate of the current system is low, that is, the energy of photovoltaic power generation is not fully utilized. This may mean that the photovoltaic power generation is not effectively used for local loads or battery charging, or the battery is in an overly conservative state and cannot fully utilize its charging capacity. At this time, a No. 3 warning instruction will be issued;

[0131] S52: According to the No. 1 warning instruction, No. 2 warning instruction and No. 3 warning instruction issued in step S51, corresponding control measures are respectively adopted, and the specific contents are as follows:

[0132] If the No. 1 warning command is issued, the battery charge and discharge rate will be adjusted to 80% of the original charge and discharge rate, reducing the rapid charge and discharge rate of the battery and the battery depreciation rate, so as to smooth the use of photovoltaic power generation and avoid excessive battery work. Local load consumption will be increased first to dispatch loads to consume excess photovoltaic power generation, such as electric vehicle charging, air conditioning equipment, etc., to avoid overcharging of the battery.

[0133] If the second warning command is issued, the existing control strategy will be maintained and the system status will continue to be monitored to maintain balance;

[0134] If a No. 3 warning instruction is issued, the control on battery charge and discharge depth will be relaxed. Specifically, the control content will be relaxed to limit the charge depth of energy storage equipment to Adjust to , allowing the battery to work more fully within a safe range, thereby improving the overall utilization rate of photovoltaic power generation, especially when the load demand is insufficient, more photovoltaic power can enter the energy storage system. At the same time, when photovoltaic power generation is in surplus, the power consumption of the external power grid is reduced, giving priority to the use of photovoltaic power generation.

[0135] In this embodiment, by calculating the balance evaluation index Pgzs in S43, combined with the photovoltaic utilization rate Gfyz and the battery performance coefficient Dxxs, the dimensionless balance evaluation index Pgzs can accurately reflect the balance between photovoltaic utilization and battery health. This method can comprehensively evaluate the operating status of the current system, timely discover and adjust the imbalance between photovoltaic utilization and battery health, thereby improving the overall efficiency of the system. The early warning mechanism in S51 provides intelligent monitoring of photovoltaic utilization and battery health. And according to the No. 1, No. 2 or No. 3 early warning instructions issued, corresponding control measures are taken. This mechanism can effectively identify and respond to potential problems in the system and ensure that the system always maintains relatively good performance under different operating conditions. When the No. 1 early warning instruction is issued, by reducing the battery charge and discharge rate to 80% of the original, the rapid charge and discharge pressure of the battery is reduced, and the battery is avoided from overworking and overcharging, thereby extending the battery life. At the same time, giving priority to increasing local load consumption, such as electric vehicle charging and air conditioning equipment, can effectively utilize the excess photovoltaic power generation and avoid energy waste. When the No. 3 warning command is issued, by relaxing the control of the battery charge and discharge depth, the constraint range of the energy storage device charge depth is adjusted to work more fully, thereby improving the overall utilization rate of photovoltaic power generation. This measure allows more photovoltaic power to enter the energy storage system when the load demand is insufficient, reducing dependence on the external power grid and maximizing the utilization of resources. This method can flexibly adjust the charge and discharge strategy according to the actual photovoltaic power generation and power demand to adapt to different operating conditions. This flexibility improves the adaptability and stability of the system and ensures that the system can operate effectively under various circumstances. In summary, this method provides an effective integrated optimization control method for light storage and charging through precise balance assessment, intelligent early warning mechanism, optimized battery use and photovoltaic power generation, balanced operation, improved photovoltaic utilization efficiency and enhanced system adaptability. This method not only improves the utilization efficiency of photovoltaic power generation and energy storage equipment, but also extends the service life of the battery and improves the operating efficiency and reliability of the entire system.

[0136] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A photovoltaic storage and charging integrated optimization control method, characterized in that: The following steps are included: S1. Monitor the power generation status data of photovoltaic panels in the industrial park, and monitor the power demand data of local industrial equipment and office buildings; S2. Filter and correct the relevant power generation status data information and relevant power demand data information to remove noise and outliers, so as to construct a park power data set, and establish a photovoltaic storage and charging balance prediction model in combination with historical data; S3. Preset different charging depth values ​​Scz to test the relevant charging and discharging state data information of the energy storage device under different charging depth values ​​Scz, and generate the battery performance coefficient Dxxs under the corresponding situation. At the same time, combine historical data to construct the historical battery performance coefficient Ldxs under different situations. Based on test analysis and historical analysis, optimize the constraint range of the charging depth of the energy storage device ; S3 specifically includes the following steps: S31. Taking the upper and lower safety limits of the depth of charge of the energy storage device provided by the manufacturer as the preliminary constraint range, and randomly setting a number of different groups of depth of charge values ​​Scz within the preliminary constraint range, wherein the randomly set number of different depth of charge values ​​Scz include the upper and lower safety limits of the depth of charge of the energy storage device provided by the manufacturer, and testing the performance of the energy storage device under different depth of charge values ​​Scz, so as to respectively obtain relevant charge and discharge state data information of the energy storage device under different conditions, wherein the relevant charge and discharge state data information includes the energy storage charge amount Xncz, the depth of charge value Scz, the battery power percentage SOC of the energy storage device in each monitoring period, and the battery efficiency Dcx, the battery internal resistance value Dnzz, the discharge rate Fdps and the temperature value Wdz of the energy storage device under different depth of charge values ​​Scz; S32, according to the relevant charging and discharging state data information of the energy storage device under different conditions in the historical data, to construct the historical battery performance coefficient Ldxs under different conditions, which is specifically obtained in the following manner: ； In the formula, Dnzz represents the internal resistance of the battery, Dcx represents the battery efficiency, Fdps represents the discharge rate, and Wdz represents the temperature value. , , and They are all weight values, and C represents the first correction constant; S3 also includes: S33. Pre-build exception range The specific method is as follows: sort the historical battery performance coefficients Ldxs of several groups under different conditions obtained in step S32 by numerical value to form a set of data columns, and determine the first quartile q1, the third quartile q2 and the interquartile range IQR by the interpolation method in quantile statistics; ； In the formula, Expressed as the maximum historical battery performance coefficient in the historical period, Expressed as the minimum historical battery performance coefficient in the historical period, It is expressed as the average historical battery performance coefficient during the historical period, where ; S3 also includes: S34, based on the calculation method of the historical battery performance coefficient Ldxs obtained in step S32, calculate the battery performance coefficient Dxxs under different charging depth values ​​Scz randomly set in step S31, and combine the abnormal range , filter out abnormal charging depth value Scz, the specific screening content is as follows: If the battery performance coefficient Dxxs falls into the abnormal range When the battery performance coefficient Dxxs is within 100%, the depth of charge value Scz corresponding to the battery performance coefficient Dxxs will not be screened out; If the battery performance coefficient Dxxs does not fall within the abnormal range When the battery performance coefficient Dxxs is within 100, the depth of charge value Scz corresponding to the battery performance coefficient Dxxs will be screened out and marked; S3 also includes: S341, collect statistics on the depth of charge values ​​Scz that are screened out, and use the marked depth of charge values ​​Scz as the center value to spread them around, so as to re-extract different depth of charge values ​​Scz, and repeat step S34 for the re-extracted depth of charge values ​​Scz to obtain the constraint range ; S4: Based on the constraint range of the charging depth of the energy storage device obtained in step S3 , combined with relevant power generation status data information and relevant electricity demand data information, the photovoltaic utilization rate Gfyz is constructed, and the balance evaluation index Pgzs is obtained by fitting according to the trained photovoltaic storage and charging balance prediction model; S4 specifically includes the following steps: S41. Analyze whether the current energy storage equipment can accept charging operation. The specific analysis content is as follows: S411. Obtain a discharge depth value Fcc according to the battery power percentage SOC of the energy storage device. The discharge depth value Fcc is obtained by the following formula: ; S412: If the discharge depth value Fcc falls within the constraint range When the total photovoltaic power generation Zgfz obtained by the photovoltaic panels is used to charge the energy storage equipment; S413: If the discharge depth value Fcc does not fall within the constraint range When the energy storage device is charged, the energy storage device will not be charged. S42, based on the relevant power generation status data information and relevant power demand data information, combined with the constraint range , construct the photovoltaic utilization rate Gfyz, and the photovoltaic utilization rate Gfyz is obtained by the following formula: ； In the formula, Xncz represents the energy storage charge, Bdhz represents the local electricity consumption, and Zgfz represents the total photovoltaic power generation; S4 also includes: S43, by correlating the photovoltaic utilization rate Gfyz with the battery performance coefficient Dxxs, and after dimensionless processing, fitting to obtain the balance evaluation index Pgzs, the balance evaluation index Pgzs is obtained by the following formula: ； Where i=1, 2, 3, ..., n, n represents the monitoring period, It is expressed as the photovoltaic utilization rate in the i-th monitoring period, It is expressed as the average photovoltaic utilization rate during the monitoring period, It is expressed as the battery performance coefficient in the i-th monitoring period, It is expressed as the average battery performance coefficient during the monitoring period, V is expressed as the second correction constant, and All are weight values; S5. Pre-set an evaluation threshold Q, and comprehensively judge the balance between current photovoltaic utilization and battery health by comparing and analyzing the balance evaluation index Pgzs with the evaluation threshold Q.

2. The photovoltaic storage and charging integrated optimization control method according to claim 1, characterized in that: S1 specifically includes the following steps: S11, pre-install photovoltaic panels in the industrial park, and monitor relevant power generation status data information of the photovoltaic panels in real time, wherein the relevant power generation status data information includes photovoltaic power generation in each monitoring period, and count the photovoltaic power generation in a fixed period to obtain the total photovoltaic power generation Zgfz in the fixed period; S12. Real-time monitoring of relevant electricity demand data information of local industrial equipment and office buildings, wherein the relevant electricity demand data information includes the power consumed by each production equipment, heating equipment, cooling equipment and control equipment in the industrial park during each monitoring period, and the power consumed by each production equipment, heating equipment, cooling equipment and control equipment in the industrial park during each monitoring period is counted to obtain the local electricity consumption Bdhz within a fixed period.

3. The photovoltaic storage and charging integrated optimization control method according to claim 1 is characterized in that: S2 specifically includes the following steps: S21, preprocessing the relevant power generation status data information and relevant power demand data information obtained in step S1, the preprocessing includes noise removal, missing value filling and data smoothing operations, wherein a low-pass filter is used to remove the noise in the data to construct a park power data set, and the dimensionless processing technology is used to scale the preprocessed relevant data information so that its range falls within Inside; S22. Construct a photovoltaic storage and charging balance prediction model based on convolutional neural network technology, and input relevant data information and historical data in the park power data set into the photovoltaic storage and charging balance prediction model.

4. The photovoltaic storage and charging integrated optimization control method according to claim 1 is characterized in that: S5 specifically includes the following steps: S51. After comparing and analyzing the balance evaluation index Pgzs with the evaluation threshold Q, a comprehensive judgment is made on the balance between current photovoltaic utilization and battery health. The specific comparison and analysis contents are as follows: If the balance evaluation index Pgzs is greater than the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are not in a balanced state, and a first warning instruction will be issued; If the balance evaluation index Pgzs is equal to the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are in a relatively balanced state, and a second warning instruction will be issued at this time; If the balance evaluation index Pgzs is less than the evaluation threshold Q, it is determined that the current photovoltaic utilization and battery health are not in a balanced state, and a third warning instruction will be issued; S52: According to the No. 1 warning instruction, No. 2 warning instruction and No. 3 warning instruction issued in step S51, corresponding control measures are respectively adopted, and the specific contents are as follows: If the No. 1 warning command is issued, the battery charge and discharge rate will be adjusted to 80% of the original charge and discharge rate to smooth the use of photovoltaic power generation, and local load consumption will be increased first to dispatch loads to consume excess photovoltaic power generation; If the second warning command is issued, the existing control strategy will be maintained and the system status will continue to be monitored to maintain balance; If a No. 3 warning instruction is issued, the control on battery charge and discharge depth will be relaxed. Specifically, the control content will be relaxed to limit the charge depth of energy storage equipment to Adjust to .

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