A method and system for evaluating frequency regulation capability of energy storage thermal power units

Abstract

The present invention relates to the field of electric energy storage and control technology, and specifically to a method and system for evaluating the frequency regulation capability of an energy storage thermal power unit, comprising the following steps: based on thermal power storage operation data, identifying node indicators and status records, extracting fluctuations and frequencies, evaluating frequency response adaptability, analyzing regulation rates and coordination ratios, screening risk units, and outputting frequency regulation capability evaluation intervals. In the present invention, by integrating and identifying the operation data of the energy storage thermal power unit, real-time monitoring of power fluctuations, energy storage status, and load changes is performed, the response cycle of the energy storage unit is optimized, the stability of the frequency regulation process is ensured, and the battery capacity, temperature rise rate, and cycle frequency are comprehensively monitored to avoid overload and efficiency reduction, thereby ensuring stable grid dispatch. By identifying health risks and delineating the frequency regulation response space, the coordination of energy storage and thermal power units is achieved, the grid's regulation capability and long-term reliability in response to fluctuating energy are enhanced, and response delays or efficiency issues in traditional methods are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage control, and in particular to a method and system for evaluating the frequency regulation capability of an energy storage thermal power unit. Background Art

[0002] The field of energy storage and control technology encompasses various technologies related to the storage, dispatch, and management of electrical energy in power systems. Its core focus is optimizing power efficiency and ensuring grid stability and reliability through various energy storage devices. With the large-scale integration of renewable energy, particularly in the dispatch of volatile energy sources such as wind and photovoltaic power, energy storage and control technology primarily encompasses the storage, conversion, and dispatch of electrical energy. This involves methods for managing energy storage devices, optimizing energy storage technology, and coordinating it with traditional generators (such as thermal and wind turbines). Through scientific and rational control methods, grid load fluctuations and low energy efficiency can be effectively addressed, while also improving the regulation and flexibility of power.

[0003] Among them, the frequency regulation capability assessment method of thermal power units with energy storage refers to a technical method for evaluating the energy storage capability of thermal power units during the frequency regulation process. It focuses on the participation capability of thermal power units in grid frequency regulation, especially the frequency regulation capability assessment after the combination of energy storage technology and thermal power units. This method monitors and dispatches the energy storage equipment of thermal power units, and evaluates their response capability and energy storage efficiency during the frequency regulation process in combination with the operating status of the thermal power units. By conducting a detailed analysis of the coordinated operation of thermal power units and energy storage, it evaluates their performance under different frequency regulation conditions, and proposes to optimize the relationship between energy storage and frequency regulation.

[0004] In practical applications, existing energy storage control technologies suffer from insufficiently rapid and accurate responses to grid frequency regulation. This is particularly true when coordinating energy storage with traditional thermal power units, making efficient response and coordination difficult. For example, during frequency regulation of thermal power units, energy storage devices are used based on preset load change patterns. However, because actual grid load fluctuations are instantaneous and highly variable, traditional methods are unable to promptly adjust the energy storage system's charging and discharging strategies, resulting in delayed frequency regulation or inaccurate frequency regulation. Existing technologies lack real-time monitoring and diagnosis of energy storage health, failing to effectively identify potential risks of energy storage units during continuous operation. This can easily lead to overcharging and discharging or excessive losses in the energy storage system, impacting its long-term service life and efficiency. These issues present traditional methods with challenges of insufficient flexibility and delayed emergency response when responding to large-scale renewable energy access, impacting power stability and regulation capabilities. Summary of the Invention

[0005] In order to solve the problem that the existing energy storage control technology has insufficient response speed and accuracy to grid frequency regulation in practical applications, especially when coordinating energy storage with traditional thermal power units, it is difficult to achieve efficient response and coordination. For example, in the frequency regulation process of thermal power units, the use of energy storage equipment is based on a preset load change pattern. However, since the actual grid load fluctuations are instantaneous and highly variable, traditional methods cannot adjust the charging and discharging strategy of the energy storage system in a timely manner, resulting in delayed frequency regulation or inaccurate frequency regulation. The existing technology lacks real-time monitoring and diagnosis of the health status of energy storage, and fails to effectively identify the potential risks of energy storage units in continuous operation, which can easily lead to overcharging and discharging or excessive loss of the energy storage system, affecting its long-term service life and efficiency. This problem makes the traditional method face the challenges of insufficient flexibility and delayed emergency response when dealing with large-scale renewable energy access, thereby affecting the technical problem of power stability and regulation capability. The embodiment of the present invention provides a method and system for evaluating the frequency regulation capability of energy storage thermal power units. The technical solution is as follows:

[0006] In one aspect, a method for evaluating the frequency regulation capability of a thermal power generation unit with energy storage is provided, the method comprising:

[0007] S1: Based on the operating data of the energy storage thermal power unit, including the grid frequency value, energy storage power value, current energy storage charge and discharge status, and thermal power load change value, the edge node interface is called to merge and organize the operating data, identify the indicators of the corresponding nodes, and obtain multi-source status collection records;

[0008] S2: Based on the multi-source status collection records, filter the power fluctuation value of the energy storage unit and the adjustment frequency of the edge control node, determine the power supply capacity coverage within the corresponding frequency response period, and obtain the energy storage frequency regulation matching metric value;

[0009] S3: Calling the energy storage frequency regulation matching metric value, comparing it with the regulation rate value of the thermal power load interface, identifying the load coverage difference range within the same response period, determining the response matching degree, and obtaining the frequency regulation resource coordination ratio;

[0010] S4: According to the frequency modulation resource coordination ratio, the battery capacity change rate, temperature rise rate and cycle frequency are extracted, the three values ​​are compared by threshold, the low adaptation state marking unit is filtered, and the health risk identification item is output.

[0011] As a further solution of the present invention, the multi-source status collection record includes grid frequency, energy storage power, energy storage charging and discharging status, and thermal power load changes; the energy storage frequency regulation matching measurement value includes power fluctuation amplitude, adjustment frequency, and power supply capacity coverage; the frequency regulation resource coordination ratio includes load coverage difference, response matching degree, and frequency regulation resource coordination ratio; and the health risk identification items include battery capacity changes, temperature rise rate, cycle frequency, and its low adaptation mark.

[0012] As a further solution of the present invention, the steps of multi-source status collection and recording are specifically as follows:

[0013] S101: Based on the operating data of the energy storage thermal power unit, the grid frequency value, the energy storage power value, the current charge and discharge status, and the thermal power load change value are extracted, and the data parameters are uniformly processed according to the timestamp. The corresponding data parameters are matched through the edge node interface to generate a node parameter attribute value set;

[0014] S102: Calling the node parameter attribution value set, identifying and indexing the grid frequency and energy storage capacity, classifying them based on the charge and discharge status, dividing the intervals according to the thermal power load change, and constructing a classified state parameter sequence;

[0015] S103: According to the classified state parameter sequence, the load interval is matched with the charge and discharge state, the parameter combinations within the interval are extracted and the frequencies are counted, and the frequency threshold is used for screening to eliminate unstable combinations and retain the state sets with repetition rates exceeding the set standard to obtain multi-source state acquisition records.

[0016] As a further solution of the present invention, the step of energy storage frequency modulation matching metric value is specifically as follows:

[0017] S201: Filtering the power change values ​​of the energy storage units and the number of adjustment actions of the corresponding edge control nodes based on the multi-source status collection records, summarizing them by time period, and generating energy storage fluctuation and adjustment statistics;

[0018] S202: Calling the energy storage fluctuation and regulation statistics, dividing the time period according to the frequency response cycle, determining the coverage of the power supply upper limit corresponding to the node regulation frequency by the power fluctuation value in each cycle, and obtaining the response cycle power supply coverage ratio;

[0019] S203: Based on the response cycle power supply coverage ratio, the cycle coverage ratio and the cycle duration are dimensionalized, a cycle difference value between the coverage ratio and a power supply coverage reference value is calculated, a mean value of the cycle difference value is extracted, and an energy storage frequency regulation matching metric value is established;

[0020] The period difference between the coverage ratio and the power supply coverage reference value is calculated using the formula:

[0021] ;

[0022] in, Represents the periodic difference between the coverage ratio and the power supply coverage reference value, represents the coverage ratio of the i-th period, represents the duration of the i-th cycle, Represents the power supply coverage reference value of the i-th cycle, Represents the total number of cycles.

[0023] As a further solution of the present invention, the step of frequency modulation resource coordination ratio is specifically as follows:

[0024] S301: Calling the energy storage frequency regulation matching metric, unifying the cycle number matching segment data based on the response cycle and regulation rate value in the thermal power load interface, calculating the difference between the energy storage frequency regulation value and the thermal power regulation rate value in each cycle, and summarizing the extreme values ​​within the cycle to obtain the load response difference interval;

[0025] S302: Based on the load response difference interval, determine whether the energy storage frequency regulation value in each cycle segment is within the response overlap reference range set within the thermal power regulation rate change interval, calculate the ratio between the number of overlaps and the total number of cycles, and obtain the frequency regulation resource coordination ratio.

[0026] As a further solution of the present invention, the frequency modulation resource coordination ratio adopts the formula:

[0027] ;

[0028] in, represents the frequency modulation resource coordination ratio, represents the frequency change in the kth cycle, Represents the energy storage frequency modulation value in the kth cycle, Represents the maximum frequency change, represents the experience adjustment coefficient, Represents the total number of cycles.

[0029] As a further solution of the present invention, the steps of health risk identification are specifically as follows:

[0030] S401: extracting the battery capacity change value and time series of the energy storage unit according to the frequency modulation resource coordination ratio, calculating the difference rate between adjacent time periods and performing normalization processing to generate a set of energy storage capacity change rate values;

[0031] S402: Calling the energy storage capacity change rate value set, extracting the temperature sequence of the corresponding unit and the number of charge and discharge times in the cycle, calculating the temperature change amplitude and the average frequency, and generating the temperature rise and cycle rate;

[0032] S403: Based on the temperature rise and cycle rate, the battery capacity change rate, temperature rise rate and cycle frequency are compared with the set performance thresholds respectively, and cells with all three indicators lower than the lower threshold are screened out, and abnormal status marks are added to obtain health risk identification items.

[0033] As a further embodiment of the present invention, the method further comprises step S5:

[0034] S5: Invoke the health risk identification item, delineate the effective frequency regulation response space of energy storage and thermal power in the current cycle based on the weight distribution within the continuous frequency regulation cycle, and output an overview of the frequency regulation capability assessment interval;

[0035] The frequency regulation capability assessment interval overview includes the energy storage frequency regulation response space, the thermal power frequency regulation response space, and the frequency regulation capability effective interval.

[0036] As a further solution of the present invention, the step of summarizing the frequency modulation capability evaluation interval is specifically as follows:

[0037] S501: Calling the health risk identification item, extracting the response amplitude and duration of the energy storage and thermal power of the marked unit in the continuous frequency modulation cycle, weighting them by frequency, and generating a cycle response weight group;

[0038] S502: Normalizing the weighted data of energy storage and thermal power according to the periodic response weight group, screening the continuous output segment, and obtaining the effective response interval;

[0039] S503: Based on the effective response range, a combined analysis is performed on the segment boundaries of energy storage and thermal power, and grade segments are divided according to the cycle index. The upper and lower response boundaries within each segment are extracted to obtain an overview of the frequency regulation capability evaluation range.

[0040] A frequency regulation capability evaluation system for energy storage thermal power units, the system comprising:

[0041] The data merging module classifies nodes based on the operating data of energy storage thermal power units, including grid frequency, energy storage capacity, energy storage charge and discharge status, and thermal power load changes. It also divides frequency fluctuations within the same cycle into intervals, integrates data by time period, and generates frequency modulation input association records.

[0042] Based on the frequency modulation input association records, the response analysis module extracts the periodic energy storage power fluctuations and the control node adjustment frequency, calculates the ratio of the adjustment amplitude to the frequency change rate, classifies the frequency distribution weights, identifies the energy storage unit's frequency modulation participation, and establishes an energy storage frequency response profile;

[0043] The load comparison module extracts the thermal power regulation rate data based on the energy storage frequency response portrait, calculates the difference with the energy storage unit response intensity value, extracts the difference section, determines the compensation trend based on the difference distribution, and outputs a load action coordination relationship diagram;

[0044] The state recognition module calls the capacity change rate, temperature rise rate and cycle frequency of the energy storage unit based on the load action coordination relationship diagram to determine whether the limit is exceeded, and marks the over-limit unit. The results are filtered according to the frequency of overlap between the marked unit and the difference section to obtain the abnormal adaptation unit index table;

[0045] Based on the abnormal adaptation unit index table, the capacity assessment module extracts the action period and output level within the continuous frequency regulation cycle, performs segment matching in combination with the thermal power response curve, analyzes the overlap of the output trajectories of the two types of resources, identifies the joint coverage interval of the dual regulation capability, and generates a frequency regulation resource collaborative capability zoning map.

[0046] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0047] By integrating and identifying the operating data of energy storage thermal power units, the adaptability and response efficiency of grid frequency regulation can be comprehensively improved. Real-time monitoring of key data such as power fluctuations, energy storage status, and load changes allows precise analysis of frequency response adaptability metrics, enabling more accurate measurement of the capacity and contribution of energy storage systems under different frequency regulation conditions. This optimizes the response cycle of energy storage units and ensures their stability during grid frequency regulation. Comprehensive monitoring of battery capacity, temperature rise rate, and cycle frequency enables timely identification of health status, avoiding load overloads or decreased efficiency of energy storage equipment, thereby ensuring stable dispatch of the entire grid. Based on health risk identification and the delineation of frequency regulation response space, optimal coordination between energy storage and thermal power units can be achieved, enhancing frequency regulation capabilities and strengthening the grid's emergency regulation capabilities and long-term operational reliability in the face of fluctuating energy sources. The processing logic not only improves energy storage efficiency but also, through multi-dimensional real-time monitoring, enhances the flexibility of frequency regulation resources, avoiding the response delays or efficiency degradation associated with traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0049] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0050] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0051] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0052] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0053] Figure 6 This is a detailed flow chart of S5 of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0056] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0057] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] See also Figure 1 The embodiment of the present invention provides a method for evaluating the frequency regulation capability of an energy storage thermal power unit. The processing flow of the method may include the following steps:

[0060] S1: Based on the operating data of the energy storage thermal power unit, including the grid frequency value, energy storage power value, current energy storage charge and discharge status, and thermal power load change value, the edge node interface is called to merge and organize the operating data, identify the indicators of the corresponding nodes, and obtain multi-source status collection records;

[0061] S2: Based on multi-source state collection records, the energy storage unit's power fluctuation value and the edge control node's adjustment frequency are screened to determine the power supply capacity coverage within the corresponding frequency response period and obtain the energy storage frequency regulation matching metric.

[0062] S3: Call the energy storage frequency regulation matching metric value, compare it with the regulation rate value of the thermal power load interface, identify the load coverage difference range within the same response period, determine the response matching degree, and obtain the frequency regulation resource coordination ratio;

[0063] S4: Based on the frequency modulation resource coordination ratio, the battery capacity change rate, temperature rise rate, and cycle frequency are extracted, and threshold comparison is performed on the three values ​​to filter out low-adaptation state marking units and output health risk identification items;

[0064] S5: Call the health risk identification item, and based on the weight distribution within the continuous frequency regulation cycle, define the effective frequency regulation response space of energy storage and thermal power in the current cycle, and output an overview of the frequency regulation capability assessment range.

[0065] Multi-source status collection records include grid frequency, energy storage power, energy storage charging and discharging status, and thermal power load changes. Energy storage frequency regulation matching metrics include power fluctuation amplitude, adjustment frequency, and power supply capacity coverage. Frequency regulation resource coordination ratio includes load coverage difference, response matching degree, and frequency regulation resource coordination ratio. Health risk identification items include battery capacity changes, temperature rise rate, cycle frequency, and its low adaptation mark. The frequency regulation capability assessment interval overview includes energy storage frequency regulation response space, thermal power frequency regulation response space, and frequency regulation capability effective interval.

[0066] Specifically, if Figure 2 As shown in the figure, the steps for multi-source status collection and recording are as follows:

[0067] S101: Based on the operating data of the energy storage thermal power unit, the grid frequency value, the energy storage power value, the current charge and discharge status, and the thermal power load change value are extracted, and the data parameters are uniformly processed according to the timestamp. The corresponding data parameters are matched through the edge node interface to generate a node parameter attribute value set;

[0068] When extracting the grid frequency, energy storage capacity, charge and discharge status, and thermal power load change values, the parameters are first extracted from the operating data of the energy storage thermal power unit. The data extraction process needs to be uniformly processed through timestamps to ensure the accuracy and consistency of the data. The grid frequency is a key parameter for power stability and is obtained through real-time monitoring equipment. The energy storage capacity value reflects the current storage capacity of the battery. The charge and discharge status is calibrated according to the working status of the battery (charging or discharging). The thermal power load change value refers to the change in the output power of the thermal power unit, which is recorded in real time by the load monitoring device. Through data integration, it can provide the necessary foundation for subsequent data analysis and model construction. The data interface and edge node are used to integrate the data. The node interface matching function can ensure that the data is accurately distributed according to different sensors and devices. Through real-time collection and transmission, a node parameter attribution value set is generated to prepare data for subsequent analysis and processing.

[0069] S102: Calling the node parameter attribution value set, identifying and indexing the grid frequency and energy storage capacity, classifying them based on the charge and discharge status, dividing the intervals according to the thermal power load changes, and constructing a classified state parameter sequence;

[0070] Identify the relevant data of grid frequency and energy storage capacity. The grid frequency value is obtained from real-time power monitoring. By analyzing the frequency data, the stability of the grid can be judged and combined with the energy storage capacity data. The energy storage capacity value can reflect the changes in the charging or discharging state of the battery. By classifying these two parameters, for example, when the grid frequency fluctuates greatly, the energy storage capacity will show faster charging and discharging changes, which can serve as an important classification basis. By analyzing the charging and discharging state, the working state of the battery can be further subdivided. When classifying, it can be further distinguished according to the different intervals of thermal power load changes. The division of thermal power load change intervals needs to be set according to historical data. Through comprehensive analysis of the classification, the classified state parameter sequence can be obtained, and data support can be provided for subsequent state predictions.

[0071] S103: Based on the classified state parameter sequence, the load interval is matched with the charge and discharge state, the parameter combinations within the interval are extracted and the frequencies are counted, and the frequency threshold is used for screening to eliminate unstable combinations and retain the state sets with repetition rates exceeding the set standard to obtain multi-source state acquisition records;

[0072] The load intervals are mapped to the charge and discharge states. The load intervals are divided by analyzing thermal power load data. Based on the distribution of historical load changes, the load intervals are divided into multiple levels or intervals. The interval division needs to be based on the actual fluctuation range of the grid load and the battery's charge and discharge capacity. For example, when the load fluctuation is set within the ±5% range, the grid is stable, while when the fluctuation exceeds ±10%, the grid is unstable. At this time, the change in energy storage capacity will directly affect the stability of the grid. The parameter combinations within the interval are combined and statistically analyzed. Through frequency statistics, the frequency of state combinations in different intervals is found. When screening, a frequency threshold is set. If the frequency of a parameter combination is lower than the set threshold, it is considered an unstable combination and eliminated. The threshold setting should be debugged according to actual data. The purpose of setting the threshold is to eliminate extreme abnormal conditions and retain state combinations that appear more frequently in actual applications. This frequency threshold screening method can accurately identify stable states that have a significant impact on grid frequency and energy storage capacity, and obtain multi-source state collection records.

[0073] Specifically, if Figure 3 As shown in the figure, the steps for energy storage frequency matching metric value are as follows:

[0074] S201: Filter the energy storage unit's power change values ​​and the number of adjustment actions of its edge control node based on multi-source state collection records, summarize them by time period, and generate energy storage fluctuation and adjustment statistics;

[0075] The power change value of the energy storage unit is extracted from the multi-source status acquisition record. The power change value can be obtained through the energy storage monitoring data. It is the change in power when the battery is charging or discharging. Each time the battery is charged or discharged, the value of the increase or decrease in power is recorded. The number of adjustment actions of the control node is extracted from the same record. The number of adjustment actions refers to the number of adjustment operations performed by the control node to maintain stability, including charging, discharging, and current adjustment. The number of actions reflects the frequency of the adjustment process. When summarizing data, it is necessary to summarize by time period. For example, the data can be segmented by hour or day, and the change in power of the energy storage unit and the number of adjustment actions in each time period are counted. The data can be summarized by weighted average or simple summation. For example, for a power change of +100kWh in a certain hour, the number of adjustment actions is 5 times. After summarizing by time period, the energy storage fluctuation and adjustment statistics are obtained.

[0076] S202: Retrieving energy storage fluctuation and regulation statistics, dividing time periods according to the frequency response cycle, determining the coverage of the power supply upper limit corresponding to the node regulation frequency by the power fluctuation value within each cycle, and obtaining the power supply coverage ratio of the response cycle;

[0077] The time periods are divided into frequency response cycles. The frequency response cycles are set according to the load variation pattern of the power grid. For example, a response cycle is set every 30 minutes. This is used to analyze the load fluctuation of the power grid within this period. The relationship between the power fluctuation value and the node adjustment frequency within this period is used to determine the coverage of the power supply upper limit by the power fluctuation value. First, the power fluctuation value within each cycle is calculated. The power fluctuation value can be obtained by calculating the maximum change in power within the cycle. For example, if the power consumption changes from 50kWh to 30kWh within a certain cycle, the power fluctuation is 20kWh. Correspondingly, the frequency of node adjustment within this cycle is calculated. If the number of adjustment actions within this cycle is 4, it can be considered that the adjustment frequency within this cycle is 4. This data is compared with the power supply upper limit. The power supply upper limit refers to the maximum power supply capacity that the power grid can provide within the response period. It is determined based on the maximum power supply capacity of the power grid. For example, if the maximum power supply capacity is set to 200kW, the relationship between the power fluctuation value and the adjustment frequency within this cycle is analyzed to obtain the power supply coverage ratio of the response period.

[0078] S203: Based on the response cycle power supply coverage ratio, the cycle coverage ratio and the cycle duration are dimensionalized, the cycle difference between the coverage ratio and the power supply coverage reference value is calculated, the mean of the cycle difference is extracted, and the energy storage frequency regulation matching metric is established;

[0079] The period difference between the coverage ratio and the power supply coverage reference value is calculated using the formula:

[0080] ;

[0081] in, Represents the periodic difference between the coverage ratio and the power supply coverage reference value, represents the coverage ratio of the i-th period, represents the duration of the i-th cycle, Represents the power supply coverage reference value of the i-th cycle, represents the total number of cycles;

[0082] (Cycle Difference Value) This parameter represents the cycle difference value, which is used to reflect the difference between the power supply coverage of each cycle and the baseline value. It is obtained by calculating the difference of each cycle and taking the average value.

[0083] (Coverage Ratio for Cycle i) This parameter is the coverage ratio for cycle i, representing the ratio of actual power supply coverage to expected power supply coverage during that cycle. This parameter is obtained from actual monitoring data. The power grid management system obtains the actual power supply to demand ratio for each cycle. For example, if the actual power supply for a cycle is 800 kW and the expected power supply for that cycle is 1000 kW, the coverage ratio for that cycle is: ;

[0084] (Duration of the i-th cycle) This parameter indicates the duration of the i-th cycle and is recorded in real time by the dispatching system according to the grid operation plan. For example, assuming that the duration of a cycle is 5 hours, the record is: ;

[0085] (Power supply coverage baseline value for cycle i) This parameter is the preset power supply coverage baseline value, which is used to compare with the actual power supply coverage ratio. The baseline value is set based on historical data or grid management standards. For example, if the power supply coverage baseline value is set to 0.85, then: ;

[0086] Assume there are 3 cycles, , based on the actual monitoring data, the following information can be obtained:

[0087] Cycle 1: , Hour, ;

[0088] Cycle 2: , Hour, ;

[0089] Cycle 3: , Hour, ;

[0090] Substitute the formula to calculate the difference value of each period:

[0091] For cycle 1: ;

[0092] For cycle 2: ;

[0093] For cycle 3: ;

[0094] Calculate the average of the period difference values: ;

[0095] The results show that the period difference value is 0.6828, which reflects the average difference between the power supply coverage ratio and the power supply coverage benchmark value in all periods;

[0096] Supplement and improve the process of obtaining each parameter and the process of dimension unification:

[0097] (Coverage ratio of cycle i): The coverage ratio is determined by the ratio between the actual power supply capacity of the power grid and the expected power supply capacity. This parameter is quantified by comparing the power demand and supply in each cycle through real-time data acquisition by the power monitoring system.

[0098] (Duration of the i-th cycle): The duration is recorded by the power grid dispatching system. The timestamp is used to accurately obtain the duration of each cycle in hours.

[0099] (Power supply coverage baseline value for cycle i): The baseline value is determined by grid management standards, historical data analysis, or optimization calculations. It is adjusted based on demand changes in different regions and time periods. The baseline value needs to be dynamically adjusted based on historical operating data to adapt to grid operation requirements.

[0100] All parameters in the formula must maintain the same unit, especially the calculation of coverage ratio and duration. The coverage ratio is a unitless quantity, the duration unit is hours, the baseline value is a unitless quantity, and the period difference value result is a unitless quantity.

[0101] Specifically, if Figure 4 As shown in the figure, the steps for frequency modulation resource coordination ratio are as follows:

[0102] S301: Call the energy storage frequency regulation matching metric value, unify the cycle number matching segment data according to the response cycle and regulation rate value in the thermal power load interface, calculate the difference between the energy storage frequency regulation value and the thermal power regulation rate value in each cycle, and summarize the extreme values ​​within the cycle to obtain the load response difference range;

[0103] First, the response cycles are numbered and matched to corresponding segment data. Within each cycle, the difference between the energy storage frequency regulation value and the thermal power regulation rate value is calculated. The difference is obtained by comparing the frequency regulation value and the regulation rate value within each cycle. The range of the frequency regulation value and the regulation rate value must be clearly defined. For example, the frequency regulation value represents the rate of energy regulation, measured in kilowatts (kW), while the regulation rate refers to the rate of change in regulation capacity, measured in kilowatts per hour (kW / h). The difference is calculated using simple subtraction. For example, if the energy storage frequency regulation value is 100 kW and the thermal power regulation rate is 80 kW / h during a certain period, the difference is 20 kW / h. The extreme values ​​of each cycle are aggregated to obtain the maximum or minimum difference within that cycle, thereby deriving the load response difference range. This cycle-by-cycle difference calculation and extreme value extraction provides a basis for determining the overlap benchmark range and accurately extracts the difference data between the energy storage frequency regulation and thermal power load responses, providing accurate basic data support for subsequent coordinated frequency regulation resource analysis.

[0104] S302: Based on the load response difference interval, determine whether the energy storage frequency regulation value in each cycle segment is within the response overlap reference range set within the thermal power regulation rate change interval, calculate the ratio of the number of overlaps to the total number of cycles, and obtain the frequency regulation resource coordination ratio;

[0105] Frequency modulation resource coordination ratio, using the formula:

[0106] ;

[0107] in, represents the frequency modulation resource coordination ratio, represents the frequency change in the kth cycle, Represents the energy storage frequency modulation value in the kth cycle, Represents the maximum frequency change, represents the experience adjustment coefficient, represents the total number of cycles;

[0108] Frequency regulation resource coordination ratio, which indicates the degree of overlap between the energy storage frequency regulation value and the frequency change within each cycle, expressed in percentage.

[0109] : Frequency change during the kth cycle, in Hz. This parameter is obtained in real time by the frequency monitoring device. At the end of each cycle, it is obtained by comparing the maximum and minimum frequency values ​​within the cycle.

[0110] : The energy storage frequency modulation value in the kth cycle, in MW. This value reflects the power change of the energy storage in this cycle. This value is calculated by the energy storage dispatch system based on the charge and discharge capacity of the energy storage device and the frequency modulation response;

[0111] : Maximum frequency change, in Hz. This value is the maximum frequency change within all cycles and is calculated by the frequency monitoring device based on the historical data of the entire cycle;

[0112] : Adjustment coefficient, dimensionless. This coefficient is an empirical value set according to the load response characteristics of the power grid and is set between 0.2 and 0.5. In some cases, this coefficient will be dynamically adjusted according to the load fluctuation frequency range of the system;

[0113] : Total number of cycles. This value is the total number of cycles during the data collection process and is determined by the length of the data cycle within the collection period.

[0114] Parameter acquisition process and dimension unification:

[0115] (Frequency change): Calculated in real time by the power grid monitoring system, the frequency change value within each cycle is obtained by comparing the frequency sensor with the dispatching system. The monitoring frequency value and the determination of the sampling period need to be unified in dimension and accurate to two decimal places in Hz;

[0116] Energy storage frequency regulation value: This value is calculated in real time by the energy storage power station's control system and automatically recorded. The frequency regulation value needs to be unified under the dimensions of power output (MW) and power change (MW). This value is calculated based on the frequency regulation instructions collected in real time by the energy storage and the actual power change.

[0117] (Maximum frequency change): This value is calculated based on historical period data. The maximum frequency change value within a time period is selected. For example, if the frequency fluctuates from 49.8Hz to 50.2Hz within a certain period, the maximum frequency change is 0.4H.

[0118] (Adjustment coefficient): This coefficient is obtained through experiments on the grid regulation characteristics or analysis of historical data. The adjustment coefficient fluctuates with the fluctuation range of the load response and is generally set based on the grid stability and system response speed;

[0119] Assume the following monitoring data:

[0120] Total number of cycles: ;

[0121] Frequency change: Hz, Hz, Hz, Hz, Hz;

[0122] Energy storage frequency regulation value: MW, MW, MW, MW, MW;

[0123] Maximum frequency variation: Hz;

[0124] Adjustment factor: ;

[0125] Substituting into the formula: ;

[0126] Calculate each term:

[0127] For cycle 1: ;

[0128] For cycle 2: ;

[0129] For cycle 3: ;

[0130] For cycle 4: ;

[0131] For cycle 5: ;

[0132] Sum all the results: ;

[0133] The results show that the frequency regulation resource coordination ratio is 28.45%, that is, within 5 cycles, the overlap between energy storage frequency regulation and frequency changes is 28.45%, reflecting the effectiveness of frequency changes in the response of the energy storage frequency regulation system.

[0134] Specifically, if Figure 5 As shown in the figure, the steps for health risk identification are as follows:

[0135] S401: Extracting the battery capacity change values ​​and time series of the energy storage units based on the frequency modulation resource coordination ratio, calculating the differential rate between adjacent time periods and performing normalization processing to generate a set of energy storage capacity change rate values;

[0136] Extract the battery capacity change value and time series of the energy storage unit. The extraction process involves obtaining historical battery capacity data from energy storage management. The data is the total battery capacity recorded at regular intervals (such as every hour or every day). This data reflects the charging and discharging conditions of the battery over a period of time. The differential rate of battery capacity between adjacent time periods is calculated. The differential rate is calculated by subtracting the capacity of the previous period from the battery capacity of the current period, and then dividing it by the capacity of the previous period to obtain a percentage representing the capacity change. This percentage can indicate the capacity loss or increase of the battery in the period. Next, normalization is performed. Normalization is to convert the differential rate value into a standard range, between 0 and 1, which allows data at different times to be compared fairly, and finally generates a set of energy storage capacity change rate values. This value set provides a standardized battery performance indicator for subsequent analysis.

[0137] S402: Calling the energy storage capacity change rate value set, extracting the temperature sequence of the corresponding unit and the number of charge and discharge times in the cycle, calculating the temperature change amplitude and frequency average, and generating the temperature rise and cycle rate;

[0138] The temperature sequence of the corresponding unit and the number of charge and discharge times within the cycle are extracted. The temperature sequence is obtained and recorded in real time by the temperature sensor in the energy storage, while the number of charge and discharge times is recorded by battery management in each cycle. The temperature change amplitude is calculated. The calculation involves the difference between the highest and lowest temperature values ​​recorded in the cycle. This temperature difference can reflect the thermal stability of the battery during operation. The average value of the temperature change and the number of charge and discharge times is calculated. The calculation of the average value can provide an overall assessment of the temperature rise and battery usage frequency within each cycle, thereby generating the temperature rise and cycle rate. These two indicators help to understand the thermal management efficiency and usage intensity of the battery in actual operation.

[0139] S403: Based on the temperature rise and cycle rate, the battery capacity change rate, temperature rise rate, and cycle frequency are compared with the set performance thresholds. Cells with all three indicators below the lower threshold are screened and marked as abnormal to obtain health risk identification items.

[0140] After analyzing the temperature rise and cycle rate, the battery capacity change rate, temperature rise rate and cycle frequency are compared with the set performance thresholds. The thresholds are pre-set based on battery performance requirements and safety standards. The comparison can help identify which battery cells are performing below the expected level. The screening process involves checking whether all three indicators are simultaneously below the lower limit of their corresponding thresholds. If all indicators of a cell are below the threshold, this indicates that the cell has health or performance problems, and the cell will be marked with an abnormal status. This mark helps in subsequent maintenance decisions. Health risk identification items are obtained through the process. The identification items are a warning of future operational risks of the battery cell.

[0141] Specifically, if Figure 6 As shown in the figure, the steps of the frequency regulation capability assessment interval overview are as follows:

[0142] S501: Invoke the health risk identification item, extract the response amplitude and duration of the energy storage and thermal power of the marked unit within the continuous frequency modulation cycle, perform frequency weighting, and generate a cycle response weight group;

[0143] Extracting marked unit data from health risk identification items involves obtaining data from energy storage units that have been identified as potentially problematic, including the unit's response amplitude and duration within a continuous frequency modulation cycle. The response amplitude is represented by the amount of electrical energy the energy storage unit responds to a frequency modulation request within a specific cycle, while the duration refers to the length of time the energy storage unit can maintain a stable electrical output in a demand response state. The data needs to be weighted based on the frequency of occurrence. The purpose of weighting is to more accurately reflect the importance of frequent and critical responses. The weights are calculated based on the number of responses within the cycle and the effective duration of the response. Through this weighting method, data with a single large-amplitude response but a low frequency can be effectively balanced with data with frequent but smaller amplitude responses, ultimately generating a cycle response weight group that comprehensively reflects the response performance of the energy storage unit. This weight group helps operators understand which units perform better in frequent frequency modulation activities.

[0144] S502: Normalize the weighted data of energy storage and thermal power according to the periodic response weight group, select the continuous output segment, and obtain the effective response interval;

[0145] The data of energy storage and thermal power are normalized. Normalization eliminates the scale differences between different data sets by converting the data to the same scale or range, making the data of energy storage and thermal power units from different sources or different technologies comparable. The normalized data can more fairly reflect the performance of each unit, and then screen the continuous output segments. The continuous output segments refer to those time periods that always maintain stable output within the continuous frequency modulation cycle. The screening is determined by setting a threshold. For example, the time period in which the continuous output is not lower than a set value is set as the effective response interval, reflecting the stability and responsiveness when facing continuous demand.

[0146] S503: Based on the effective response range, a combined analysis is performed on the segment boundaries of energy storage and thermal power. The level segments are divided according to the cycle index. The upper and lower response limits within each segment are extracted to obtain an overview of the frequency regulation capability assessment range.

[0147] Based on the effective response range, a combined analysis of the segment boundaries of energy storage and thermal power is conducted. The analysis involves comparing and categorizing the performance of different energy storage and thermal power units within the cycle. By dividing the grade segments by cycle index, the performance of the units can be evaluated and classified. The grade segments are divided based on multiple dimensions such as the speed, durability and stability of the response. The upper and lower response boundaries within each segment are the high and low standards of unit performance in the grade. The boundaries are set based on performance statistics and expected operating standards, thereby obtaining an overview of the frequency regulation capability assessment range. This overview provides operators with a clear view to identify the position and role of each unit in the overall frequency regulation strategy and its potential upgrade or optimization needs.

[0148] A frequency regulation capability evaluation system for energy storage thermal power units, the system comprising:

[0149] The data merging module classifies nodes based on the operating data of energy storage thermal power units, including grid frequency, energy storage capacity, energy storage charge and discharge status, and thermal power load changes. It also divides frequency fluctuations within the same cycle into intervals, integrates data by time period, and generates frequency modulation input association records.

[0150] Based on the frequency modulation input association records, the response analysis module extracts the periodic energy storage power fluctuations and the control node adjustment frequency, calculates the ratio of the adjustment amplitude to the frequency change rate, classifies the frequency distribution weights, identifies the energy storage unit's frequency modulation participation, and establishes an energy storage frequency response profile;

[0151] The load comparison module extracts the thermal power regulation rate data based on the energy storage frequency response portrait, calculates the difference with the energy storage unit response intensity value, extracts the difference section, determines the compensation trend based on the difference distribution, and outputs a load action coordination relationship diagram;

[0152] The state recognition module calls the capacity change rate, temperature rise rate and cycle frequency of the energy storage unit based on the load action coordination relationship diagram to determine whether the limit is exceeded, and marks the over-limit unit. The results are filtered according to the frequency of overlap between the marked unit and the difference section to obtain the abnormal adaptation unit index table;

[0153] Based on the abnormal adaptation unit index table, the capacity assessment module extracts the action period and output level within the continuous frequency regulation cycle, performs segment matching in combination with the thermal power response curve, analyzes the overlap of the output trajectories of the two types of resources, identifies the joint coverage interval of the dual regulation capability, and generates a frequency regulation resource collaborative capability zoning map.

[0154] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the frequency regulation capability of a thermal power generation unit with energy storage, characterized in that: The following steps are involved: S1: Based on the operating data of the energy storage thermal power unit, including the grid frequency value, energy storage power value, current energy storage charge and discharge status, and thermal power load change value, the edge node interface is called to merge and organize the operating data, identify the indicators of the corresponding nodes, and obtain multi-source status collection records; S2: Based on the multi-source status collection records, filter the power fluctuation value of the energy storage unit and the adjustment frequency of the edge control node, determine the power supply capacity coverage within the corresponding frequency response period, and obtain the energy storage frequency regulation matching metric value; S3: Calling the energy storage frequency regulation matching metric value, comparing it with the regulation rate value of the thermal power load interface, identifying the load coverage difference range within the same response period, determining the response matching degree, and obtaining the frequency regulation resource coordination ratio; S4: According to the frequency modulation resource coordination ratio, the battery capacity change rate, temperature rise rate and cycle frequency are extracted, the three values ​​are compared by threshold, the low adaptation state marking unit is filtered, and the health risk identification item is output.

2. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 1, characterized in that: The multi-source status collection records include grid frequency, energy storage power, energy storage charging and discharging status, and thermal power load changes. The energy storage frequency regulation matching measurement values ​​include power fluctuation amplitude, adjustment frequency, and power supply capacity coverage. The frequency regulation resource coordination ratio includes load coverage difference, response matching degree, and frequency regulation resource coordination ratio. The health risk identification items include battery capacity changes, temperature rise rate, cycle frequency, and its low adaptation mark.

3. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 1, characterized in that: The steps of multi-source status collection and recording are specifically as follows: S101: Based on the operating data of the energy storage thermal power unit, the grid frequency value, the energy storage power value, the current charge and discharge status, and the thermal power load change value are extracted, and the data parameters are uniformly processed according to the timestamp. The corresponding data parameters are matched through the edge node interface to generate a node parameter attribute value set; S102: Calling the node parameter attribution value set, identifying and indexing the grid frequency and energy storage capacity, classifying them based on the charge and discharge status, dividing the intervals according to the thermal power load change, and constructing a classified state parameter sequence; S103: According to the classified state parameter sequence, the load interval is matched with the charge and discharge state, the parameter combinations within the interval are extracted and the frequencies are counted, and the frequency threshold is used for screening to eliminate unstable combinations and retain the state sets with repetition rates exceeding the set standard to obtain multi-source state acquisition records.

4. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 3, characterized in that: The steps of energy storage frequency regulation matching metric value are specifically as follows: S201: Filtering the power change values ​​of the energy storage units and the number of adjustment actions of the corresponding edge control nodes based on the multi-source status collection records, summarizing them by time period, and generating energy storage fluctuation and adjustment statistics; S202: Calling the energy storage fluctuation and regulation statistics, dividing the time period according to the frequency response cycle, determining the coverage of the power supply upper limit corresponding to the node regulation frequency by the power fluctuation value in each cycle, and obtaining the response cycle power supply coverage ratio; S203: Based on the response cycle power supply coverage ratio, the cycle coverage ratio and the cycle duration are dimensionalized, the cycle difference between the coverage ratio and the power supply coverage reference value is calculated, the mean of the cycle difference is extracted, and the energy storage frequency regulation matching metric is established.

5. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 4, characterized in that: The period difference between the coverage ratio and the power supply coverage reference value is calculated using the formula: ； in, Represents the periodic difference between the coverage ratio and the power supply coverage reference value, represents the coverage ratio of the i-th period, represents the duration of the i-th cycle, Represents the power supply coverage reference value of the i-th cycle, Represents the total number of cycles.

6. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 4, characterized in that: The steps of frequency modulation resource coordination ratio are specifically as follows: S301: Calling the energy storage frequency regulation matching metric, unifying the cycle number matching segment data based on the response cycle and regulation rate value in the thermal power load interface, calculating the difference between the energy storage frequency regulation value and the thermal power regulation rate value in each cycle, and summarizing the extreme values ​​within the cycle to obtain the load response difference interval; S302: Based on the load response difference interval, determine whether the energy storage frequency regulation value in each cycle segment is within a response overlap reference range set within the thermal power regulation rate change interval, calculate the ratio of the number of overlaps to the total number of cycles, and obtain a frequency regulation resource coordination ratio; The frequency modulation resource coordination ratio adopts the formula: ； in, represents the frequency modulation resource coordination ratio, represents the frequency change in the kth cycle, Represents the energy storage frequency modulation value in the kth cycle, Represents the maximum frequency change, represents the experience adjustment coefficient, Represents the total number of cycles.

7. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 6, characterized in that: The steps of health risk identification are as follows: S401: extracting the battery capacity change value and time series of the energy storage unit according to the frequency modulation resource coordination ratio, calculating the difference rate between adjacent time periods and performing normalization processing to generate a set of energy storage capacity change rate values; S402: Calling the energy storage capacity change rate value set, extracting the temperature sequence of the corresponding unit and the number of charge and discharge times in the cycle, calculating the temperature change amplitude and the average frequency, and generating the temperature rise and cycle rate; S403: Based on the temperature rise and cycle rate, the battery capacity change rate, temperature rise rate and cycle frequency are compared with the set performance thresholds respectively, and cells with all three indicators lower than the lower threshold are screened out, and abnormal status marks are added to obtain health risk identification items.

8. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 1, characterized in that: The method further comprises step S5: S5: Invoke the health risk identification item, delineate the effective frequency regulation response space of energy storage and thermal power in the current cycle based on the weight distribution within the continuous frequency regulation cycle, and output an overview of the frequency regulation capability assessment interval; The frequency regulation capability assessment interval overview includes the energy storage frequency regulation response space, the thermal power frequency regulation response space, and the frequency regulation capability effective interval.

9. The method for evaluating the frequency regulation capability of an energy storage thermal power unit according to claim 8, characterized in that: The steps of the frequency modulation capability evaluation interval overview are as follows: S501: Calling the health risk identification item, extracting the response amplitude and duration of the energy storage and thermal power of the marked unit in the continuous frequency modulation cycle, weighting them by frequency, and generating a cycle response weight group; S502: Normalizing the weighted data of energy storage and thermal power according to the periodic response weight group, screening the continuous output segment, and obtaining the effective response interval; S503: Based on the effective response range, a combined analysis is performed on the segment boundaries of energy storage and thermal power, and grade segments are divided according to the cycle index. The upper and lower response boundaries within each segment are extracted to obtain an overview of the frequency regulation capability evaluation range.

10. The frequency regulation capability evaluation system of energy storage thermal power units is characterized by: The system is used to implement the method for evaluating the frequency regulation capability of an energy storage thermal power unit according to any one of claims 1 to 9, and the system includes: The data merging module classifies nodes based on the operating data of energy storage thermal power units, including grid frequency, energy storage capacity, energy storage charge and discharge status, and thermal power load changes. It also divides frequency fluctuations within the same cycle into intervals, integrates data by time period, and generates frequency modulation input association records. Based on the frequency modulation input association records, the response analysis module extracts the periodic energy storage power fluctuations and the control node adjustment frequency, calculates the ratio of the adjustment amplitude to the frequency change rate, classifies the frequency distribution weights, identifies the energy storage unit's frequency modulation participation, and establishes an energy storage frequency response profile; The load comparison module extracts the thermal power regulation rate data based on the energy storage frequency response portrait, calculates the difference with the energy storage unit response intensity value, extracts the difference section, determines the compensation trend based on the difference distribution, and outputs a load action coordination relationship diagram; The state recognition module calls the capacity change rate, temperature rise rate and cycle frequency of the energy storage unit based on the load action coordination relationship diagram to determine whether the limit is exceeded, and marks the over-limit unit. The results are filtered according to the frequency of overlap between the marked unit and the difference section to obtain the abnormal adaptation unit index table; Based on the abnormal adaptation unit index table, the capacity assessment module extracts the action period and output level within the continuous frequency regulation cycle, performs segment matching in combination with the thermal power response curve, analyzes the overlap of the output trajectories of the two types of resources, identifies the joint coverage interval of the dual regulation capability, and generates a frequency regulation resource collaborative capability zoning map.

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