An energy storage power supply remote management system based on the internet of things

Abstract

The present application relates to power supply management technical field, especially to a kind of energy storage power remote management system based on Internet of Things, comprising: data acquisition module;With data state determines to execute data comparison analysis or abnormal sub-region analysis data analysis module;With the first analysis module based on the proportion of abnormal data of analysis data and abnormal time reference value determines frequency deviation risk degree;With the second analysis module based on abnormal sub-region quantity and abnormal sub-region distribution coefficient determines frequency deviation risk degree;With optimization determination module for determining whether energy storage power optimization is carried out for the current monitoring period according to frequency deviation risk degree;With strategy execution module for determining the number of collaborative energy storage power according to frequency deviation risk degree, and based on relative deviation value, the climbing degree of collaborative energy storage power is adjusted under preset instability condition.The present application can improve the reliability of power grid operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply management, and in particular to a remote management system for energy storage power supply based on Internet of Things. BACKGROUND

[0002] With the rapid increase in the number of energy storage power supplies, traditional manual inspection has been difficult to capture all kinds of transient risks in real time. By relying on Internet of Things technology, high-frequency sampling terminals can be deployed around each energy storage power supply and its surrounding environment to realize real-time collection of multi-dimensional monitoring data. However, in actual operation, energy storage power supplies are continuously disturbed by factors such as sudden changes in local load, internal resistance rise caused by battery aging, and other factors, which can easily cause dynamic deviation of power grid frequency. Therefore, how to accurately identify the frequency deviation risk of energy storage power supply to improve the reliability of power grid operation is a problem to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN109524974A discloses a power grid primary frequency control method and system based on battery energy storage power supply. The method includes: first, real-time monitoring of power grid frequency, and real-time calculation of power grid frequency deviation absolute value based on the rated value of power grid frequency. If the power grid frequency deviation absolute value is greater than or equal to the preset power grid frequency stability threshold, the output model of the energy storage power supply is determined based on the power grid frequency deviation change rate. Finally, when the power grid frequency deviation absolute value stabilizes at the steady-state frequency deviation value, the frequency regulation ends. However, the above-mentioned scheme has the following problems: the adjustment is only started when the frequency deviation reaches the threshold, and the frequency deviation risk cannot be accurately evaluated and responded to in combination with multi-dimensional information, resulting in poor reliability of power grid operation. SUMMARY

[0004] Therefore, the present application provides a remote management system for energy storage power supply based on Internet of Things to overcome the problem that the prior art cannot accurately evaluate and respond to the frequency deviation risk in combination with multi-dimensional information, resulting in poor reliability of power grid operation.

[0005] To achieve the above-mentioned purpose, the present application provides a remote management system for energy storage power supply based on Internet of Things, comprising:

[0006] The data acquisition module is used to collect storage data and monitoring data of the target monitoring period;

[0007] The data analysis module is connected to the data acquisition module and is used to determine the data state according to the power grid frequency deviation fluctuation value and the performance degradation degree, and to determine the execution of data comparison analysis or abnormal sub-region analysis according to the data state;

[0008] a first analysis module connected with the data acquisition module and the data analysis module, configured to determine, in the data comparison analysis, analysis data according to a data correlation degree or a characteristic parameter similarity based on a data correlation mean value, and determine a frequency deviation risk degree based on an abnormal data proportion of the analysis data and an abnormal time reference value;

[0009] a second analysis module connected with the data acquisition module and the data analysis module, configured to determine, in the abnormal sub-region analysis, an abnormal sub-region according to a power comparison value or a power change rate based on a power comparison value, and determine the frequency deviation risk degree based on an abnormal sub-region quantity and an abnormal sub-region distribution coefficient;

[0010] an optimization determination module connected with the first analysis module and the second analysis module, configured to determine whether to perform energy storage power optimization for a current monitoring period according to the frequency deviation risk degree;

[0011] a strategy execution module connected with the optimization determination module, configured to determine a number of cooperative energy storage powers according to the frequency deviation risk degree when performing the energy storage power optimization for the current monitoring period, and adjust a climbing degree of the cooperative energy storage powers based on a relative deviation value under a preset instability condition.

[0012] Further, the data analysis module performs the abnormal sub-region analysis in response to a data state that a power grid frequency deviation fluctuation value is less than a preset power grid frequency deviation fluctuation value and a performance attenuation degree is less than a preset performance attenuation degree.

[0013] Further, the data analysis module performs the data comparison analysis in response to a data state that the power grid frequency deviation fluctuation value is greater than or equal to the preset power grid frequency deviation fluctuation value or the performance attenuation degree is greater than or equal to the preset performance attenuation degree.

[0014] Further, the performance attenuation degree confirmation manner includes:

[0015] if the internal resistance growth rate is greater than or equal to a preset internal resistance growth rate, determining the performance attenuation degree according to a SOH drop amplitude;

[0016] if the internal resistance growth rate is less than the preset internal resistance growth rate, determining the performance attenuation degree according to a SOH drop comparison degree.

[0017] Further, the first analysis module determines the analysis data according to the data correlation degree or the characteristic parameter similarity based on the data correlation mean value, including:

[0018] the first analysis module determines the analysis data according to the data correlation degree in response to the data correlation mean value being greater than or equal to a preset data correlation mean value;

[0019] The first analysis module selects analysis data according to the feature parameter similarity in response to the data correlation mean value being less than a preset data correlation mean value.

[0020] Further, the first analysis module determines the frequency deviation risk degree based on the abnormal data proportion of the analysis data and the abnormal time reference value;

[0021] The frequency deviation risk degree and the abnormal data proportion are in a positive correlation relationship, and the frequency deviation risk degree and the abnormal time reference value are in a negative correlation relationship.

[0022] Further, the second analysis module determines the abnormal sub-region according to the power comparison value or the power change rate according to the power comparison value, comprising:

[0023] The second analysis module determines the abnormal sub-region according to the power comparison value in response to the power comparison value being greater than or equal to a preset power comparison value;

[0024] The second analysis module determines the abnormal sub-region according to the power change rate in response to the power comparison value being less than a preset power comparison value.

[0025] Further, the optimization judgment module performs energy storage power optimization for the current monitoring period in response to the frequency deviation risk degree being greater than or equal to a preset frequency deviation risk degree.

[0026] Further, the strategy execution module determines the number of collaborative energy storage power sources according to the frequency deviation risk degree;

[0027] The number of collaborative energy storage power sources and the frequency deviation risk degree are in a positive correlation relationship.

[0028] Further, the strategy execution module increases the climbing degree of the collaborative energy storage power source based on the relative deviation value under a preset instability condition;

[0029] The preset instability condition is that the relative deviation value is less than a preset relative deviation value, and the increase value of the climbing degree and the relative deviation value are in a positive correlation relationship.

[0030] Compared with the prior art, the beneficial effects of the present application are that in the technical scheme of the present application, the power grid frequency deviation fluctuation value and the performance degradation degree effectively reflect the comprehensive state of the power grid operation stability and the energy storage power performance health level, and then the data state is determined to perform data comparison analysis or abnormal sub-region analysis, so that the analysis method and the actual application scene are accurately matched, which is beneficial to improve the pertinence and efficiency of risk identification, and then provides accurate basis for the formulation of energy storage power optimization strategy, and then improves the reliability of power grid operation.

[0031] Further, in the present application, the overall correlation strength between the monitoring data and the stored data is effectively reflected by the data correlation mean value, and then the analysis data is selected according to the data correlation degree or the characteristic parameter similarity according to the data correlation mean value, so that the selected analysis data can not only consider the consistency of multiple parameters when the overall correlation is strong, but also focus on the matching of the core characteristic parameters when the overall correlation is weak, avoiding irrelevant or weakly correlated data interference, and thus improving the accuracy and efficiency of subsequent abnormal data identification and frequency deviation risk assessment, and providing a more reliable data analysis basis for power system frequency stability control.

[0032] Further, in the present application, the deviation amplitude of the actual power consumption intensity of the sub-region from the historical normal level is effectively reflected by the power comparison value, and then the abnormal sub-region is determined according to the power comparison value or the power change rate according to the power comparison value, so that the determination of the abnormal sub-region can be dynamically adapted according to different scenes of power consumption characteristics, which is beneficial to accurately locking different types of frequency deviation hidden dangers, avoiding missed or false judgments caused by a single determination standard, and improving the pertinence and reliability of risk early warning.

[0033] Further, in the present application, the number of cooperative energy storage power sources is determined according to the frequency deviation risk degree, which can ensure that the total regulation power of the cooperative energy storage power sources matches the frequency deviation risk scale currently faced by the power grid, and through the cooperative charging and discharging of a sufficient number of energy storage power sources, the continuous deviation of the frequency from the rated value caused by insufficient regulation power is avoided, and the climbing degree of the cooperative energy storage power sources is increased based on the relative deviation value under the preset instability condition, which is beneficial to compensating for the immediate gap in regulation capacity by improving the climbing degree when the number of energy storage is insufficient relative to the risk degree, avoiding the loss of frequency control due to response lag, and improving the reliability and timeliness of power grid frequency stability control. BRIEF DESCRIPTION OF DRAWINGS

[0034] Fig. 1 It is a module connection diagram of the present application based on the Internet of Things energy storage power remote management system;

[0035] Fig. 2 It is a flowchart of the present application for determining whether to perform data comparison analysis or abnormal sub-region analysis according to the data state;

[0036] Fig. 3 It is a flowchart of the present application for determining whether to perform data comparison analysis or abnormal sub-region analysis according to the data state;

[0037] Fig. 4 It is a flowchart of the present application for determining whether to perform data comparison analysis or abnormal sub-region analysis according to the data state; DETAILED DESCRIPTION

[0038] In order to make the objects and advantages of the present application more clear, the present application will be further described below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0039] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that the embodiments are only used to explain the technical principles of the present application and are not used to limit the protection scope of the present application.

[0040] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0041] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through an intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0042] Please refer to Figs. 1 to 4 The present application provides a kind of energy storage power supply remote management system based on Internet of Things, comprising:

[0043] Data acquisition module is used to collect storage data and monitoring data of target monitoring period;

[0044] Data analysis module is connected with the data acquisition module, to determine data state according to power grid frequency deviation fluctuation value and performance attenuation degree, and to determine to execute data comparison analysis or abnormal sub-region analysis according to data state;

[0045] First analysis module is connected with the data acquisition module and the data analysis module respectively, to determine to select analysis data according to data correlation degree or characteristic parameter similarity based on data correlation mean in data comparison analysis, and to determine frequency deviation risk degree based on abnormal data proportion of analysis data and abnormal time reference value;

[0046] a second analysis module connected with the data acquisition module and the data analysis module respectively, configured to determine an abnormal sub-region according to the power comparison value or the power change rate in the abnormal sub-region analysis, and determine the frequency deviation risk degree based on the number of abnormal sub-regions and the abnormal sub-region distribution coefficient;

[0047] an optimization determination module connected with the first analysis module and the second analysis module respectively, configured to determine whether to perform energy storage power optimization for the current monitoring period according to the frequency deviation risk degree;

[0048] a strategy execution module connected with the optimization determination module, configured to determine the number of cooperative energy storage powers according to the frequency deviation risk degree when the energy storage power optimization is performed for the current monitoring period, and adjust the climbing degree of the cooperative energy storage powers based on the relative deviation value under the preset instability condition.

[0049] The application scenario of the present application is risk prediction and optimization in the energy storage power supply process, and the present application is provided with a plurality of energy storage powers, which are connected in parallel to form an energy storage power cluster, and when the power grid frequency fluctuates, the energy storage power cluster can immediately adjust its output or input power to stabilize the power grid frequency.

[0050] The present application is provided with a plurality of historical records, and each historical record records the power grid frequency deviation fluctuation value, performance attenuation degree, period drop degree, SOH drop amplitude and SOH drop comparison degree in the historical process of risk prediction and optimization in the energy storage power supply process, and each historical record corresponds to a qualified mark, which records whether the process of risk prediction and optimization in the energy storage power supply process meets the user's demand, and the qualified mark can be recorded manually. It can be understood that the user can determine whether the process of risk prediction and optimization in the energy storage power supply process meets the demand according to the self-set index, and the self-set index can be but is not limited to the number of abnormalities, which is not described here. The number of abnormalities is the number of times when the power grid frequency deviation is greater than 0.2 Hz; the power grid frequency deviation = | actual power grid frequency - rated frequency |, the rated frequency is 50 Hz, and the actual power grid frequency is determined by the zero-crossing method, which is a common technical means for those skilled in the art, and will not be described in detail.

[0051] The present application is provided with a continuous monitoring cycle, and the length of the monitoring cycle can be set according to the user's demand. The greater the user's demand for monitoring accuracy, the shorter the length of the monitoring cycle. A monitoring cycle value is provided, and the monitoring cycle is 30 min. A time point setting method is provided, taking the starting time of the target monitoring cycle as the starting point, setting an interval point every 1 min, and recording the starting point and each interval point as a time point.

[0052] The target monitoring period is a monitoring period adjacent to the current monitoring period and earlier in time than the current monitoring period.

[0053] The monitoring data are values of monitoring parameters corresponding to each time point in the target monitoring period, and the monitoring parameters include but are not limited to actual power grid frequency, internal resistance of the energy storage power cluster, temperature of the energy storage power cluster, charging power of the energy storage power cluster, and discharging power of the energy storage power cluster. The internal resistance of the energy storage power cluster is the average value of the internal resistances of each energy storage power in the energy storage power cluster, and the internal resistance of the energy storage power is measured by the pulse discharge method. The temperature of the energy storage power cluster is the average value of the temperatures of each energy storage power, and the temperature of the energy storage power is measured by a temperature sensor. The charging power of the energy storage power cluster is the average value of the charging powers of each energy storage power, and the discharging power of the energy storage power cluster is the average value of the discharging powers of each energy storage power. The charging power of the energy storage power is Ucharge x Icharge (the current flowing in is positive), and the discharging power of the energy storage power is Udischarge x Idischarge (the current flowing out is positive). Ucharge and Udischarge are the charging voltage and discharging voltage of the energy storage power, which are measured by a direct current voltage sensor. Icharge and Idischarge are the charging current and discharging current of the energy storage power, which are measured by a bidirectional Hall current sensor. This is a commonly used technical means for those skilled in the art and will not be described in detail.

[0054] In the present application, several storage data are included, and each storage data contains monitoring parameters corresponding to each time point in a single monitoring period and an abnormal time interval. The latest time of the monitoring period corresponding to the storage data is recorded as a target time, and the earliest time later than the target time and with a power grid frequency deviation greater than 0.2 Hz is recorded as a reference time. The abnormal time interval corresponding to the storage data is the time length between the target time and the reference time, with the unit being h.

[0055] Specifically, the data analysis module performs abnormal sub-region analysis in response to a data state in which the power grid frequency deviation fluctuation value is less than a preset power grid frequency deviation fluctuation value and the performance degradation degree is less than a preset performance degradation degree.

[0056] The data state includes a first data state and a second data state. The first data state is that the power grid frequency deviation fluctuation value is less than a preset power grid frequency deviation fluctuation value and the performance degradation degree is less than a preset performance degradation degree. The second data state is that the power grid frequency deviation fluctuation value is greater than or equal to a preset power grid frequency deviation fluctuation value or the performance degradation degree is greater than or equal to a preset performance degradation degree.

[0057] The power grid frequency deviation fluctuation value is the standard deviation of the power grid frequency deviation corresponding to each time point in the target monitoring period.

[0058] The preset power grid frequency deviation fluctuation value and the preset performance attenuation degree value can be determined by the user according to the actual application scene. The greater the preset power grid frequency deviation fluctuation value and the preset performance attenuation degree value, the greater the user's demand for executing abnormal sub-region analysis. A method for determining the preset power grid frequency deviation fluctuation value and the preset performance attenuation degree value is provided. The historical records of the user performing data comparison analysis are detected. The average value of the power grid frequency deviation fluctuation value and the average value of the performance attenuation degree corresponding to the historical records that can meet the user's demand are respectively recorded as the preset power grid frequency deviation fluctuation value and the preset performance attenuation degree.

[0059] It can be understood that the power grid frequency deviation fluctuation value and the performance attenuation degree can effectively reflect the comprehensive state of the power grid operation stability and the energy storage power performance health level, and accordingly divide the data state:

[0060] In the first data state, the power grid frequency fluctuation is gentle and the energy storage power attenuation is slight, and the system as a whole is in a stable normal operation state. At this time, the global frequency fluctuation risk is difficult to capture directly through monitoring data due to weak signals, and the recognition degree of implicit risks can be amplified through the differentiated characteristics of the focused regional power data, so as to avoid the spread of local abnormalities to global fluctuations through the coupling relationship of the power grid, and therefore execute abnormal sub-region analysis.

[0061] In the second data state, the power grid frequency fluctuation is significant or the energy storage power attenuation is obvious, and the system as a whole presents an outstanding abnormal tendency. At this time, the abnormal risk needs to be determined by mining related data, and therefore data comparison analysis is executed.

[0062] Specifically, the data analysis module executes data comparison analysis in response to the data state that the power grid frequency deviation fluctuation value is greater than or equal to the preset power grid frequency deviation fluctuation value or the performance attenuation degree is greater than or equal to the preset performance attenuation degree.

[0063] Specifically, the confirmation method of the performance attenuation degree includes:

[0064] If the internal resistance growth rate is greater than or equal to the preset internal resistance growth rate, the performance attenuation degree is determined according to the SOH drop amplitude;

[0065] If the internal resistance growth rate is less than the preset internal resistance growth rate, the performance attenuation degree is determined according to the SOH drop comparison degree.

[0066] Wherein, the internal resistance growth rate = (the internal resistance of the energy storage power cluster at the beginning of the target monitoring period - the internal resistance of the energy storage power cluster when the energy storage power cluster is first put into use) / the internal resistance of the energy storage power cluster when the energy storage power cluster is first put into use;

[0067] The preset internal resistance growth rate value can be determined by the user according to the actual application scene. The greater the preset internal resistance growth rate value, the greater the user's demand for determining the performance attenuation degree according to the SOH drop ratio. A preset internal resistance growth rate value determination method is provided. The historical records of the user determining the performance attenuation degree according to the SOH drop ratio are detected. The average value of the internal resistance growth rate corresponding to the historical records that can meet the user's demand is recorded as the preset internal resistance growth rate.

[0068] SOH corresponding to a single moment = (Re-Rc) / (Re-Rn), wherein Rn is the internal resistance of the energy storage power supply cluster when the energy storage power supply cluster is initially put into use, Rc is the internal resistance of the energy storage power supply cluster at the moment, and Re is the internal resistance threshold value when the life of the energy storage power supply cluster ends, Re = 200% * Rn.

[0069] SOH drop amplitude = SOH at the beginning of the target monitoring period-SOH when the energy storage power supply cluster is initially put into use;

[0070] SOH drop ratio = period drop degree-average value of the period drop degree corresponding to the historical records that do not need to perform energy storage power supply optimization and can meet the user's demand; period drop degree = SOH corresponding to the beginning of the target monitoring period-SOH corresponding to the end of the target monitoring period;

[0071] When the performance attenuation degree is determined according to the SOH drop amplitude, the performance attenuation degree = SOH drop amplitude / average value of the SOH drop amplitude corresponding to the historical records that can meet the user's demand;

[0072] When the performance attenuation degree is determined according to the SOH drop ratio, the performance attenuation degree = SOH drop ratio / average value of the SOH drop ratio corresponding to the historical records that can meet the user's demand;

[0073] It can be understood that the internal resistance growth rate effectively reflects the deterioration degree of the energy storage power supply cluster. When the internal resistance growth rate is greater than or equal to the preset internal resistance growth rate, it indicates that the internal structure of the energy storage power supply has obviously deteriorated, the internal resistance growth has entered a stage that needs to be focused on, and the overall performance attenuation is mainly manifested as long-term accumulated capacity loss. The SOH drop amplitude can directly reflect the severity of this long-term deterioration. When the internal resistance growth rate is less than the preset internal resistance growth rate, the internal structure deterioration is relatively slight, the internal resistance growth is in a flat stage, and the performance attenuation is more reflected in the fluctuation difference within a single monitoring period. The SOH drop ratio can accurately capture short-term abnormalities.

[0074] Specifically, the first analysis module determines to select analysis data according to data correlation degree or feature parameter similarity based on data correlation mean value, comprising:

[0075] The first analysis module responds when the average correlation value of the data is greater than or equal to the preset average correlation value of the data, and selects the analysis data based on the correlation degree of the data.

[0076] The first analysis module responds to data where the mean of the associated data is less than the preset mean of the associated data, and then selects analysis data based on the similarity of the feature parameters.

[0077] Among them, the average data correlation is the average of the data correlation degree between the monitoring data and each stored data, and the data correlation degree between a single monitoring data and a single stored data is the minimum value among the correlation reference values ​​corresponding to each monitoring parameter.

[0078] The formula for calculating the associated reference value r corresponding to a single monitoring parameter is:

[0079] ;

[0080] Where m is the number of time points in a single monitoring cycle; and These are the values ​​of the monitoring parameter at the k-th time point in a single monitoring data point and a single stored data point, respectively. This represents the average value of the monitoring parameter at each time point within a single monitoring dataset. The average value of the monitoring parameter at each time point in a single stored data set, k = 1, 2, 3, ..., m;

[0081] The value of the preset data association mean can be determined by the user according to the actual application scenario. The smaller the value of the preset data association mean, the greater the user's need to select and analyze data based on the data association degree. A method for determining the value of the preset data association mean is provided, which detects the historical records of users selecting and analyzing data based on the data association degree, and records the average value of the data association mean corresponding to the historical records that can meet the user's needs as the preset data association mean.

[0082] When selecting analysis data based on data correlation, stored data with a data correlation greater than the preset data correlation should be selected as analysis data.

[0083] When selecting analysis data based on feature parameter similarity, stored data with feature parameter similarity greater than the preset feature parameter similarity are selected as analysis data;

[0084] Users can determine the preset values ​​for data correlation and preset feature parameter similarity based on their actual application scenarios. The greater the user's need to improve the accuracy of the selected analytical data, the higher the values ​​of the preset data correlation and preset feature parameter similarity. One preset value for data correlation and preset feature parameter similarity is provided: preset data correlation is 0.7, and preset feature parameter similarity is 80%.

[0085] The similarity of a single stored data to a characteristic parameter corresponding to the monitoring data is an average of the fluctuation similarities of each characteristic parameter corresponding to the monitoring data and the stored data. For a single characteristic parameter and a single stored data, the fluctuation reference value of the characteristic parameter in the monitoring data is denoted as a1, the fluctuation reference value of the characteristic parameter in the stored data is denoted as a2, and the fluctuation similarity of a single characteristic parameter corresponding to the monitoring data and the stored data is = 1- |a1-a2| / (the larger value of a1 and a2); the fluctuation reference value of a single characteristic parameter in a single stored data or a single monitoring data is a standard deviation of the values of the characteristic parameter at each time point in the single stored data or the single monitoring data;

[0086] For a single stored data, if the abnormal time interval corresponding to the stored data is greater than or equal to the preset abnormal time interval, the stored data is recorded as normal stored data; if the abnormal time interval corresponding to the stored data is less than the preset abnormal time interval, the stored data is recorded as abnormal stored data;

[0087] The value of the preset abnormal time interval can be determined by the user according to the actual application scenario. The greater the value of the preset abnormal time interval, the greater the user's demand for determining the stored data as abnormal stored data. A method for determining the value of the preset abnormal time interval is provided. The maximum value of the abnormal time interval corresponding to each abnormal stored data in the historical record that can meet the user's demand is recorded as the preset abnormal time interval.

[0088] For a single monitoring parameter, if the deviation representation value corresponding to the monitoring parameter is greater than the preset deviation representation value, the monitoring parameter is recorded as a characteristic parameter.

[0089] The deviation representation value corresponding to a single monitoring parameter is = (mean deviation / preset mean deviation) + (fluctuation deviation / preset fluctuation deviation); the mean deviation is = | the average of the fluctuation reference values corresponding to the monitoring parameter in each abnormal stored data - the average of the fluctuation reference values corresponding to the monitoring parameter in each normal stored data |, the fluctuation deviation is = | the standard deviation of the fluctuation reference values corresponding to the monitoring parameter in each abnormal stored data - the standard deviation of the fluctuation reference values corresponding to the monitoring parameter in each normal stored data |, the preset mean deviation is the average of the deviation means corresponding to the monitoring parameter in each historical record that can meet the user's demand, and the preset fluctuation deviation is the average of the fluctuation deviations corresponding to the monitoring parameter in each historical record that can meet the user's demand.

[0090] The value of the preset deviation representation value can be determined by the user according to the actual application scenario. The smaller the value of the preset deviation representation value, the greater the user's demand for determining the monitoring parameter as a characteristic parameter. A method for determining the value of the preset deviation representation value is provided. The average of the deviation representation values corresponding to each characteristic parameter in the historical record that can meet the user's demand is recorded as the preset deviation representation value.

[0091] It can be understood that the data correlation average effectively reflects the overall correlation strength between the monitoring data and the stored data. When the data correlation average is greater than or equal to the preset data correlation average, it indicates that the overall correlation of the monitoring data and the stored data is greater. At this time, the analysis data is selected by the data correlation degree, which can ensure the overall matching of the analysis data and the monitoring data in the multi-parameter dimension. When the data correlation average is less than the preset data correlation average, it indicates that the overall correlation of the monitoring data and the stored data is smaller, and the overall trend matching significance is reduced. The similarity of the core characteristic parameters can better reflect the data essence, so the analysis data is selected by the characteristic parameter similarity, which can focus on the matching of the key characteristics and improve the pertinence of the screening.

[0092] Specifically, the first analysis module determines the frequency deviation risk degree based on the abnormal data proportion of the analysis data and the abnormal time reference value;

[0093] The frequency deviation risk degree and the abnormal data proportion are in a positive correlation relationship, and the frequency deviation risk degree and the abnormal time reference value are in a negative correlation relationship.

[0094] The abnormal data proportion is the number of abnormal stored data in the analysis data / the number of analysis data.

[0095] The abnormal time reference value is the average value of the abnormal time intervals corresponding to each abnormal stored data in the analysis data.

[0096] The frequency deviation risk degree=(abnormal data proportion / average value of abnormal data proportion corresponding to historical records capable of meeting user demand)+(1-abnormal time reference value / average value of abnormal time reference value corresponding to historical records capable of meeting user demand).

[0097] It can be understood that the abnormal data proportion and the abnormal time reference value effectively reflect the universality of the abnormal situation in the analysis data and the time proximity of the frequency deviation occurrence. Based on the abnormal data proportion and the abnormal time reference value of the analysis data, the frequency deviation risk degree can be determined, which can comprehensively quantify the frequency instability risk faced by the power grid from two dimensions, making the risk assessment more comprehensive and accurate, avoiding risk misjudgment caused by single-dimensional evaluation, and providing a more reliable risk quantification basis for subsequent formulation of energy storage adjustment strategies.

[0098] Specifically, the second analysis module determines the abnormal sub-region according to the power comparison value or the power change rate based on the power comparison value, including:

[0099] The second analysis module responds to the power comparison value greater than or equal to the preset power comparison value, and determines the abnormal sub-region according to the power comparison value;

[0100] The second analysis module determines the abnormal sub-region according to the power change rate in response to the power comparison value being less than the preset power comparison value.

[0101] The target area is divided into a plurality of adjacent and equal sub-areas, the greater the user's demand for improving monitoring accuracy, the greater the number of sub-areas, and a value of the number of divided sub-areas is provided, and the number of divided sub-areas is 50;

[0102] For a single sub-area, the power reference value corresponding to the sub-area is the average of the power consumption at each time point in the target monitoring period, the power change rate corresponding to the single sub-area is the standard deviation of the power consumption at each time point in the target monitoring period, the power consumption of the single sub-area at a single time point is the sum of the reading change amount of each power meter in the sub-area, the reading change amount of a single power meter is the power displayed on the power meter at the time point minus the power displayed on the power meter at a time point adjacent to the time point, and the unit of power consumption is kWh;

[0103] The value of the preset power reference value can be determined by the user according to the actual application scenario, the greater the user's demand for improving the early warning accuracy, the smaller the value of the preset power reference value, and a value of the preset power reference value is provided. The average of the power reference values of each normal sub-area in the historical record that can meet the user's demand is recorded as the preset power reference value; the normal sub-area is other sub-area outside the abnormal sub-area;

[0104] The power comparison value = |power reference value - preset power reference value|;

[0105] When the abnormal sub-area is determined according to the power comparison value, the sub-area with the power comparison value greater than the preset power comparison value is recorded as the abnormal sub-area;

[0106] When the abnormal sub-area is determined according to the power change rate, the sub-area with the power change rate greater than the preset power change rate is recorded as the abnormal sub-area;

[0107] The values of the preset power comparison value and the preset power change rate can be determined by the user according to the actual application scenario, the greater the user's demand for improving the risk early warning accuracy, the smaller the values of the preset power comparison value and the preset power change rate, and a value of the preset power comparison value and the preset power change rate is provided. The average of the power comparison degrees of each abnormal sub-area in the historical record that can meet the user's demand is recorded as the preset power comparison degree, and the average of the power change rates of each abnormal sub-area in the historical record that can meet the user's demand is recorded as the preset power change rate.

[0108] It can be understood that the power comparison value effectively reflects the deviation of the actual power intensity of the sub-region from the normal level, when the power reference value is greater than or equal to the preset power reference value, it indicates that the overall power intensity of the sub-region has reached or exceeded the historical normal level, at this time the deviation of the total power from the normal range directly reflects the potential power imbalance risk, therefore the abnormal sub-region is determined according to the power comparison value;

[0109] When the power reference value is less than the preset power reference value, it indicates that the overall power intensity of the sub-region is below the historical normal level, but there may be short-term load fluctuations. The sudden rise and fall of short-term load will cause rapid fluctuations of active demand, if the response speed of energy storage power source cannot keep up, it is easy to cause instantaneous oscillation of frequency, at this time the abnormal sub-region is determined by the power change rate, which can early warning of such dynamic imbalance risk.

[0110] When the frequency deviation risk degree is determined based on the number of abnormal sub-regions and the abnormal sub-region distribution coefficient, the frequency deviation risk degree = number of abnormal sub-regions / average value of number of abnormal sub-regions corresponding to historical records that can meet user demand + (1-abnormal sub-region distribution coefficient / average value of abnormal sub-region distribution coefficient corresponding to historical records that can meet user demand);

[0111] The number of abnormal sub-regions is the total amount of abnormal sub-regions in the target region;

[0112] The abnormal sub-region distribution coefficient is the average value of the distance reference value corresponding to each abnormal sub-region. For a single abnormal sub-region, the abnormal sub-region is recorded as a target abnormal sub-region, and other abnormal sub-regions outside the target abnormal sub-region are recorded as reference abnormal sub-regions. The distance reference value corresponding to the target abnormal sub-region is the average value of the shortest distance from the target abnormal sub-region to each reference abnormal sub-region. The shortest distance corresponding to the target abnormal sub-region and the reference abnormal sub-region is the length of the line connecting the center points of the two abnormal sub-regions. The midpoint of a single abnormal sub-region is the center of the circumscribed circle of the abnormal sub-region.

[0113] It can be understood that the number of abnormal sub-regions directly reflects the coverage and total impact strength of abnormal electricity consumption. The more the number is, the more extensive the abnormal electricity consumption sub-regions in the target region are, and the larger the overall load fluctuation is. Such a large-scale electricity consumption anomaly will increase the possibility of total power imbalance of the power grid, thereby increasing the risk of frequency deviation from the rated value. The abnormal sub-region distribution coefficient reflects the spatial distribution characteristics of the abnormal region. The smaller the abnormal sub-region distribution coefficient is, the more concentrated the abnormal sub-regions are, which will cause a sharp fluctuation of the local power grid load and may cause local power imbalance and rapid spread to the entire power grid, and the impact on frequency stability is more direct and more intense. The frequency deviation risk degree is determined based on the number of abnormal sub-regions and the abnormal sub-region distribution coefficient, which can determine the overall size of the anomaly through the number and determine the local impact strength of the anomaly through the distribution coefficient, so that the evaluation result is more in line with the actual operation law of the power grid and meets the user's demand for risk warning accuracy.

[0114] Specifically, the optimization determination module responds to the frequency deviation risk degree greater than or equal to the preset frequency deviation risk degree, and performs energy storage power optimization for the current monitoring period.

[0115] If the frequency deviation risk degree is less than the preset frequency deviation risk degree, the energy storage power optimization for the current monitoring period is not needed.

[0116] The value of the preset frequency deviation risk degree can be determined by the user according to the actual application scenario. The greater the user's demand for improving the risk warning accuracy is, the smaller the value of the preset frequency deviation risk degree is. A method for determining the value of the preset frequency deviation risk degree is provided. The minimum value of the frequency deviation risk degree corresponding to each historical record that can meet the user's demand in the historical record of energy storage power optimization is recorded as the preset frequency deviation risk degree.

[0117] Specifically, the strategy execution module determines the number of collaborative energy storage power sources according to the frequency deviation risk degree.

[0118] The number of collaborative energy storage power sources and the frequency deviation risk degree have a positive correlation.

[0119] The number of collaborative energy storage power sources is the smallest integer greater than or equal to w, and w=(frequency deviation risk degree / preset frequency deviation risk degree) x average value of the number of collaborative energy storage power sources corresponding to the historical record that can meet the user's demand.

[0120] It can be understood that when the frequency deviation risk degree is large, the power grid has a greater risk of frequency imbalance, since the power grid frequency is directly related to the active power balance, when the load demand suddenly increases, the frequency will decrease; when the power generation power is suddenly excessive, the frequency will rise, according to the frequency deviation risk degree to determine the number of collaborative energy storage power, through collaborative regulation, increase the output power when the frequency decreases, and increase the input power when the frequency rises, which can quickly suppress the power imbalance in the power grid, and further ensure the stability of the power grid frequency.

[0121] Specifically, the strategy execution module increases the ramp rate of the collaborative energy storage power based on the relative deviation value under the preset instability condition;

[0122] The preset instability condition is that the relative deviation value is less than the preset relative deviation value, and the increase value of the ramp rate is in a positive correlation with the relative deviation value.

[0123] Wherein, the historical record of determining the number of collaborative energy storage power according to the frequency deviation risk degree and meeting the user demand is recorded as a reference historical record;

[0124] The relative deviation value = the number of collaborative energy storage power / the average value of the number of collaborative energy storage power corresponding to each reference historical record-the frequency deviation risk degree / the average value of the frequency deviation risk degree corresponding to each reference historical record;

[0125] The value of the preset relative deviation value can be determined by the user according to the actual application scene, the greater the user's demand for reducing the frequency deviation risk, the smaller the value of the preset relative deviation value, a method for determining the value of the preset relative deviation value is provided, the average value of the relative deviation value corresponding to the historical record in which the user can meet the demand is recorded as the preset relative deviation value;

[0126] The increase value of the ramp rate = the relative deviation value / the preset relative deviation value x the initial ramp rate, the ramp rate is the rate of change of the output power of the energy storage power with time, and the unit is kW / min, and the initial ramp rate is 200 kW / min;

[0127] It can be understood that the relative deviation value effectively reflects the matching imbalance degree between the current number of collaborative energy storage power and the frequency deviation risk degree, and the increase adjustment of the ramp rate based on the relative deviation value can make the energy storage power increase or decrease power at a faster speed, and can quickly reduce the power imbalance gap, thereby more quickly suppressing the power grid frequency imbalance.

[0128] The technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical schemes after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. A remote management system for energy storage power sources based on the Internet of Things, characterized in that, include: The data acquisition module is used to collect and store data as well as monitoring data for the target monitoring cycle; The data analysis module is connected to the data acquisition module and is used to determine the data status based on the power grid frequency deviation fluctuation value and performance degradation, and to determine whether to perform data comparison analysis or abnormal sub-region analysis based on the data status. The first analysis module is connected to the data acquisition module and the data analysis module respectively. It is used to determine the analysis data based on the data correlation mean or the similarity of feature parameters in the data comparison analysis, and to determine the frequency deviation risk based on the abnormal data ratio and the abnormal time reference value of the analysis data. The second analysis module is connected to the data acquisition module and the data analysis module respectively. It is used to determine the abnormal sub-regions based on the power comparison value or the power change rate in the abnormal sub-region analysis, and to determine the frequency deviation risk level based on the number of abnormal sub-regions and the distribution coefficient of abnormal sub-regions. An optimization judgment module is connected to the first analysis module and the second analysis module respectively, and is used to determine whether to optimize the energy storage power supply for the current monitoring cycle based on the frequency deviation risk level. The strategy execution module, which is connected to the optimization determination module, is used to determine the number of collaborative energy storage power sources based on the frequency deviation risk level when optimizing energy storage power sources for the current monitoring period, and to adjust the ramp rate of collaborative energy storage power sources based on the relative deviation value under preset instability conditions.

2. The IoT-based remote management system for energy storage power supply according to claim 1, characterized in that, The data analysis module responds to data states where the power grid frequency deviation fluctuation value is less than the preset power grid frequency deviation fluctuation value and the performance degradation degree is less than the preset performance degradation degree, and then performs anomaly sub-region analysis.

3. The IoT-based remote management system for energy storage power supply according to claim 2, characterized in that, The data analysis module responds to data states where the power grid frequency deviation fluctuation value is greater than or equal to a preset power grid frequency deviation fluctuation value or the performance degradation degree is greater than or equal to a preset performance degradation degree, and performs data comparison and analysis.

4. The IoT-based remote management system for energy storage power supply according to claim 3, characterized in that, The methods for confirming the performance degradation include: If the internal resistance growth rate is greater than or equal to the preset internal resistance growth rate, the performance degradation rate is determined based on the decrease in SOH. If the internal resistance growth rate is less than the preset internal resistance growth rate, the performance degradation degree is determined based on the SOH decrease comparison degree.

5. The IoT-based remote management system for energy storage power supply according to claim 1, characterized in that, The first analysis module determines the analysis data based on the data correlation mean, according to the data correlation degree or feature parameter similarity, including: The first analysis module responds when the average correlation value of the data is greater than or equal to the preset average correlation value of the data, and selects the analysis data based on the correlation degree of the data. The first analysis module responds to data where the mean of the associated data is less than the preset mean of the associated data, and then selects analysis data based on the similarity of the feature parameters.

6. The IoT-based remote management system for energy storage power supply according to claim 1, characterized in that, The first analysis module determines the frequency deviation risk level based on the proportion of abnormal data in the analysis data and the reference value of abnormal time. The frequency deviation risk level is positively correlated with the proportion of abnormal data, and the frequency deviation risk level is negatively correlated with the abnormal time reference value.

7. The IoT-based remote management system for energy storage power supply according to claim 1, characterized in that, The second analysis module determines abnormal sub-regions based on the power comparison value or the power change rate, including: If the response power comparison value of the second analysis module is greater than or equal to the preset power comparison value, then the abnormal sub-region is determined based on the power comparison value. If the response power comparison value of the second analysis module is less than the preset power comparison value, then the abnormal sub-region is determined based on the power change rate.

8. The IoT-based remote management system for energy storage power supply according to claim 1, characterized in that, The optimization judgment module responds when the frequency deviation risk level is greater than or equal to the preset frequency deviation risk level, and then optimizes the energy storage power supply for the current monitoring cycle.

9. The IoT-based remote management system for energy storage power supply according to claim 8, characterized in that, The strategy execution module determines the number of collaborative energy storage power sources based on the frequency deviation risk level. The number of collaborative energy storage power sources is positively correlated with the risk of frequency deviation.

10. The IoT-based remote management system for energy storage power supply according to claim 9, characterized in that, The strategy execution module adjusts the ramp rate of the collaborative energy storage power supply based on the relative deviation value under preset instability conditions. The preset instability condition is that the relative deviation value is less than the preset relative deviation value, and the increase in the gradient is positively correlated with the relative deviation value.

Images