A control method and system of an industrial park energy storage device

Abstract

The application discloses a kind of control method and system of industrial park energy storage device, it is related to energy storage device control technical field.This application is based on production plan and real-time electricity price, dynamically senses load demand fluctuation, and distributes distribution unit accordingly dynamic adjustment;Further through real-time current signal analysis, actively identify potential hazards, guarantee the reliability of distribution unit hazard analysis result;While comprehensive multidimensional data assesses the overall health status of distribution unit, provides basis for maintenance and replacement decision, solves the limitations existing in current energy storage device control feasibility analysis;Once hidden danger or state anomaly occurs, immediately give early warning, improve system security and reliability;From demand calculation, resource matching and operation monitoring to abnormal early warning form complete closed loop, realize the intelligentization, safety and high efficiency management of energy storage device control.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage device control, and in particular to a control method and system for an energy storage device in an industrial park. Background Art

[0002] In modern industrial parks, a stable, efficient, and secure power supply is essential to ensuring production continuity and economic benefits. However, traditional industrial park power distribution management systems are gradually exposing numerous problems in the face of increasingly complex production demands, a dynamic energy market environment, and safety hazards brought about by aging equipment.

[0003] The prior art, such as the invention application patent with announcement number: CN113469401A, discloses a control method, system, device and storage medium for an energy supply system. The control method of the energy supply system includes: obtaining an expected influence matrix based on the initial energy supply demand of each energy-consuming object in each time interval in each time period, and the energy supply parameters in each time interval in each time period; constructing an optimized influence matrix under the condition of minimizing the rank of the influence matrix based on the expected influence matrix and the matrix position set; determining the actual energy supply parameters of the energy supply system based on the optimized influence matrix, so as to control the energy supply system to supply energy to each energy-consuming object according to the actual energy supply parameters.

[0004] In response to the above scheme, the inventors of this application found that the above technology has at least the following technical problems: 1. Currently, the instantaneous frequency of the current signal is lacking. The absolute value of the ratio of the instantaneous frequency of the dynamically updated current signal to the frequency standard deviation threshold is multiplied by the corresponding adaptive weight factor and the product of the average value of the frequency fluctuation and the corresponding adaptive weight factor is not added. The hidden danger coefficient of the distribution unit cannot be calculated, and the calculation accuracy cannot be improved. At the same time, the accuracy of the hidden danger assessment results is reduced to a certain extent. The data deviation in the assessment process cannot be effectively reduced, and the potential risk points cannot be identified more accurately, so the possibility of hidden dangers and the severity of the consequences cannot be quantified. Ultimately, the reliability, objectivity and predictive ability of the overall hidden danger assessment results cannot be substantially enhanced.

[0005] 2. Currently, there is a lack of multi-source data fusion modeling and dynamic threshold judgment mechanisms, which make it impossible to achieve accurate assessment of the health status of distribution units and early warning of abnormalities. There is a lack of real-time dynamic analysis during the use of distribution units, and standby distribution units cannot be replaced in time. Without multiple detections, it is impossible to significantly improve the reliability of the power grid and reduce operation and maintenance costs, and it is impossible to provide core data support for predictive maintenance of smart grids. Summary of the Invention

[0006] In response to the above-mentioned technical deficiencies, the purpose of this application is to provide a control method and system for an industrial park energy storage device.

[0007] In order to solve the above technical problems, the present application adopts the following technical solutions: In the first aspect, the present application provides a control method for an energy storage device in an industrial park, the method comprising the following steps: Step 1, based on the real-time electricity price gradient and the pre-acquired production instructions, generate a production instruction matrix factor, and then obtain the load data corresponding to the production instruction, so as to use the load formula to calculate the load demand fluctuation index.

[0008] Step 2: Based on the load demand fluctuation index, power distribution units are matched for areas within the park.

[0009] Step 3: When the distribution unit distributes power, the instantaneous frequency of the current signal is collected in real time, and the instantaneous frequency of the current signal is analyzed using adaptive weights to obtain a hidden danger assessment coefficient, thereby determining the execution result of the potential hidden danger of the distribution unit.

[0010] Step 4: Perform comprehensive calculation on the status data, efficiency attenuation factor and environmental factor of the power distribution unit to obtain the working status index of the power distribution unit, and make an abnormal judgment on the working status of the power distribution unit.

[0011] Step 5: Provide an early warning based on the potential hidden danger determination result of the power distribution unit or when there is an abnormality in the working state of the power distribution unit.

[0012] Preferably, the production instruction matrix factor is generated based on the real-time electricity price gradient and the output production instruction, including: When the production instruction is generated, the corresponding production instruction matrix factor is extracted from the production instruction matrix factor mapping table according to the production instruction.

[0013] Preferably, the load data includes: real-time load data of the area within the park corresponding to the production instruction, basic forecast load and historical load impact factors.

[0014] Preferably, the load demand fluctuation index is calculated using the load formula, and the specific calculation process is as follows: According to the load formula Calculate the load demand fluctuation index ,in Expressed as real-time load data, Expressed as the basic forecast load, Expressed as the historical load impact factor, Expressed as production instruction matrix factors.

[0015] Preferably, the power distribution unit matching for the areas in the park based on the load demand fluctuation index comprises: according to the load demand fluctuation index level, the corresponding number of power distribution units is allocated, and the power distribution units with more than 80% of the energy storage capacity are allocated first; when the total energy of the allocated power distribution units is less than the total production energy corresponding to the load demand fluctuation index, the power distribution units being charged are detected, and the charging duration of the power distribution units is predicted, and then the power distribution units with a charging duration less than the total charging duration of the allocated power distribution units are allocated; and the total energy is greater than the total production energy by linear programming minimization allocation cost.

[0016] Preferably, the instantaneous frequency of the current signal is analyzed by using the adaptive weight to obtain the hidden danger evaluation coefficient, and the specific analysis process is as follows: according to the adaptive formula the hidden danger evaluation coefficient is obtained , wherein represents the instantaneous frequency of the current signal, the frequency standard deviation threshold, represents the frequency fluctuation of the th acquisition point, represents the number corresponding to each real-time acquisition time point, , represents the total number of real-time acquisition time points, and respectively represent the adaptive weight factor corresponding to the instantaneous frequency of the current signal and the adaptive weight factor corresponding to the frequency fluctuation.

[0017] Preferably, the potential hidden danger execution result of the power distribution unit is judged, comprising: when the hidden danger evaluation coefficient is less than or equal to the hidden danger evaluation coefficient threshold, step 4 is executed; when the hidden danger evaluation coefficient is greater than the hidden danger evaluation coefficient threshold, the first power distribution unit is switched, and step 3 is executed again; when the hidden danger evaluation coefficient of the first power distribution unit is greater than the hidden danger evaluation coefficient threshold, the production machine corresponding to the production instruction is detected for abnormality; when the production machine has an abnormality, the warning prompt of step 5 is performed; when the production machine has no abnormality, the second power distribution unit is switched, and step 3 is executed again; when the hidden danger evaluation coefficient of the second power distribution unit is less than or equal to the hidden danger evaluation coefficient threshold, step 4 is performed; when the hidden danger evaluation coefficient of the second power distribution unit is greater than the hidden danger evaluation coefficient threshold, the warning prompt of step 5 is performed.

[0018] Preferably, the abnormality detection of the production machine corresponding to the production instruction comprises: based on the time sequence data of the production machine corresponding to the production instruction, the key production stage is segmented to extract time domain features, frequency domain features and trajectory features; a dynamic threshold template is established based on the historical normal period corresponding to the production instruction, a current period deviation is detected in real time by an unsupervised algorithm, and the abnormality detection of the production machine is completed.

[0019] Preferably, the state data, efficiency attenuation factor and environmental factor of the distribution unit are comprehensively calculated to obtain the working state index of the distribution unit, and the working state of the distribution unit is judged to be abnormal, including: A1, respectively calculating the results of multiplying the state data of the distribution unit with the efficiency attenuation factor and the environmental factor and performing weighted fusion, and using the calculation results as the working state index of the distribution unit.

[0020] A2. Compare the working status index of the distribution unit with the working status index threshold. When the working status index of the distribution unit is greater than or equal to the working status index threshold, it is determined that the working status of the distribution unit is abnormal and step 5 is executed. Otherwise, it is determined that the working status of the distribution unit is normal.

[0021] In a second aspect, the present application provides a control system for an energy storage device in an industrial park, including: a load demand fluctuation index acquisition module, which generates a production instruction matrix factor based on a real-time electricity price gradient and pre-acquired production instructions, and then obtains the load data corresponding to the production instructions, thereby using a load formula to calculate the load demand fluctuation index.

[0022] The distribution unit matching module matches the distribution units of the areas within the park based on the load demand fluctuation index.

[0023] The distribution unit hidden danger judgment module is used to collect the instantaneous frequency of the current signal in real time when the distribution unit is distributing power, analyze the instantaneous frequency of the current signal using adaptive weights, and obtain the hidden danger assessment coefficient, so as to judge the execution results of the potential hidden dangers of the distribution unit.

[0024] The distribution unit working status judgment module is used to comprehensively calculate the status data, efficiency attenuation factor and environmental factor of the distribution unit to obtain the working status index of the distribution unit and make abnormal judgments on the working status of the distribution unit.

[0025] The early warning terminal issues an early warning based on the potential hidden danger judgment results of the distribution unit or when there is an abnormality in the working status of the distribution unit.

[0026] The beneficial effects of the present application are: 1. The control method and system of the industrial park energy storage device provided by the present application are based on production plans and real-time electricity prices, dynamically perceive load demand fluctuations, and dynamically adjust the distribution of power distribution units accordingly; further, through real-time current signal analysis, potential risks are actively identified, ensuring the reliability of the power distribution unit risk analysis results; at the same time, the overall health status of the power distribution unit is evaluated comprehensively in multiple dimensions, providing a basis for maintenance and replacement decisions, solving the limitations existing in the current energy storage device control feasibility analysis; once a risk or abnormal state occurs, an early warning prompt is immediately given, improving the safety and reliability of the system; from demand calculation, resource matching and operation monitoring to abnormal early warning, a complete closed loop is formed, realizing intelligent, safe and efficient management of energy storage device control.

[0027] 2. The present application calculates the risk coefficient of the power distribution unit by multiplying the absolute value of the ratio of the dynamically updated instantaneous frequency of the current signal to the frequency standard deviation threshold value by the corresponding adaptive weight factor, and adding the product of the average value of the frequency fluctuation and the corresponding adaptive weight factor, improving the calculation accuracy, effectively improving the accuracy of the risk assessment result, more effectively reducing the data deviation in the evaluation process, and more accurately identifying potential risk points, thereby quantifying the possibility and severity of the risk, and ultimately substantially enhancing the reliability, objectivity and prediction ability of the overall risk assessment result, providing a more solid scientific basis for subsequent risk control decisions.

[0028] 3. The present application realizes precise evaluation and early warning of abnormality of the health status of the power distribution unit through multi-source data coupling modeling and dynamic threshold determination mechanism, and performs real-time dynamic analysis in the use of the power distribution unit, timely replaces the standby power distribution unit, and performs multiple detection, significantly improves the reliability of the power grid, reduces the operation and maintenance cost, and provides core data support for predictive maintenance of the smart grid. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any inventive labor.

[0030] Figure 1 The present method is a schematic diagram of the implementation step flow.

[0031] Figure 2 The present application is a schematic diagram of the system structure connection. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] See also Figure 1 As shown, the present application provides a control method for an energy storage device in an industrial park in the first aspect, including: step 1, generating a production instruction matrix factor based on the real-time electricity price gradient and pre-acquired production instructions, and then obtaining the load data corresponding to the production instructions, thereby using the load formula to calculate the load demand fluctuation index.

[0034] In a specific example, the production instruction matrix factor is generated based on the real-time electricity price gradient and the output production instruction, including: When the production instruction is generated, the corresponding production instruction matrix factor is extracted from the production instruction matrix factor mapping table according to the production instruction.

[0035] It should be noted that the output of the production instruction is represented by a triggering event, such as the control center issuing a production instruction.

[0036] It should be noted that the production instructions include the name, specifications, batch number, quantity, process procedure number, single production load usage, production operation time, production machine, instruction issuance time and instruction receiving department of the product to be produced, and then construct a production instruction matrix factor mapping table. The corresponding production instruction matrix factor is set according to the instruction type, such as 0.8 for a shutdown instruction, and the weight is dynamically adjusted to adapt to production changes.

[0037] It should be noted that the standard electricity price is the average of historical electricity prices.

[0038] In a specific example, the load data includes: real-time load data of the area within the park corresponding to the production instruction, basic forecast load and historical load impact factors.

[0039] It should be noted that the real-time load data is the actual power demand value sampled from the current sensor, reflecting the current real-time energy consumption of the factory; the product of the quantity in the production instruction and the usage of a single production load is used as the basic predicted load; the historical load impact factor is a constant factor multiplied by the uncorrected measurement result to compensate for the error of the load data, and is specifically calculated as the variance value of the average value of several conforming data of the same instruction type.

[0040] In a specific example, the load demand fluctuation index is calculated using the load formula. The specific calculation process is as follows: According to the load formula Calculate the load demand fluctuation index ,in Expressed as real-time load data, Expressed as the basic forecast load, Expressed as the historical load impact factor, Expressed as production instruction matrix factors.

[0041] It should be noted that .

[0042] Step 2: Based on the load demand fluctuation index, matching distribution units for areas within the park;

[0043] In a specific example, the distribution units in the area within the park are matched based on the load demand fluctuation index, including: allocating the corresponding number of distribution units according to the load demand fluctuation index level, first allocating distribution units with energy storage capacity greater than eighty percent; when the total energy of the allocated distribution units is less than the total production energy corresponding to the load demand fluctuation index, detecting the distribution units that are being charged, and predicting the charging time of the distribution units, and then allocating distribution units with a charging time less than the total charging time of the allocated distribution units; minimizing the allocation cost through linear programming so that the total energy is greater than the total production energy.

[0044] It should be noted that the energy storage capacity is the ratio of the current storage capacity to the rated storage capacity.

[0045] It should be noted that according to the comparison formula The load demand fluctuation index level is obtained, and the load demand fluctuation index level includes low, medium and high.

[0046] It should be noted that a low level of load demand fluctuation index indicates that the energy demand corresponding to the load demand fluctuation in this area within the park is low, a medium level of load demand fluctuation index indicates that the energy demand corresponding to the load demand fluctuation in this area within the park is medium, and a high level of load demand fluctuation index indicates that the energy demand corresponding to the load demand fluctuation in this area within the park is high.

[0047] It should be noted that It is expressed as the lower limit of the load demand fluctuation index, Expressed as the upper limit value of the load demand fluctuation index.

[0048] It should be noted that the corresponding number of power distribution units is allocated according to the load demand fluctuation index level; the order from high to low according to the load demand fluctuation index level is used to allocate the corresponding number of power distribution units to the areas in the park; first, the area with a high load demand fluctuation index level is allocated, and when the allocation of the area with a high load demand fluctuation index level is completed, the area with a medium load demand fluctuation index level is allocated, and when the allocation of the area with a medium load demand fluctuation index level is completed, the area with a low load demand fluctuation index level is allocated.

[0049] It should be noted that the charging duration of the power distribution unit is predicted by the calculation formula The charging duration of the power distribution unit is predicted , wherein represents the current charging state, represents the charging rate of the power distribution unit, represents the charging duration prediction function, which is obtained based on historical data training, the historical data being all valid data of the charging duration, the charging rate and the charging state of the power distribution unit in a preset collection duration before the collection time point, and the rated charging duration, the charging rate and the charging state of the power distribution unit being used as a verification set, and then the charging duration prediction function of the power distribution unit is obtained through training and optimization.

[0050] It should be noted that the linear programming minimizes the allocation cost while satisfying the energy constraint, wherein the specific constraint conditions of the energy constraint are as follows: the first constraint condition is that the energy storage amount of the power distribution unit is greater than eighty percent; the second constraint condition is that the sum of the current storage limits of each power distribution unit based on the total number of power distribution units is greater than the total production energy.

[0051] Step 3, when the power distribution unit is power distribution, the instantaneous frequency of the current signal is collected in real time, the instantaneous frequency of the current signal is analyzed by using adaptive weight, and the hidden danger evaluation coefficient is obtained, so as to perform potential hidden danger execution result judgment on the power distribution unit.

[0052] In one specific example, the instantaneous frequency of the current signal is analyzed by using adaptive weight, and the hidden danger evaluation coefficient is obtained, and the specific analysis process is as follows: according to the adaptive formula The hidden danger evaluation coefficient is obtained , wherein represents the instantaneous frequency of the current signal, the frequency standard deviation threshold, represents the frequency fluctuation of the th collection point, represents the corresponding number of each real-time collection time point, represents the total number of real-time collection time points, and ​respectively represent adaptive weight factors corresponding to the instantaneous frequency of the current signal and adaptive weight factors corresponding to the frequency fluctuation.

[0053] It should be noted that Also represented as the current acquisition time point.

[0054] It should be noted that the adaptive weight factors corresponding to the instantaneous frequency of the current signal and the adaptive weight factors corresponding to the frequency fluctuation are obtained by factor analysis method, first the information condensation of the instantaneous frequency and the frequency fluctuation of the current signal is carried out, then the variance explained rate after rotation is obtained, and the weight is obtained by accumulating the variance explained rate.

[0055] It should be noted that the instantaneous frequency and the frequency fluctuation of the current signal are updated in real time, and the adaptive weight factors corresponding to the instantaneous frequency of the current signal and the adaptive weight factors corresponding to the frequency fluctuation are also updated in real time.

[0056] It should be noted that the factor analysis method is a known technology, which is a multivariate statistical analysis method that reduces some variables with complex relationships to a few comprehensive factors from the dependent relationship of internal correlation of variables; information condensation is represented as the calculation of the median; variance explained rate is the information amount of factor extraction, variance explained rate = eigenvalue / total analysis item number; variance explained rate after rotation represents the variance explained rate of the factor after maximum variance rotation.

[0057] In one specific example, the potential hazard execution result determination of the power distribution unit is performed, including:

[0058] When the hazard evaluation coefficient is less than or equal to the hazard evaluation coefficient threshold value, step 4 is performed; when the hazard evaluation coefficient is greater than the hazard evaluation coefficient threshold value, the first power distribution unit is switched, step 3 is performed again, when the hazard evaluation coefficient of the first power distribution unit is greater than the hazard evaluation coefficient threshold value, the production machine corresponding to the production instruction is detected for abnormality, when the production machine has an abnormality, step 5 is performed for early warning prompt; when the production machine has no abnormality, the second power distribution unit is switched, step 3 is performed again, when the hazard evaluation coefficient of the second power distribution unit is less than or equal to the hazard evaluation coefficient threshold value, step 4 is performed; when the hazard evaluation coefficient of the second power distribution unit is greater than the hazard evaluation coefficient threshold value, step 5 is performed for early warning prompt.

[0059] It should be noted that the first distribution unit and the second distribution unit are backup distribution units; the first distribution unit is used to distribute power when the hidden danger assessment coefficient is greater than the hidden danger assessment coefficient threshold; when the first distribution unit distributes power, and the hidden danger assessment coefficient of the first distribution unit is greater than the hidden danger assessment coefficient threshold, the production machine corresponding to the production instruction is detected for abnormality. When there is an abnormality in the production machine, it indicates that the production machine is abnormal and the distribution unit is not abnormal, and power distribution is stopped; when there is no abnormality in the production machine, it indicates that the production machine is not abnormal and the distribution unit is abnormal, and the second distribution unit is switched to distribute power.

[0060] It should be noted that when the hidden danger assessment coefficient of the first power distribution unit is less than or equal to the hidden danger assessment coefficient threshold, step 4 is performed.

[0061] In a specific example, the performing abnormality detection on the production machine corresponding to the production instruction includes:

[0062] Based on the time series data of the production machines corresponding to the production instructions, the key production stages are segmented to extract time domain features, frequency domain features and trajectory features. Based on the historical normal cycles corresponding to the production instructions, a dynamic threshold template is established, and the current cycle deviation is detected in real time through an unsupervised algorithm to complete the anomaly detection of the production machines.

[0063] It should be noted that based on the time series data of the production machine corresponding to the production instruction, the key production stages are segmented and the time domain features, frequency domain features and trajectory features are extracted; there is a corresponding operation process for the production instruction, and the key production stages of the production machine are segmented according to the operation process. For example, if the production instruction is to drill a 3mm hole in the center, the 3mm drill bit of the production machine is moved to a fixed position to operate; then the time domain features, frequency domain features and trajectory features of the production machine in this operation process are extracted.

[0064] It should be noted that the current cycle deviation is detected in real time through an unsupervised algorithm. When the characteristic deviation is output, there is an abnormality in the production machine; otherwise, there is no abnormality in the production machine.

[0065] It should be noted that time series data includes vibration, current, and temperature; time domain features include the mean, peak value, and variance of each stage, for example, a sudden increase in current during the processing stage indicates abnormal load; frequency domain features include the spectrum energy of a specific stage, for example, a sudden increase in energy in the bearing fault frequency band; trajectory features include the position and speed deviation of the moving axis, etc.; unsupervised algorithms include dynamic time warping; current cycle deviation, for example, a slight offset indicates gradual wear, such as a slow increase in current.

[0066] This application collects the instantaneous frequency of the current signal, multiplies the absolute value of the ratio of the dynamically updated instantaneous frequency of the current signal to the frequency standard deviation threshold by the corresponding adaptive weight factor, and adds the result to the product of the average value of the frequency fluctuation and the corresponding adaptive weight factor to calculate the hidden danger coefficient of the distribution unit. While improving the calculation accuracy, it effectively improves the accuracy of the hidden danger assessment results, more effectively reduces the data deviation in the assessment process, and thus more accurately identifies potential risk points, thereby quantifying the possibility of hidden dangers and the severity of consequences, and ultimately substantially enhances the reliability, objectivity and predictive ability of the overall hidden danger assessment results, providing a more solid scientific basis for subsequent risk management and control decisions.

[0067] Step 4: Perform weighted fusion on the status data, efficiency attenuation factor and environmental factor of the distribution unit to obtain the working status index of the distribution unit, and make an abnormal judgment on the working status of the distribution unit.

[0068] In a specific example, the state data, efficiency attenuation factor and environmental factor of the distribution unit are comprehensively calculated to obtain the working state index of the distribution unit, and the working state of the distribution unit is judged to be abnormal, including: A1, respectively calculating the results of multiplying the state data of the distribution unit with the efficiency attenuation factor and the environmental factor and performing weighted fusion, and using the calculation results as the working state index of the distribution unit.

[0069] It should be noted that the status data of the distribution unit is 1 minus the absolute value of the ratio of actual output energy to the required output energy. This data reflects the actual performance deviation of the distribution unit. The efficiency attenuation factor is obtained based on the analysis of the usage time and historical fault data of the distribution unit. Specifically, it is the product of the absolute value of the ratio of usage time to rated usage time and the number of historical faults. The larger the value, the greater the efficiency drop caused by equipment aging. The absolute values ​​of the standard ratios of external environmental parameters such as temperature, humidity and wind speed are added together as the environmental factor.

[0070] A2. Compare the working status index of the distribution unit with the working status index threshold. When the working status index of the distribution unit is greater than or equal to the working status index threshold, it is determined that the working status of the distribution unit is abnormal and step 5 is executed. Otherwise, it is determined that the working status of the distribution unit is normal.

[0071] Step 5: Provide an early warning based on the potential hidden danger determination result of the power distribution unit or when there is an abnormality in the working state of the power distribution unit.

[0072] It should be noted that, based on the result of the execution of the potential hidden dangers of the power distribution unit, an early warning is issued; and when there is an abnormality in the working state of the power distribution unit, an early warning is issued.

[0073] This application achieves accurate assessment of the health status of distribution units and early warning of abnormalities through multi-source data coupling modeling and dynamic threshold judgment mechanism, and conducts real-time dynamic analysis during the use of distribution units, timely replaces spare distribution units, and conducts multiple tests, which significantly improves power grid reliability, reduces operation and maintenance costs, and provides core data support for predictive maintenance of smart grids.

[0074] See also Figure 2 As shown, the present application provides a control system for an industrial park energy storage device in the second aspect, including: a load demand fluctuation index acquisition module, which generates a production instruction matrix factor based on the real-time electricity price gradient and pre-acquired production instructions, and then obtains the load data corresponding to the production instructions, thereby using the load formula to calculate the load demand fluctuation index.

[0075] The distribution unit matching module matches the distribution units of the areas within the park based on the load demand fluctuation index.

[0076] The distribution unit hidden danger judgment module is used to collect the instantaneous frequency of the current signal in real time when the distribution unit is distributing power, analyze the instantaneous frequency of the current signal using adaptive weights, and obtain the hidden danger assessment coefficient, so as to judge the execution results of the potential hidden dangers of the distribution unit.

[0077] The distribution unit working status judgment module is used to comprehensively calculate the status data, efficiency attenuation factor and environmental factor of the distribution unit to obtain the working status index of the distribution unit and make abnormal judgments on the working status of the distribution unit.

[0078] The early warning terminal issues an early warning based on the potential hidden danger judgment results of the distribution unit or when there is an abnormality in the working status of the distribution unit.

[0079] This application discloses a control method and system for an energy storage device in an industrial park, which relates to the field of energy storage device control technology. Based on production plans and real-time electricity prices, this application dynamically senses load demand fluctuations and dynamically adjusts the distribution of distribution units accordingly. It further actively identifies potential hidden dangers through real-time current signal analysis, ensuring the reliability of the hidden danger analysis results of the distribution units. At the same time, it comprehensively evaluates the overall health status of the distribution units using multi-dimensional data, providing a basis for maintenance and replacement decisions, and solving the limitations of the current feasibility analysis of energy storage device control. Once a hidden danger or abnormal state occurs, an early warning prompt is immediately issued, improving the safety and reliability of the system. A complete closed loop is formed from demand calculation, resource matching and operation monitoring to abnormality early warning, realizing intelligent, safe and efficient management of energy storage device control.

[0080] In the several embodiments provided by the present invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0081] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0082] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0083] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0084] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A control method for an energy storage device in an industrial park, characterized in that: include: Step 1: Based on the real-time electricity price gradient and pre-acquired production instructions, a production instruction matrix factor is generated, and then the load data corresponding to the production instruction is obtained, so as to calculate the load demand fluctuation index using the load formula; The load demand fluctuation index is calculated using the load formula, and the specific calculation process is as follows: According to the load formula Calculate the load demand fluctuation index ,in Expressed as real-time load data, Expressed as the basic forecast load, Expressed as the historical load impact factor, Expressed as production instruction matrix factors; Step 2: Based on the load demand fluctuation index, matching distribution units for areas within the park; Step 3: When the power distribution unit distributes power, the instantaneous frequency of the current signal is collected in real time, and the instantaneous frequency of the current signal is analyzed using an adaptive weight to obtain a hidden danger assessment coefficient, thereby determining the execution result of the potential hidden danger of the power distribution unit; The instantaneous frequency of the current signal is analyzed using adaptive weights to obtain a hidden danger assessment coefficient. The specific analysis process is as follows: According to the adaptive formula Obtain hidden danger assessment coefficient ,in Expressed as the instantaneous frequency of the current signal, Frequency standard deviation threshold, Expressed as The frequency fluctuation of each acquisition point, Indicates the number corresponding to each real-time acquisition time point, , Expressed as the total number of real-time acquisition time points, and They are respectively represented as the adaptive weight factor corresponding to the instantaneous frequency of the current signal and the adaptive weight factor corresponding to the frequency fluctuation; Step 4: Comprehensively calculate the status data, efficiency attenuation factor, and environmental factor of the power distribution unit to obtain the working status index of the power distribution unit, and make an abnormal judgment on the working status of the power distribution unit; Step 5: Provide an early warning based on the potential hidden danger determination result of the power distribution unit or when there is an abnormality in the working state of the power distribution unit.

2. The control method of an industrial park energy storage device according to claim 1, characterized in that: The production instruction matrix factors are generated based on the real-time electricity price gradient and the output production instructions, including: when When the production instruction is generated, the corresponding production instruction matrix factor is extracted from the production instruction matrix factor mapping table according to the production instruction.

3. The control method of an industrial park energy storage device according to claim 1, characterized in that: The load data includes: Real-time load data, basic forecast load and historical load influencing factors of the area within the park corresponding to the production instructions.

4. The control method of an industrial park energy storage device according to claim 1, characterized in that: The matching of power distribution units for areas within the park based on the load demand fluctuation index includes: According to the level of load demand fluctuation index, the corresponding number of distribution units is allocated, and the distribution units with energy storage capacity greater than 80% are allocated first; when the total energy of the allocated distribution units is less than the total production energy corresponding to the load demand fluctuation index, the distribution units that are being charged are detected and the charging time of the distribution units is predicted, and then the distribution units with charging time less than the total charging time of the allocated distribution units are allocated; through linear programming, the allocation cost is minimized so that the total energy is greater than the total production energy.

5. The control method of an industrial park energy storage device according to claim 1, characterized in that: The determination of the execution result of the potential hidden danger of the power distribution unit includes: When the hidden danger assessment coefficient is less than or equal to the hidden danger assessment coefficient threshold, execute step 4; when the hidden danger assessment coefficient is greater than the hidden danger assessment coefficient threshold, switch the first distribution unit and execute step 3 again. When the hidden danger assessment coefficient of the first distribution unit is greater than the hidden danger assessment coefficient threshold, perform abnormality detection on the production machine corresponding to the production instruction, and perform the early warning prompt of step 5 when there is an abnormality in the production machine; when there is no abnormality in the production machine, switch the second distribution unit and execute step 3 again. When the hidden danger assessment coefficient of the second distribution unit is less than or equal to the hidden danger assessment coefficient threshold, execute step 4; when the hidden danger assessment coefficient of the second distribution unit is greater than the hidden danger assessment coefficient threshold, perform the early warning prompt of step 5.

6. The control method of an industrial park energy storage device according to claim 5, characterized in that: The abnormality detection of the production machine corresponding to the production instruction includes: Based on the time series data of the production machines corresponding to the production instructions, the key production stages are segmented to extract time domain features, frequency domain features and trajectory features. Based on the historical normal cycles corresponding to the production instructions, a dynamic threshold template is established, and the current cycle deviation is detected in real time through an unsupervised algorithm to complete the anomaly detection of the production machines.

7. The control method of an industrial park energy storage device according to claim 1, characterized in that: The comprehensive calculation of the state data, efficiency attenuation factor and environmental factor of the power distribution unit to obtain the working state index of the power distribution unit and abnormality judgment of the working state of the power distribution unit includes: A1. Calculate the results of multiplying the status data of the distribution unit by the efficiency attenuation factor and the environmental factor, perform weighted fusion, and use the calculated results as the working status index of the distribution unit; A2. Compare the working status index of the distribution unit with the working status index threshold. When the working status index of the distribution unit is greater than or equal to the working status index threshold, it is determined that there is an abnormality in the working status of the distribution unit and step 5 is executed. Otherwise, it is determined that there is no abnormality in the working status of the distribution unit.

8. A control system for executing the industrial park energy storage device according to any one of claims 1 to 7, characterized in that: include: The load demand fluctuation index acquisition module generates a production instruction matrix factor based on the real-time electricity price gradient and pre-acquired production instructions, and then obtains the load data corresponding to the production instructions, thereby calculating the load demand fluctuation index using the load formula; A distribution unit matching module, which matches distribution units for areas within the park based on the load demand fluctuation index; The power distribution unit hidden danger determination module is used to collect the instantaneous frequency of the current signal in real time when the power distribution unit is distributing power, analyze the instantaneous frequency of the current signal using adaptive weights, and obtain the hidden danger assessment coefficient, thereby determining the execution result of the potential hidden danger of the power distribution unit; The distribution unit working state judgment module is used to comprehensively calculate the status data, efficiency attenuation factor and environmental factor of the distribution unit to obtain the working state index of the distribution unit and make abnormal judgments on the working state of the distribution unit; The early warning terminal issues an early warning based on the potential hidden danger judgment results of the distribution unit or when there is an abnormality in the working status of the distribution unit.

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