An independent electrochemical energy storage integrated management system

Abstract

The present invention relates to the field of power storage management technology, specifically to an independent electrochemical energy storage integrated management system, the system includes a load forecasting and analysis module, a charge and discharge response control module, a dynamic load threshold adjustment module, an energy storage response optimization module, a PID adjustment module, and an abnormal state monitoring module. In the present invention, by adjusting the load change rate and fluctuation amplitude in real time, the power demand change is accurately predicted, the system stability and response speed are improved, the load fluctuation cycle and mutation value are analyzed, the demand trend is accurately predicted, the charge and discharge rate is adjusted, the actual demand is matched, the energy utilization efficiency is improved, the equipment life is extended, the response threshold is dynamically adjusted, and the operation time interval is optimized, so that the system maintains the best state under variable loads, reduces energy consumption and operation and maintenance costs, monitors power output and equipment status in real time, handles abnormalities in a timely manner, ensures system safety and stability, and significantly improves the overall efficiency and reliability of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage management, and in particular to an independent electrochemical energy storage integrated management system. Background Art

[0002] The field of power energy storage management technology involves the effective monitoring, dispatching and optimization management of energy storage equipment in the power system, aiming to achieve stable operation of the power system and efficient energy utilization. This field covers a number of technologies such as charge and discharge control of energy storage systems, energy dispatching, power management, and health monitoring of energy storage devices. By real-time evaluation and adjustment of the status and performance of energy storage devices, the balance of the power system and the reliable transmission of electric energy are ensured. Power energy storage management technology also focuses on optimizing system energy efficiency, extending the life of energy storage devices, and realizing automatic switching between energy storage and discharge under multiple power supply modes to meet the needs of various power loads.

[0003] Among them, the independent electrochemical energy storage comprehensive management system is an independent management system for electrochemical energy storage devices. It has the functions of real-time monitoring, status assessment and safety assurance of energy storage devices. The purpose of this system is to improve the response speed and stability of the power system, and to achieve efficient energy utilization and balanced regulation by independently monitoring and managing the charging and discharging process of the energy storage device. It is especially suitable for power scenarios that require fast dynamic response.

[0004] Existing technologies are obviously insufficient in dealing with large-scale or rapidly changing electricity demand. Existing systems rely on traditional monitoring and response mechanisms, which are slow to respond to real-time data processing and dynamic adjustments and are insufficient to cope with highly volatile or sudden load changes. For example, traditional systems are unable to adjust the charging and discharging rates of energy storage devices in a timely manner when encountering rapid load increases and decreases, resulting in energy waste or insufficient power supply. The lack of an effective abnormal monitoring mechanism causes the equipment to accumulate losses during long-term operation. Failure to promptly discover and repair problems leads to reduced system efficiency and potential safety risks, resulting in unstable power supply in the high-demand and highly volatile modern power market, affecting the economy and reliability of the overall power grid. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an independent electrochemical energy storage comprehensive management system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an independent electrochemical energy storage integrated management system comprises:

[0007] The load forecasting and analysis module is based on the original data of energy storage load, extracts the load value of different time periods, calculates the change rate of each time period, analyzes the fluctuation cycle, detects the mutation value, records the change characteristics, and analyzes the load change trend during the battery charging and discharging process in combination with the full and full discharge states. In the full and full discharge periods, it identifies and adjusts the battery load fluctuations to obtain the load change trend data;

[0008] The charge and discharge response control module determines the difference between the charging rate and the load based on the load change trend data, adjusts the rate parameters to match the fluctuation, and obtains the charge and discharge dynamic control data;

[0009] The dynamic load threshold adjustment module extracts the load fluctuation characteristic value based on the charge and discharge dynamic control data, calculates the energy storage response threshold, determines the trigger condition, updates the start and stop conditions, and obtains the load threshold instruction;

[0010] The energy storage response optimization module analyzes the energy storage response time based on the load threshold instruction, optimizes the response rate, adjusts the time interval of the energy storage operation, and obtains the energy storage operation time interval data;

[0011] The PID adjustment module extracts the power output and load demand values ​​based on the energy storage operation time interval data, determines the difference between the current power and the demand, and adjusts the proportional, integral and differential parameters to generate a power output control value;

[0012] The abnormal state monitoring module calculates the state difference of the node based on the power output control value, detects the abnormal fluctuation value, analyzes the abnormal node with the normal state, and generates the energy storage abnormal state monitoring result.

[0013] As a further solution of the present invention, the load change trend data includes load values ​​in different time periods, fluctuation cycles, and mutation characteristic values; the charge and discharge dynamic control data includes charging rate, discharge rate, and fluctuation matching parameters; the load threshold instruction includes response threshold, trigger condition, start condition, and stop condition; the energy storage operation time interval data includes response time, time interval adjustment, and optimization rate parameters; the power output control value includes proportional parameter, integral parameter, and differential parameter; the energy storage abnormal state monitoring result includes voltage abnormality, current abnormality, and power abnormality.

[0014] As a further solution of the present invention, the load forecasting and analysis module includes:

[0015] The time period load calculation submodule extracts the load value of each time period based on the original data of the energy storage load, and calculates the load value change of each time period of the energy storage device during the full charging and full discharging process by referring to the load change under the full charging and full discharging state, analyzes the load of the differentiated time periods, calculates the difference between the load value of each time period and the load value of the previous time period, and obtains the load change rate set of the time period;

[0016] The fluctuation cycle analysis submodule analyzes the differences between adjacent values ​​in the sequence based on the load change rate set during the time period, extracts periodic peaks and valleys, identifies load changes during charging and discharging, calculates load fluctuation parameters of the time period in combination with the cumulative fluctuation amplitude, and matches the corresponding relationship between the fluctuation cycle and the load fluctuation value to obtain a periodic fluctuation characteristic set;

[0017] The load change detection submodule identifies data that deviates from the periodic fluctuation characteristics based on the periodic fluctuation characteristic set, and for load changes under full charge and full discharge states, counts the intensity changes and distribution positions of load mutation points to obtain load change trend data.

[0018] As a further solution of the present invention, the charge and discharge response control module includes:

[0019] The fluctuation amplitude extraction submodule screens the load value of each time period based on the load change trend data, calculates the difference between the load values ​​of adjacent time periods, and integrates the fluctuation amplitudes of the time periods into sequence data by summarizing the fluctuation differences of the time periods to obtain the fluctuation amplitude sequence of the time periods;

[0020] The rate difference adjustment submodule analyzes the difference between the charging rate and the load demand based on the period fluctuation amplitude sequence, performs segmented difference calculation on the difference between the load demand and the charging rate, determines the adjustment amount of the charging and discharging rate, and adds the adjustment amount to the current charging rate one by one to obtain the charging and discharging rate adjustment data;

[0021] The dynamic parameter matching submodule applies a multivariate linear regression algorithm based on the charge and discharge rate adjustment data, sequentially matches the adjusted rate data with the time period fluctuation amplitude sequence, integrates the matching values ​​of the rate and load fluctuation into a parameter matrix, and obtains the charge and discharge dynamic control data.

[0022] As a further solution of the present invention, the formula of the multivariate linear regression algorithm is as follows:

[0023]

[0024] Calculate the pairing values ​​of charge and discharge rate and load fluctuation to generate a parameter matrix, where: Indicates the pairing value of charge and discharge rate and load fluctuation, Representatives during the period After adjusting the charge and discharge rate, Representative period Internal load fluctuation range, Represents the difference value compared with the previous period. is the intercept of the regression model, is the coefficient of charge and discharge rate, is the coefficient that weights the square root of the load fluctuation, It is the coefficient of the inverse of the load fluctuation difference.

[0025] As a further solution of the present invention, the dynamic load threshold adjustment module includes:

[0026] The load fluctuation analysis submodule extracts the load data of each period based on the charge and discharge dynamic control data, calculates the peak and valley values ​​of the load in each period, analyzes the amplitude difference between adjacent peaks and valleys, and obtains the load fluctuation characteristic value sequence by summarizing the fluctuation amplitudes of the period and performing normalization processing;

[0027] The threshold response calculation submodule extracts the fluctuation characteristic value of each period based on the load fluctuation characteristic value sequence, calculates the difference value and determines the response amplitude of each period, calculates the dynamic response value for each period, and combines the values ​​into a sequence to obtain a dynamically adjusted response threshold;

[0028] The charge and discharge condition update submodule applies a sliding window algorithm based on the dynamically adjusted response threshold, checks the trigger conditions of the charging rate and load fluctuation amplitude in each time period, compares the current charging rate with the load threshold, verifies the start and stop conditions of charging and discharging, and gradually summarizes the time periods that meet the conditions to obtain the load threshold instruction.

[0029] As a further solution of the present invention, the formula of the sliding window algorithm is as follows:

[0030]

[0031] in, Indicates the response threshold of the charge and discharge conditions, Represents the charging rate at the current moment, represents the load demand threshold at the same time, represents the charge capacity adjustment factor, Represents the width of the sliding window.

[0032] As a further solution of the present invention, the energy storage response optimization module includes:

[0033] The response time analysis submodule calculates the start and stop time difference between adjacent time periods based on the load threshold instruction, summarizes the response interval of each time period, arranges the time intervals of the differentiated time periods, and obtains the energy storage response time series;

[0034] The dynamic rate optimization submodule extracts the time interval data of each time period based on the energy storage response time series, identifies the rate adjustment requirements between time periods, applies the adjusted rate to the energy storage response data of each time period, and obtains energy storage dynamic rate data;

[0035] The time interval adjustment submodule analyzes the start and stop conditions of the energy storage operation in the time period based on the energy storage dynamic rate data, calculates the matching of the adjusted rate and interval, and obtains the energy storage operation time interval data by analyzing the matching degree of the response rate and time interval of each time period.

[0036] As a further solution of the present invention, the PID adjustment module includes:

[0037] The data analysis submodule divides the original power output and load demand data within the time interval based on the energy storage operation time interval data, analyzes the power fluctuation and load demand value of each interval, performs interval data alignment and standardization processing, integrates the power and load parameters at the time point, and generates a power load demand data set;

[0038] The deviation calculation submodule decomposes the current power output and load demand values ​​based on the power load demand data set, extracts the parameters in sections, calculates the power and load differences, determines the deviation amplitude and change trend, summarizes the deviation parameters and performs normalization processing, and generates a power demand difference value;

[0039] The control signal processing submodule performs adjustment operations of proportional, integral and differential parameters based on the power demand difference value, performs proportional adjustment, and generates a power output control value by combining integral and differential adjustment operations.

[0040] As a further solution of the present invention, the abnormal state monitoring module includes:

[0041] The parameter monitoring submodule extracts the values ​​of voltage, current and power output from the charging and discharging equipment based on the power output control value, matches the data items in chronological order, organizes the parameters in segments in the time series, calculates the extreme value, average value and variation range of voltage, current and power in each time period, and summarizes the data into a sequence to establish a charging and discharging parameter data set;

[0042] The state difference analysis submodule groups the voltage, current and power values ​​of the nodes by time interval based on the charging and discharging parameter data set, calculates the difference between the standard reference value and the real-time value of the node in the time period, analyzes and records the fluctuation amplitude and parameter difference of the node in the differentiated time period, integrates the node difference feature data, and generates the node state difference value;

[0043] The abnormality detection submodule analyzes the parameter fluctuation amplitude and the set critical range based on the node state difference value, marks the fluctuation in the node that exceeds the critical value, sorts out the abnormal fluctuation type of the node, compares the parameter characteristic distribution of the abnormal node and the normal node, and generates the energy storage abnormal state monitoring result.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by real-time calculation and adjustment of the load change rate and fluctuation amplitude, changes in power demand can be predicted and responded to more accurately, and the stability and response speed of the power system can be effectively improved. By analyzing the load fluctuation cycle and mutation value, the power demand trend can be more accurately predicted, and the charging and discharging rate can be adjusted to match the actual demand, which not only improves the energy utilization efficiency, but also extends the service life of the energy storage equipment. The response threshold is dynamically adjusted and the energy storage operation time interval is optimized, so that the system can maintain the best operating state under different power load conditions, reduce energy consumption and operation and maintenance costs, and monitor the power output and equipment status in real time, detect and handle abnormalities in time, and ensure the safe and stable operation of the system, which has a significant effect on improving the overall efficiency and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 This is a flow chart of the load forecasting and analysis module in the present invention;

[0049] Figure 4 It is a flow chart of the charge and discharge response control module in the present invention;

[0050] Figure 5 It is a flow chart of the dynamic load threshold adjustment module in the present invention;

[0051] Figure 6 This is a flow chart of the energy storage response optimization module in the present invention;

[0052] Figure 7 It is a flow chart of the PID adjustment module in the present invention;

[0053] Figure 8 It is a flow chart of the abnormal state monitoring module in the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] Embodiment 1

[0057] See also Figure 1 to Figure 2 , an independent electrochemical energy storage integrated management system includes:

[0058] The load forecasting and analysis module is based on the original data of energy storage load, extracts the load value of different time periods, calculates the change rate of each time period, analyzes the fluctuation cycle, detects the mutation value, records the change characteristics, and analyzes the load change trend during the battery charging and discharging process in combination with the full and full discharge states. In the full and full discharge periods, it identifies and adjusts the battery load fluctuations to obtain the load change trend data;

[0059] The charge and discharge response control module extracts the load fluctuation amplitude and the charge and discharge rate based on the load change trend data, determines the difference between the current charging rate and the load demand, dynamically adjusts the charge and discharge rate, matches the adjusted rate parameter with the load fluctuation amplitude, and obtains the charge and discharge dynamic control data;

[0060] The dynamic load threshold adjustment module extracts the current load fluctuation characteristic value based on the dynamic control data of charge and discharge, calculates the response threshold of the energy storage to the fluctuation range item by item, judges the threshold trigger condition through the adjusted charge and discharge rate and load fluctuation amplitude, updates the charge and discharge start and stop conditions, and obtains the load threshold instruction;

[0061] The energy storage response optimization module analyzes the energy storage response time based on the load threshold instruction, extracts the current response interval data, dynamically optimizes the response rate, adjusts the time interval of the energy storage operation, and obtains the energy storage operation time interval data;

[0062] The PID control module extracts the power output and load demand values ​​based on the energy storage operation time interval data, determines the difference between the current power and the demand, and adjusts the proportional, integral, and differential parameters to generate the power output control value;

[0063] The abnormal state monitoring module extracts the voltage, current and power output parameters of the charging and discharging equipment based on the power output control value, calculates the state difference of the nodes, detects the abnormal fluctuation value, analyzes the abnormal nodes with the normal state, and generates the energy storage abnormal state monitoring results.

[0064] The load change trend data includes the load value in different time periods, the fluctuation cycle, and the mutation characteristic value. The dynamic control data of charge and discharge includes the charging rate, the discharge rate, and the fluctuation matching parameters. The load threshold instructions include the response threshold, the trigger condition, the start condition, and the stop condition. The energy storage operation time interval data includes the response time, the time interval adjustment, and the optimization rate parameters. The power output control value includes the proportional parameter, the integral parameter, and the differential parameter. The energy storage abnormal state monitoring results include the voltage abnormality, the current abnormality, and the power abnormality.

[0065] Specifically, if Figure 2 , 3 As shown, the load forecasting analysis module includes:

[0066] The time period load calculation submodule extracts the load value of each time period based on the original data of the energy storage load, and calculates the load value change of each time period of the energy storage device during the full charging and full discharging process by referring to the load change under the full charging and full discharging state. The load of the differentiated time period is analyzed, and the difference between the load value of each time period and the load value of the previous time period is calculated. The execution process of obtaining the time period load change rate set is as follows;

[0067] Based on the original data of energy storage load, the load value of each period is extracted. First, the load data in each time period is obtained, and the load changes in each period are analyzed according to the actual operation of the energy storage equipment. For the changes in the load value of each period, the load change trends under the full charge and full discharge states are compared, the maximum load value under the full charge state and the minimum load value under the full discharge state are set, and the load value changes of the energy storage equipment under these two states are calculated. The load value of each period is compared with the load changes in the full charge and full discharge states to identify the load fluctuation range. The difference between the load value of each period and the load value of the previous period is calculated. The calculation method is to obtain the load change rate set of each period by calculating the load difference between each two adjacent periods and recording the change rate, and record its value and change trend. Then, these difference values ​​are counted and analyzed to understand the load change pattern and help evaluate the load fluctuation of the energy storage equipment. In this process, special attention is paid to the periods with large load change rates, which may be critical moments when energy consumption or the state of energy storage equipment changes significantly.

[0068] The fluctuation cycle analysis submodule analyzes the differences between adjacent values ​​in the sequence based on the load change rate set during the period, extracts the periodic peak and valley values, identifies the load changes during the charging and discharging process, and calculates the load fluctuation parameters of the time period in combination with the cumulative fluctuation amplitude, and matches the corresponding relationship between the fluctuation cycle and the load fluctuation value to obtain the execution process of the periodic fluctuation characteristic set as follows;

[0069] Based on the load change rate set of each period, sequence analysis is first performed to extract the differences between the values ​​of adjacent periods and identify periodic fluctuations in the data. By calculating the range of load change rate in each period, special attention is paid to the peak and trough values ​​of the fluctuation. During the charging and discharging process, the load value usually shows periodic fluctuations. Therefore, by analyzing the fluctuations, the law of load change during the charging and discharging process can be identified. By analyzing the load fluctuation amplitude in each cycle, the load fluctuation parameters in the time period are calculated to determine whether the load in each time period meets the expected fluctuation pattern of charging or discharging. At this time, the periodic fluctuation parameters can not only help identify periodic fluctuations, but also analyze the duration and amplitude of each cycle in combination with the time dimension, thereby providing a reference for subsequent analysis. The corresponding relationship between load fluctuations and time periods is matched to obtain a periodic fluctuation characteristic set. According to these periodic characteristics, the law of load fluctuation of energy storage equipment can be further understood, the excessive fluctuation period that may exist in the equipment can be inferred, and a basis can be provided for optimizing energy storage operation strategies.

[0070] The load change detection submodule identifies data that deviates from the periodic fluctuation characteristics based on the periodic fluctuation characteristic set. For the load changes under full charge and full discharge states, the intensity changes and distribution positions of the load mutation points are counted to obtain the execution process of the load change trend data as follows;

[0071] Based on the periodic fluctuation characteristic set, load change detection is performed. By analyzing the deviation of the load fluctuation characteristics, which data has obvious abnormal fluctuations, it is identified. When comparing the periodic fluctuation characteristics, special attention is paid to the points where the load fluctuation characteristics have abrupt changes. The intensity change and distribution position of each mutation point are counted to identify the abnormal points of load fluctuation. These mutation points may indicate the risk of abnormal operation or excessive load fluctuation of the equipment. In this process, it is necessary to comprehensively consider the load change data under full charge and full discharge states, and extract the fluctuation characteristics under these two extreme states, so as to more accurately analyze the load change under non-limit state. By counting the intensity change and position distribution of the load mutation points, potential problems that may exist in the actual operation of energy storage equipment can be revealed. In addition, the frequency and intensity of load fluctuations need to be considered in the statistical process, which helps to judge the working stability of energy storage equipment and the changing trend of fluctuation mode, and finally obtain the load change trend data. These trend data provide a key reference for subsequent risk assessment, state prediction and equipment maintenance.

[0072] Specifically, if Figure 2 , 4 As shown, the charge and discharge response control module includes:

[0073] The fluctuation amplitude extraction submodule screens the load value of each time period based on the load change trend data, calculates the difference between the load values ​​of adjacent time periods, and integrates the fluctuation amplitude of each time period into sequence data by summarizing the fluctuation differences of each time period. The execution process of obtaining the fluctuation amplitude sequence of each time period is as follows;

[0074] Based on the load change trend data, the load value of each time period is screened, and the load data is obtained section by section in chronological order. The load values ​​of adjacent time periods are calculated one by one to gradually form a time period change sequence containing the load differences of each time period. The load fluctuation amount of each time period is obtained by gradually accumulating the fluctuation difference data of the time period in chronological order. For the fluctuation data of each time period, the difference value is calculated and the results are summarized in the order of the time period. It is necessary to consider the change amplitude and difference of the load value in each time period. The integrated sequence of fluctuation amplitude provides basic data for the subsequent dynamic analysis of the load, and in actual use, this sequence can be used to observe the short-term change trend of the load value, thereby providing a basis for the subsequent charging rate and dynamic matching. The generation of the time period fluctuation amplitude sequence can gradually accumulate the difference changes in the load fluctuation amplitude between different time periods, so as to obtain detailed information on the fluctuation changes in the subsequent steps.

[0075] The rate difference adjustment submodule analyzes the difference between the charging rate and the load demand based on the period fluctuation amplitude sequence, performs segmented difference calculation on the difference between the load demand and the charging rate, determines the adjustment amount of the charging and discharging rate, and adds the adjustment amount to the current charging rate one by one. The execution process of obtaining the charging and discharging rate adjustment data is as follows;

[0076] Based on the time period fluctuation amplitude sequence, the difference between the charging rate and the load demand is analyzed and calculated, and combined with the current load demand data and the charging rate data, the difference between the load demand data and the charging rate data is analyzed in sections, and the difference between the load demand and the charging rate is recorded respectively. On this basis, the specific adjustment amount of the charging and discharging rate in each time period is determined, and the adjustment amount of each time period is used to update the current charging rate value, so that the charging rate can dynamically fit the current load demand. By adding the adjustment amount of each time period to the current charging rate value one by one, a data set containing the charging and discharging rate adjustment values ​​of each time period can be obtained. The data set dynamically updates the charging rate through the segmented difference calculation within the time period, and is associated with the time period fluctuation amplitude sequence, so that the overall rate control is more accurate, and the charging and discharging rate adjustment data is formed, which provides a basis for the subsequent dynamic matching of charging and discharging, and ensures that the charging rate can flexibly adapt to the changes in load demand in each time period.

[0077] The dynamic parameter matching submodule is based on the charge and discharge rate adjustment data, applies the multivariate linear regression algorithm, matches the adjusted rate data with the time period fluctuation amplitude sequence in sequence, integrates the matching values ​​of the rate and load fluctuation into a parameter matrix, and obtains the execution process of the charge and discharge dynamic control data as follows;

[0078] The formula for the multivariate linear regression algorithm is as follows:

[0079]

[0080] Calculate the pairing values ​​of charge and discharge rate and load fluctuation to generate a parameter matrix, where: Indicates the pairing value of charge and discharge rate and load fluctuation, Representatives during the period After adjusting the charge and discharge rate, Representative period Internal load fluctuation range, Represents the difference value compared with the previous period. is the intercept of the regression model, is the coefficient of charge and discharge rate, is the coefficient that weights the square root of the load fluctuation, is the coefficient of the inverse of the load fluctuation difference;

[0081] Detailed explanation of the formula and the process of formula calculation and derivation:

[0082] This formula is used to calculate the pairing value , indicating that during a specific period of time Charge and discharge control data, each parameter represents quantitative information obtained through data monitoring, collection or calculation;

[0083] is the intercept term, representing the baseline paired value when no other variables are in play, set to 0.15, derived from the average baseline analysis of historical data;

[0084] is the charge and discharge rate The coefficient of , which reflects how each unit increase rate affects the pairing value, is set to 0.05 based on historical data regression analysis;

[0085] Load fluctuation The coefficient of the square root of , which mitigates the impact of high volatility through square root transformation, is set to 0.02 based on sensitivity testing of past data;

[0086] yes The reciprocal coefficient of Indicates the difference between the current load and the previous period. This coefficient is set to -0.03, based on the average impact analysis of the data difference value;

[0087] Set in time period , the data is as follows:

[0088] Unit,obtained from real-time monitoring of the energy management system;

[0089] Unit,represents the load fluctuation amplitude, which is monitored by real-time sensors;

[0090] Unit, indicating the difference compared with the previous period, also provided by the monitoring system;

[0091] According to the above parameters, calculate The process is as follows:

[0092]

[0093]

[0094]

[0095] This result shows that in a given period , the charge-discharge pairing value is 1.5494, which means that after considering the charge-discharge rate, load fluctuation and their differences, the energy management system of this period can adjust the control strategy accordingly to ensure the balance between power demand and supply, thereby optimizing the charge-discharge efficiency. This pairing value reflects the dynamic control index calculated by the multivariate linear regression model based on real-time data, providing data support for system operation.

[0096] Specifically, if Figure 2 , 5 As shown, the dynamic load threshold adjustment module includes:

[0097] The load fluctuation analysis submodule extracts the load data of each period based on the dynamic control data of charge and discharge, calculates the peak and valley values ​​of the load in each period, analyzes the amplitude difference between adjacent peaks and valleys, and obtains the load fluctuation characteristic value sequence by summarizing the fluctuation amplitude of the period and performing normalization processing. The execution process is as follows;

[0098] Based on the load information of the dynamic control data of charge and discharge, the load data of each time period is extracted, and the load fluctuation characteristics are analyzed section by section. By detecting the peak and trough values ​​of the load in each time period, the extreme state of the fluctuation is obtained, and the peak and trough data set of the load in the time period is gradually formed. The amplitude difference between adjacent peaks and troughs is calculated one by one to form a difference sequence reflecting the load fluctuation, and the fluctuation amplitude of each time period is normalized. The amplitude difference is adjusted by normalization so that the fluctuation characteristics of each time period can be presented on the same scale. The normalization process divides the amplitude difference of each load by the maximum difference to form a fluctuation characteristic value in the interval [0, 1], so as to further analyze the relative fluctuation characteristics within the time period, and the normalized data is combined into a load fluctuation characteristic value sequence to provide the input data required for subsequent response calculation.

[0099] The threshold response calculation submodule extracts the fluctuation characteristic value of each period based on the load fluctuation characteristic value sequence, calculates the difference value and determines the response amplitude of each period, calculates the dynamic response value for each period, and combines the values ​​into a sequence to obtain the execution process of the dynamically adjusted response threshold value as follows;

[0100] Based on the load fluctuation characteristic value sequence, the fluctuation characteristic value of each time period is extracted, and the difference analysis of the fluctuation characteristic values ​​of adjacent time periods is performed to gradually form a difference value sequence. The response amplitude of each time period is calculated based on the difference value. For each time period, the dynamic response value is calculated, and the dynamic response value is normalized by comparing the fluctuation range of the characteristic value of each time period with the change trend of the difference value, so that each response value is expressed in the interval [0, 1], which is convenient for comparison between time periods. The normalized response value is used to form a dynamic response threshold sequence to match the response degree under different load fluctuation conditions. The dynamic response values ​​of all time periods are combined into a sequence in order to obtain a dynamic adjustment response threshold sequence, which provides basic data for subsequent charging and discharging condition update operations.

[0101] The charging and discharging condition update submodule uses a sliding window algorithm based on a dynamically adjusted response threshold to check the trigger conditions of the charging rate and load fluctuation amplitude in each period, compare the current charging rate with the load threshold, verify the start and stop conditions of charging and discharging, and gradually summarize the periods that meet the conditions. The execution process of obtaining the load threshold instruction is as follows;

[0102] The formula for the sliding window algorithm is as follows:

[0103]

[0104] in, Indicates the response threshold of the charge and discharge conditions, Represents the charging rate at the current moment, represents the load demand threshold at the same time, represents the charge capacity adjustment factor, Represents the width of the sliding window;

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

[0106] This formula is used to determine the start and stop of charging and discharging conditions in the load response strategy of the power grid, and to monitor and adjust the charging rate in real time. and load demand threshold , and using sliding window To smooth short-term fluctuations, and at the same time adjust the coefficient To optimize charging behavior;

[0107] Charging rate By acquiring real-time grid data, the charging rate recorded by the grid monitoring system at a specific moment is set to 500 kWh;

[0108] Load demand threshold The demand threshold at this moment is also obtained through real-time monitoring of the power grid, and is set to 450 kWh;

[0109] Adjustment coefficient According to historical data analysis, it is used to adjust the response of charging rate to load threshold. is 1.05, reflecting the impact of historical load fluctuations on current regulation;

[0110] Sliding Window Considering the need to smooth data fluctuations in a short period of time, the time interval is set to 10 minutes, or 0.167 hours, to provide sufficient response time and reduce the impact of abnormal data;

[0111] Substitute the above parameters into the formula for calculation:

[0112]

[0113]

[0114]

[0115]

[0116] This result shows that the response threshold of the charge and discharge conditions is is 44.45. The threshold is used to determine whether the charge and discharge start or stop conditions are met. If the value exceeds a preset specific value, such as 40, the system will start or stop charging to adapt to the grid demand and ensure load balance. If the value is higher than 40, it means that charging or discharging behavior should be triggered at this moment to adjust the actual load to reach the threshold balance.

[0117] Specifically, if Figure 2 , 6 As shown, the energy storage response optimization module includes:

[0118] The response time analysis submodule is based on the load threshold instruction. By calculating the start and stop time difference between adjacent time periods, the response interval of each time period is summarized, and the time intervals of the differentiated time periods are arranged to obtain the execution process of the energy storage response time series as follows;

[0119] Based on the load threshold instruction, by calculating the start and stop time difference between adjacent time periods, the response time interval of each time period is recorded in turn, the start and stop time node data are gradually collected within the time period, and the start and stop time difference is calculated. By summarizing the time difference in different time periods, a set of data series containing all response time intervals is formed, and in the data series, the response time interval of each time period is arranged in time series, and the data of all time periods are integrated into a time series to record the response speed and time change of the energy storage equipment under different load threshold conditions. The statistics of the response time of all time periods help to fully understand the response delay or adjustment range of the energy storage system under different load threshold changes, and provide data basis for subsequent dynamic rate optimization, complete the time interval summary of all time periods, and obtain the energy storage response time series.

[0120] The dynamic rate optimization submodule extracts the time interval data of each period based on the energy storage response time series, identifies the rate adjustment requirements between periods, applies the adjusted rate to the energy storage response data of each period, and obtains the execution process of the energy storage dynamic rate data as follows;

[0121] Based on the generated energy storage response time series, the time interval data of each time period is extracted from it. By analyzing the time intervals of different time periods, the rate adjustment requirements of each time period in terms of response time are identified. According to the energy storage response characteristics of each time period, the corresponding adjustment range is calculated for the specific rate adjustment requirements, and the adjusted rate value is applied to the energy storage response data of each time period. This process requires comparing the rate values ​​before and after the adjustment time period by time period, and ensuring that the adjusted rate can match the energy storage response requirements of the time period, gradually forming a set of dynamic data containing the energy storage rate optimization results of each time period, and integrating the adjusted rate values ​​into energy storage dynamic rate data, providing basic data for subsequent time interval adjustment and response matching, and making the response capability of the energy storage system in different time periods more accurate.

[0122] The time interval adjustment submodule analyzes the start and stop conditions of the energy storage operation in the time period based on the energy storage dynamic rate data, calculates the matching of the adjusted rate and the interval, and obtains the energy storage operation time interval data by analyzing the matching degree of the response rate and the time interval of each time period. The execution process is as follows;

[0123] By analyzing the matching degree between the response rate and the time interval in each period, according to the formula:

[0124]

[0125] Calculate the energy storage operation time interval data, where: represents the matching value of the i-th period, represents the response rate in the i-th period, The time interval representing the period;

[0126] The formula is used to analyze the matching of the rate and time interval of energy storage operation in each period. The matching value is calculated by the ratio of the period response rate to the time interval to measure the response efficiency of the energy storage operation in the period.

[0127] Set the response rate of the first period to 12 and the time interval to 4, then the matching degree is:

[0128]

[0129] The result of 3 indicates that in the first period, the response rate and the time interval are highly matched, reflecting the response effect of the energy storage operation in the period. By calculating the matching value of each period, the time interval data of the energy storage operation in each period can be obtained, providing a basis for subsequent time optimization.

[0130] Specifically, if Figure 2 , 7 As shown, the PID adjustment module includes:

[0131] The data analysis submodule divides the original power output and load demand data in the time interval based on the energy storage operation time interval data, analyzes the power fluctuation and load demand value of each interval, performs interval data alignment and standardization processing, integrates the power and load parameters at the time point, and generates the power load demand data set. The execution process is as follows;

[0132] Based on the energy storage operation time interval data, the time interval is divided into multiple sections, and the corresponding original power output data and load demand data are extracted in each interval. By analyzing the power fluctuations in different intervals, the data sets of each interval are aligned to make the power data and load data of each time period consistent at the same time point, and the differences in power fluctuations between different time periods are eliminated through standardized processing methods. The data is analyzed with a unified standard data scale, and the power value and load demand value of each data point are respectively subtracted from their mean and divided by the standard deviation, so that the data presents a standard normal distribution, which is convenient for subsequent differentiated calculations. The integrated power and load data at the time point constitute the power load demand data set, which provides unified basic data for subsequent deviation calculations.

[0133] The deviation calculation submodule decomposes the current power output and load demand values ​​based on the power load demand data set, extracts the parameters in sections, calculates the power and load differences, determines the deviation amplitude and change trend, summarizes the deviation parameters and performs normalization, and generates the power demand difference value. The execution process is as follows;

[0134] The current power output and load demand values ​​are extracted in sections and the deviation is calculated according to the formula:

[0135]

[0136] Calculate the deviation amplitude and change trend, where: represents the average deviation value of the i-th segment, represents the power output at the jth moment in the i-th segment, represents the load demand at the corresponding moment, Indicates the time period;

[0137] This formula is used to calculate the average deviation between the power output and load demand of a segment. The deviation of the segment is obtained by summing up the absolute difference between the power output value and the load demand value at each moment and taking the average value.

[0138] Assume that in the first section, there are 3 moments, the power output values ​​are 200, 210 and 195 respectively, and the load demand values ​​are 190, 205 and 200 respectively. The average deviation value is:

[0139]

[0140] The result 6.67 means that in the first period, the average deviation between the power output and the load demand is 6.67 units. By calculating the deviation amplitude in each period, the difference between the power and the load demand can be obtained, providing accurate input data for the subsequent control signal processing module.

[0141] The control signal processing submodule performs proportional, integral, and differential parameter adjustment operations based on the power demand difference value, performs proportional adjustment, and combines integral and differential adjustment operations to generate the power output control value. The execution process is as follows;

[0142] Based on the power demand difference value, the proportional, integral and differential parameters of each time period are adjusted respectively. First, the proportional adjustment operation is performed. The initial adjustment signal of each time period is determined by multiplying the difference value by the proportional factor. The difference value is integrated in turn. The deviation between the current time period and the historical time period is accumulated and summed to generate cumulative difference data to smooth the difference change trend. The differential operation is performed on the basis of integration. The adjustment range of the difference fluctuation is obtained by calculating the difference change rate between the current time period and the previous time period. The proportional adjustment result of the difference value is first accumulated to the current integral value, and the differential value is gradually increased or decreased. The adjusted difference result is used to generate the power output control value, and gradually constitutes the power output control sequence, so as to provide clear guidance for power regulation in each time period.

[0143] Specifically, if Figure 2 , 8 As shown, the abnormal state monitoring module includes:

[0144] The parameter monitoring submodule extracts the values ​​of voltage, current and power output from the charging and discharging equipment based on the power output control value, matches the data items in chronological order, organizes the parameters in segments in the time series, calculates the extreme value, average value and variation range of voltage, current and power in each time period, and summarizes the data into a sequence. The execution process of establishing the charging and discharging parameter data set is as follows;

[0145] Based on the power output control value, the voltage, current and power output values ​​are extracted from the charging and discharging equipment, and the data is collected and recorded at predetermined time intervals. During the data collection process, each data item is associated with a timestamp to ensure the consistency of the time series. The collected voltage, current and power data will be segmented according to the set time intervals. The data of each time period will be calculated to obtain the extreme values, average values ​​and variation range of voltage, current and power. A set of statistical parameters are calculated by calculating the extreme values, average values ​​and variation range of each time period. The statistical parameters will be summarized into a complete data sequence, and the sequence will be integrated into a charging and discharging parameter data set, eventually forming a structured parameter set including voltage, current and power.

[0146] The state difference analysis submodule is based on the charge and discharge parameter data set, groups the voltage, current and power values ​​of the nodes by time interval, calculates the difference between the standard reference value and the real-time value of the node in the time period, analyzes and records the fluctuation amplitude and parameter difference of the node in the differentiated time period, integrates the node difference feature data, and generates the node state difference value. The execution process is as follows;

[0147] Based on the charging and discharging parameter data set, the voltage, current and power values ​​of the nodes are segmented and sorted, and grouped by time intervals to analyze the changing trends of the voltage, current and power values ​​of the nodes in different time periods. The node voltage, current and power values ​​of each time period will be compared with the preset standard reference values. The parameter change amplitude is obtained by calculating the difference between the real-time value and the standard reference value of each node in each time period. At the same time, the difference changes of each node in different time periods are quantified. The difference changes will be recorded in the form of fluctuation amplitude and parameter difference and sorted into difference feature data to generate node state difference value.

[0148] The abnormal detection submodule analyzes the parameter fluctuation amplitude and the set critical range based on the node state difference value, marks the fluctuation in the node that exceeds the critical value, sorts out the abnormal fluctuation type of the node, compares the parameter characteristic distribution of the abnormal node and the normal node, and generates the energy storage abnormal state monitoring result. The execution process is as follows;

[0149] Analyze the fluctuation range of node parameters according to the formula:

[0150]

[0151] Calculate the node status difference value, where, Represents the node status difference value, Represents the node in the time period The real-time power value, Represents the standard reference power value of the node, represents the difference weight coefficient, which is used to measure the impact of power difference;

[0152] Difference weight coefficient Used to adjust the impact of different node parameter differences on the overall state, the value range is to and set according to the importance of the node and historical operation data;

[0153] Real-time power and standard reference power The data is obtained through node monitoring and historical records, with the real-time power value of the node in a certain period of time as Watt, reference value for Watts, difference weight coefficient For example;

[0154] The calculation process is as follows:

[0155]

[0156] Calculated node status difference value It indicates that the state deviation intensity of the node during this period is 24 watts. This value indicates that the current operating state of the node deviates greatly from the reference state. It will be used to identify and mark abnormal node states to support further abnormal analysis and generation of monitoring results.

[0157] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them 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 of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An independent electrochemical energy storage integrated management system, characterized in that: The system comprises: The load forecasting and analysis module is based on the original data of energy storage load, extracts the load value of different time periods, calculates the change rate of each time period, analyzes the fluctuation cycle, detects the mutation value, records the change characteristics, and analyzes the load change trend during the battery charging and discharging process in combination with the full and full discharge states. In the full and full discharge periods, it identifies and adjusts the battery load fluctuations to obtain the load change trend data; The charge and discharge response control module determines the difference between the charging rate and the load based on the load change trend data, adjusts the rate parameters to match the fluctuation, and obtains the charge and discharge dynamic control data; The charge and discharge response control module comprises: The fluctuation amplitude extraction submodule screens the load value of each time period based on the load change trend data, calculates the difference between the load values ​​of adjacent time periods, and integrates the fluctuation amplitudes of the time periods into sequence data by summarizing the fluctuation differences of the time periods to obtain the fluctuation amplitude sequence of the time periods; The rate difference adjustment submodule analyzes the difference between the charging rate and the load demand based on the period fluctuation amplitude sequence, performs segmented difference calculation on the difference between the load demand and the charging rate, determines the adjustment amount of the charging and discharging rate, and adds the adjustment amount to the current charging rate one by one to obtain the charging and discharging rate adjustment data; The dynamic parameter matching submodule applies a multivariate linear regression algorithm based on the charge and discharge rate adjustment data, sequentially matches the adjusted rate data with the time period fluctuation amplitude sequence, integrates the matching values ​​of the rate and load fluctuation into a parameter matrix, and obtains the charge and discharge dynamic control data; The formula for the multivariate linear regression algorithm is as follows: Calculate the pairing values ​​of charge and discharge rate and load fluctuation to generate a parameter matrix, where: Indicates the pairing value of charge and discharge rate and load fluctuation, Representatives during the period After adjusting the charge and discharge rate, Representative period Internal load fluctuation range, Represents the difference value compared with the previous period. is the intercept of the regression model, is the coefficient of charge and discharge rate, is the coefficient that weights the square root of the load fluctuation, is the coefficient of the inverse of the load fluctuation difference; The dynamic load threshold adjustment module extracts the load fluctuation characteristic value based on the charge and discharge dynamic control data, calculates the energy storage response threshold, determines the trigger condition, updates the start and stop conditions, and obtains the load threshold instruction; The dynamic load threshold adjustment module includes: The load fluctuation analysis submodule extracts the load data of each period based on the charge and discharge dynamic control data, calculates the peak and valley values ​​of the load in each period, analyzes the amplitude difference between adjacent peaks and valleys, and obtains the load fluctuation characteristic value sequence by summarizing the fluctuation amplitudes of the period and performing normalization processing; The threshold response calculation submodule extracts the fluctuation characteristic value of each period based on the load fluctuation characteristic value sequence, calculates the difference value and determines the response amplitude of each period, calculates the dynamic response value for each period, and combines the values ​​into a sequence to obtain a dynamically adjusted response threshold; The charging and discharging condition updating submodule applies a sliding window algorithm based on the dynamically adjusted response threshold, checks the triggering conditions of the charging rate and load fluctuation amplitude in each time period, compares the current charging rate with the load threshold, verifies the start and stop conditions of charging and discharging, and gradually summarizes the time periods that meet the conditions to obtain the load threshold instruction; The formula of the sliding window algorithm is as follows: in, Indicates the response threshold of the charge and discharge conditions, Represents the charging rate at the current moment, represents the load demand threshold at the same time, represents the charge capacity adjustment factor, Represents the width of the sliding window; The energy storage response optimization module analyzes the energy storage response time based on the load threshold instruction, optimizes the response rate, adjusts the time interval of the energy storage operation, and obtains the energy storage operation time interval data; The PID adjustment module extracts the power output and load demand values ​​based on the energy storage operation time interval data, determines the difference between the current power and the demand, and adjusts the proportional, integral and differential parameters to generate a power output control value; The abnormal state monitoring module calculates the state difference of the node based on the power output control value, detects the abnormal fluctuation value, analyzes the abnormal node with the normal state, and generates the energy storage abnormal state monitoring result.

2. The independent electrochemical energy storage integrated management system according to claim 1 is characterized in that: The load change trend data includes load values ​​in different time periods, fluctuation cycles, and mutation characteristic values; the charge and discharge dynamic control data includes charging rate, discharge rate, and fluctuation matching parameters; the load threshold instruction includes response threshold, trigger condition, start condition, and stop condition; the energy storage operation time interval data includes response time, time interval adjustment, and optimization rate parameters; the power output control value includes proportional parameter, integral parameter, and differential parameter; the energy storage abnormal state monitoring results include voltage abnormality, current abnormality, and power abnormality.

3. The independent electrochemical energy storage integrated management system according to claim 1 is characterized in that: The load forecasting and analysis module includes: The time period load calculation submodule extracts the load value of each time period based on the original data of the energy storage load, and calculates the load value change of each time period of the energy storage device during the full charging and full discharging process by referring to the load change under the full charging and full discharging state, analyzes the load of the differentiated time periods, calculates the difference between the load value of each time period and the load value of the previous time period, and obtains the load change rate set of the time period; The fluctuation cycle analysis submodule analyzes the differences between adjacent values ​​in the sequence based on the load change rate set during the time period, extracts periodic peaks and valleys, identifies load changes during charging and discharging, calculates load fluctuation parameters of the time period in combination with the cumulative fluctuation amplitude, and matches the corresponding relationship between the fluctuation cycle and the load fluctuation value to obtain a periodic fluctuation characteristic set; The load change detection submodule identifies data that deviates from the periodic fluctuation characteristics based on the periodic fluctuation characteristic set, and for load changes under full charge and full discharge states, counts the intensity changes and distribution positions of load mutation points to obtain load change trend data.

4. The independent electrochemical energy storage integrated management system according to claim 1 is characterized in that: The energy storage response optimization module includes: The response time analysis submodule calculates the start and stop time difference between adjacent time periods based on the load threshold instruction, summarizes the response interval of each time period, arranges the time intervals of the differentiated time periods, and obtains the energy storage response time series; The dynamic rate optimization submodule extracts the time interval data of each time period based on the energy storage response time series, identifies the rate adjustment requirements between time periods, applies the adjusted rate to the energy storage response data of each time period, and obtains energy storage dynamic rate data; The time interval adjustment submodule analyzes the start and stop conditions of the energy storage operation in the time period based on the energy storage dynamic rate data, calculates the matching of the adjusted rate and interval, and obtains the energy storage operation time interval data by analyzing the matching degree of the response rate and time interval of each time period.

5. The independent electrochemical energy storage integrated management system according to claim 1 is characterized by: The PID adjustment module comprises: The data analysis submodule divides the original power output and load demand data within the time interval based on the energy storage operation time interval data, analyzes the power fluctuation and load demand value of each interval, performs interval data alignment and standardization processing, integrates the power and load parameters at the time point, and generates a power load demand data set; The deviation calculation submodule decomposes the current power output and load demand values ​​based on the power load demand data set, extracts the parameters in sections, calculates the power and load differences, determines the deviation amplitude and change trend, summarizes the deviation parameters and performs normalization processing, and generates a power demand difference value; The control signal processing submodule performs adjustment operations of proportional, integral and differential parameters based on the power demand difference value, performs proportional adjustment, and generates a power output control value by combining integral and differential adjustment operations.

6. The independent electrochemical energy storage integrated management system according to claim 1 is characterized by: The abnormal state monitoring module includes: The parameter monitoring submodule extracts the values ​​of voltage, current and power output from the charging and discharging equipment based on the power output control value, matches the data items in chronological order, organizes the parameters in segments in the time series, calculates the extreme value, average value and variation range of voltage, current and power in each time period, and summarizes the data into a sequence to establish a charging and discharging parameter data set; The state difference analysis submodule groups the voltage, current and power values ​​of the nodes by time interval based on the charging and discharging parameter data set, calculates the difference between the standard reference value and the real-time value of the node in the time period, analyzes and records the fluctuation amplitude and parameter difference of the node in the differentiated time period, integrates the node difference feature data, and generates the node state difference value; The abnormality detection submodule analyzes the parameter fluctuation amplitude and the set critical range based on the node state difference value, marks the fluctuation in the node that exceeds the critical value, sorts out the abnormal fluctuation type of the node, compares the parameter characteristic distribution of the abnormal node and the normal node, and generates the energy storage abnormal state monitoring result.

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