Thermal power coupling battery data monitoring and safe operation optimization method

Abstract

The invention provides a thermal power coupling battery data monitoring and safe operation optimization method, and relates to the technical field of electric energy monitoring optimization. Comprising the following steps: establishing a combined dynamic simulation model by combining preprocessed historical operation data with real-time operation data, and simulating collaborative operation characteristics of a thermal power generating unit and a battery pack under different load conditions through the combined dynamic simulation model; and analyzing the cooperative operation characteristics of the thermal power generating unit and the battery pack based on the combined dynamic simulation model, determining power distribution, temperature control requirements and response time, and dynamically adjusting the output power of the thermal power generating unit and the charging and discharging power of the battery pack in combination with real-time load data. According to the method, the real-time operation data and the historical operation data of the thermal power generating unit and the battery pack are fused, and the dynamic simulation algorithm is adopted to construct the joint dynamic simulation model, so that accurate description of the cooperative operation characteristics of the thermal power generating unit and the battery pack under different load conditions is realized, and a basis is provided for operation strategy optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy monitoring and optimization, and in particular to a method for data monitoring and safe operation optimization of a thermal power coupling battery. Background Art

[0002] With the growth of energy demand and the increase in the penetration of renewable energy, the load fluctuation and energy efficiency management of power systems face severe challenges.

[0003] The current technology for the coordinated operation of thermal power units and battery energy storage systems is still in the development stage, and there are mainly the following problems: First, there are deficiencies in data fusion and simulation models. The current collaborative operation systems of thermal power units and battery energy storage often lack effective means of integrating historical operation data and real-time operation data, making it difficult to establish accurate dynamic simulation models, thus affecting the effectiveness of collaborative operation strategies.

[0004] Second, the efficiency of load fluctuation prediction and waste heat utilization is low. The existing prediction models have low prediction accuracy for power load fluctuations and waste heat demand, making it difficult to dynamically adjust the output power of thermal power units and the charging and discharging rates of battery packs.

[0005] Third, the utilization rate of waste heat resources generated by thermal power units is low, and an efficient heat recovery and dynamic regulation mechanism has not been formed. Summary of the invention

[0006] The present invention provides a method for thermal power coupled battery data monitoring and safe operation optimization, aiming to solve the technical problems of insufficient data fusion and simulation models in related technologies, thereby avoiding the subsequent problems of load fluctuation prediction and low waste heat utilization efficiency, and avoiding the actual problem of low waste heat resource utilization rate generated by thermal power units.

[0007] In order to achieve the above object, the present invention provides a method for monitoring data of a thermal power coupled battery and optimizing safe operation, comprising the following steps: The real-time operation data and historical operation data of the thermal power unit and the battery unit are obtained, and the historical operation data are preprocessed to obtain the preprocessed historical operation data.

[0008] The pre-processed historical operation data is combined with the real-time operation data to establish a joint dynamic simulation model, and the coordinated operation characteristics of the thermal power unit and the battery group under different load conditions are simulated through the joint dynamic simulation model.

[0009] Based on the joint dynamic simulation model, the coordinated operation characteristics of the thermal power unit and the battery pack are analyzed to determine the power distribution, temperature control requirements and response time. The output power of the thermal power unit and the charging and discharging power of the battery pack are dynamically adjusted based on the real-time load data.

[0010] The waste heat supply demand is calculated based on the joint dynamic simulation model to calculate the battery pack operating temperature and ambient temperature combined with real-time monitoring, and dynamically adjust the waste heat supply; provide heating for the battery pack in a low-temperature environment, and drive the cooling device to cool the battery pack in a high-temperature environment through the heat exchange equipment.

[0011] Based on long-term analysis of the joint dynamic simulation model, combined with the machine learning model, future load fluctuations, waste heat utilization needs and battery performance changes are predicted; the operation strategy is optimized according to the prediction results, and the output power of the thermal power unit, the charge and discharge rate of the battery pack and the temperature control parameters are dynamically adjusted to achieve closed-loop control.

[0012] As a preferred solution of the present invention, real-time operation data and historical operation data of the thermal power unit and the battery unit are obtained, and the historical operation data are preprocessed to obtain the preprocessed historical operation data, which specifically includes: Real-time operation data include: load output data and waste heat emission data of thermal power units, as well as charging status data, temperature distribution data and voltage balance data of battery packs.

[0013] The historical operation data include: load output data and waste heat emission data of thermal power units in the historical operation stage, as well as battery pack charging status data, temperature distribution data and voltage balance data in the historical operation stage.

[0014] Preprocessing includes: data cleaning, feature extraction, data noise reduction, time series segmentation and standardization.

[0015] As a preferred solution of the present invention, the pre-processed historical operation data is combined with the real-time operation data to establish a joint dynamic simulation model, and the coordinated operation characteristics of the thermal power unit and the battery group under different load conditions are simulated by the joint dynamic simulation model. The specific steps are as follows: The pre-processed historical operation data is integrated with the real-time operation data to generate a comprehensive data set, which includes the load output data and waste heat emission data of the thermal power units, as well as the charging status data, temperature distribution data and voltage balance data of the battery pack. The comprehensive data set provides a data basis for subsequent analysis.

[0016] Based on the comprehensive data set, the key characteristic parameters of the historical operation data and the real-time operation data are extracted, and the characteristics are matched according to the operating conditions to generate a matched data set. The matched data set is used to ensure the temporal consistency and characteristic correlation of the data.

[0017] Based on the matched data set, the operating data of the thermal power unit and the battery group are divided into different operating condition categories according to the load status and temperature control requirements. The operating condition categories are used to provide support for the multi-scenario modeling of the joint dynamic simulation model.

[0018] The key parameters are extracted from the data set after the working condition division to generate the input parameters of the joint dynamic simulation model. The input parameters include: power allocation parameters, waste heat utilization efficiency parameters and temperature control response time.

[0019] Based on the operating characteristics and operating mechanisms of thermal power units and battery packs, a joint dynamic simulation model is constructed using a dynamic simulation algorithm. The joint dynamic simulation model is used to describe the collaborative operating characteristics of thermal power units and battery packs under different loads and temperature control requirements.

[0020] The established joint dynamic simulation model is verified using the fused real-time operation data and historical operation data. The accuracy of the model is verified by comparing the model output with the actual data. The model parameters are adjusted according to the verification results.

[0021] Based on the verified joint dynamic simulation model, the coordinated operation status of thermal power units and battery groups under different load and temperature control conditions, output power distribution plan, waste heat utilization strategy and temperature control response time are simulated to provide a reference basis for subsequent optimization and dynamic adjustment.

[0022] As a preferred solution of the present invention, the coordinated operation characteristics of the thermal power unit and the battery pack are analyzed based on the joint dynamic simulation model, the power allocation, temperature control requirements and response time are determined, and the output power of the thermal power unit and the charge and discharge power of the battery pack are dynamically adjusted in combination with real-time load data. The specific steps are as follows: Based on real-time operation data and combined with the collaborative operation characteristics analysis output by the joint dynamic simulation model, the load value is calculated in real time, and it is determined whether the current load state is low or peak.

[0023] When the load value is at a low point, according to the analysis results of the joint dynamic simulation model, the output power of the thermal power units is reduced first, and the excess electric energy is used to charge the battery pack.

[0024] When the load value is at peak, the battery pack discharges to support peak load demand, reduce the load pressure of the thermal power unit, and supplement the output power of the thermal power unit according to demand.

[0025] According to the changing trend of real-time load data, combined with the response time and operating characteristics predicted by the joint dynamic simulation model, the power allocation plan of the thermal power unit and the battery unit is dynamically adjusted to smooth out load fluctuations.

[0026] As a preferred solution of the present invention, based on real-time operation data and combined with the collaborative operation characteristic analysis output by the joint dynamic simulation model, the load value is calculated in real time, and the current load state is judged to be a valley or a peak. The specific steps are as follows: According to the real-time operation data, the current load value is calculated using the following formula:

[0027] In the formula, Indicates the current load value. is the current power generation capacity of the thermal power unit, is the current load demand.

[0028] According to the dynamic changes of real-time load values, the current load status is judged. The specific judgment rules are as follows:

[0029]

[0030] In the formula, and They are the preset load value trough and peak thresholds respectively.

[0031] As a preferred solution of the present invention, the waste heat supply demand is calculated based on the joint dynamic simulation model to calculate the operating temperature of the battery pack and the ambient temperature in combination with real-time monitoring, and dynamically adjust the waste heat supply; provide heating for the battery pack in a low temperature environment, and drive the cooling device to cool the battery pack in a high temperature environment through the heat exchange device. The specific steps are as follows: The calculation formula for the waste heat supply demand of the joint dynamic simulation model is:

[0032] In the formula, Indicates the required heat, Indicates the mass of the battery pack, represents the specific heat capacity of the battery pack, represents the target temperature, Indicates the current temperature.

[0033] Obtain the real-time operating temperature and external ambient temperature data of the battery pack, and dynamically adjust the supply of waste heat from the thermal power unit based on the predicted temperature control demand and real-time temperature data; prioritize waste heat as the temperature control energy source for the battery pack to reduce the consumption of other energy sources.

[0034] In low temperature environments, the waste heat supply is adjusted to provide heat to the battery pack, raising the operating temperature of the battery pack to a preset range.

[0035] In high temperature environments, waste heat is used to drive thermal exchange equipment and cooling devices are used to reduce the temperature of the battery pack.

[0036] As a preferred solution of the present invention, based on the long-term analysis of the joint dynamic simulation model, the machine learning model is combined to predict future load fluctuations, waste heat utilization requirements and battery performance changes; the operation strategy is optimized according to the prediction results, and the output power of the thermal power unit, the charge and discharge rate of the battery pack and the temperature control parameters are dynamically adjusted to achieve closed-loop control. The specific steps are as follows: Based on the joint dynamic simulation model, by integrating historical operation data and real-time operation data, the operating characteristic data of thermal power units and battery groups under different load conditions are analyzed, including the operating efficiency, waste heat utilization rate and battery life of thermal power units and battery groups under different load conditions.

[0037] A machine learning algorithm is used to analyze the operating characteristic data and use it as training samples to build a prediction model. The trained prediction model is used to predict future load fluctuations, waste heat demand trends, and battery performance changes, providing a reference for strategy optimization.

[0038] Based on the prediction model, the future load fluctuation trend is analyzed, the occurrence time and duration of peak load and valley load are determined, and data reference is provided for power distribution adjustment.

[0039] Based on the forecast results of future load fluctuations and combined with the battery temperature control requirements, the time distribution and demand for waste heat supply are calculated, and the waste heat utilization strategy is planned in advance.

[0040] Analyze the charge and discharge cycle characteristics and service life trend of the battery pack, predict the future battery performance attenuation rate and remaining capacity, and provide support for the adjustment of the charge and discharge strategy.

[0041] Dynamically adjust the operating strategies of thermal power units and battery units based on the results of the prediction model.

[0042] As a preferred solution of the present invention, the operation strategies of the thermal power unit and the battery unit are dynamically adjusted according to the results of the prediction model, specifically including: According to the predicted future load fluctuation trend, the output power of thermal power units is reduced during low load, and the output power is increased during peak load, and the response time is dynamically adjusted to respond to the rapid changes in load demand.

[0043] During off-peak load, the battery pack is charged first to store energy. During peak load, the battery pack is discharged to support the load demand, and the discharge rate is dynamically adjusted to extend the service life of the battery pack. The charging and discharging power calculation formula of the battery pack is:

[0044] In the formula, Indicates the charge and discharge power of the battery pack. Represents the available energy storage capacity, Indicates the load duration.

[0045] Combined with the temperature control requirements of the battery pack, the waste heat supply strategy is dynamically optimized, and the waste heat supply strategy after dynamic optimization is verified by calculating the comprehensive efficiency. The calculation formula is:

[0046] In the formula, represents the overall efficiency, represents the effective power output, Indicates the total input power.

[0047] As a preferred solution of the present invention, after the step of dynamically adjusting the operation strategies of the thermal power unit and the battery unit according to the results of the prediction model, the method further includes: After dynamically adjusting the operating strategy, the dynamically adjusted operating strategy is verified through a joint dynamic simulation model, and the impact of the adjustment strategy on power distribution, waste heat utilization efficiency and battery pack life is evaluated by combining real-time operating data and prediction data.

[0048] According to the feedback results of the simulation model, the load response time of the thermal power unit and the battery group is calculated, and the load distribution strategy is optimized. The optimization formula is:

[0049] In the formula, Indicates the response time of the thermal power unit. Battery pack response time.

[0050] Combined with the feedback of the simulation model on the waste heat utilization effect, the operating parameters of the heat exchange equipment are optimized.

[0051] According to the long-term data of the battery pack's operating status, a health status assessment model is introduced to dynamically adjust the battery pack's charge and discharge strategy to extend the battery pack's service life. The health status assessment formula is expressed as:

[0052] In the formula, Indicates the health status of the battery pack. Indicates the cumulative discharge of the battery. Indicates the total discharge capacity of the battery.

[0053] A closed-loop control is formed during the verification and optimization process, and the strategy is continuously adjusted through real-time feedback from the joint dynamic simulation model to achieve stable operation of thermal power units and battery groups under various load conditions, and dynamically update the model input parameters to enhance the prediction accuracy of the simulation model.

[0054] The beneficial effects of the present invention are: 1. By integrating the real-time and historical operating data of thermal power units and battery groups, a joint dynamic simulation model is constructed using a dynamic simulation algorithm, which enables an accurate description of the collaborative operating characteristics of thermal power units and battery groups under different load conditions, providing a basis for optimizing operating strategies.

[0055] 2. Use machine learning algorithms to predict future load fluctuations, waste heat demand and battery performance changes, provide accurate load distribution and waste heat utilization strategies, and improve the response efficiency and waste heat utilization rate of thermal power units.

[0056] 3. Dynamically adjust the charge and discharge rate of the battery pack, optimize the charge and discharge cycle characteristics, slow down the battery performance degradation rate, extend the battery life, and improve the economy of long-term operation.

[0057] 4. Through the combined dynamic simulation model and real-time feedback, a closed-loop control mechanism is formed to optimize the operation strategies of thermal power units and battery groups in real time, achieving stable operation under various load conditions, and dynamically updating the model input parameters to improve the accuracy of prediction and optimization effects.

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 A flow chart of a method for monitoring data and optimizing safe operation of a thermal power coupled battery provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0062] See also Figure 1 , Figure 1 A flowchart provided for an embodiment of the present invention.

[0063] In this embodiment, a method for monitoring data of a thermal power coupled battery and optimizing safe operation includes step S100, step S200, step S300, step S400 and step S500.

[0064] Step S100, obtaining real-time operation data and historical operation data of the thermal power unit and the battery unit, preprocessing the historical operation data, and obtaining the preprocessed historical operation data, specifically includes: Real-time operation data include: load output data and waste heat emission data of thermal power units, as well as charging status data, temperature distribution data and voltage balance data of battery packs.

[0065] It should be noted that the following parameters are obtained through the operation monitoring equipment of the thermal power unit and the battery unit: Load output data of thermal power units: collected in real time through power generation sensors, and the data unit is MW.

[0066] Waste heat emission data: The real-time waste heat emission power is obtained through the heat flow sensor, and the data unit is kW.

[0067] Battery pack charging status data: The battery management system collects real-time charging current and voltage and calculates the SOC.

[0068] Battery pack temperature distribution data: The temperature distribution of each unit in the battery pack is obtained through the battery module temperature sensor. The data unit is Celsius. Voltage balancing data of the battery pack: The voltage difference of each single cell is obtained through the voltage acquisition module, and the data unit is V.

[0069] The historical operation data include: load output data and waste heat emission data of thermal power units in the historical operation stage, as well as battery pack charging status data, temperature distribution data and voltage balance data in the historical operation stage.

[0070] It should be noted that the specific ways to obtain historical operation data are: The historical load output data of thermal power units are based on the time series data extracted from the power generation record log.

[0071] The waste heat emission data is obtained through historical waste heat power records.

[0072] The historical charging state data, temperature distribution data and voltage balancing data of the battery pack are obtained through the log data of the battery management system.

[0073] Preprocessing includes: data cleaning, feature extraction, data noise reduction, time series segmentation and standardization.

[0074] It should be noted that data cleaning is to eliminate erroneous data through missing value processing and outlier detection, and fill in the missing values ​​using linear interpolation.

[0075] Feature extraction is achieved by extracting key characteristic parameters from the data. The key characteristic parameters include the load change rate of the thermal power unit, the temperature gradient of the battery pack, and the voltage fluctuation range.

[0076] Data denoising uses a moving average algorithm to denoise real-time data and historical data to remove interference signals.

[0077] Time series segmentation is to classify and segment historical data according to load status, waste heat output and battery operating conditions for subsequent analysis.

[0078] Data standardization processes data of different dimensions in a unified and standardized manner to ensure the comparability of different characteristic parameters.

[0079] Step S200: Combine the pre-processed historical operation data with the real-time operation data to establish a joint dynamic simulation model, and simulate the coordinated operation characteristics of the thermal power unit and the battery group under different load conditions through the joint dynamic simulation model. The specific steps are as follows: The pre-processed historical operation data is integrated with the real-time operation data to generate a comprehensive data set, which includes the load output data and waste heat emission data of the thermal power units, as well as the charging status data, temperature distribution data and voltage balance data of the battery pack. The comprehensive data set provides a data basis for subsequent analysis.

[0080] Based on the comprehensive data set, the key characteristic parameters of the historical operation data and the real-time operation data are extracted, and the characteristics are matched according to the operating conditions to generate a matched data set. The matched data set is used to ensure the temporal consistency and characteristic correlation of the data.

[0081] It should be noted that operating conditions refer to the key operating parameters that affect the coordinated operating characteristics of thermal power units and battery groups, including the following: Load status: the real-time load output level of the thermal power unit and the charge and discharge status of the battery group.

[0082] Temperature control requirement: The difference between the operating temperature of the battery pack and the ambient temperature, corresponding to the waste heat heating or cooling requirement of the battery pack.

[0083] Waste heat availability: The real-time waste heat emissions of thermal power units affect battery temperature control and energy allocation strategies.

[0084] It should be further explained that feature matching is achieved through the following steps to generate a matched data set: A1. Feature extraction: Extract the following feature parameters from the comprehensive dataset: Real-time load output data and waste heat emission data of thermal power units.

[0085] Battery pack SOC, temperature distribution data and voltage balance data.

[0086] A2. Operating condition screening: According to the definition of operating conditions, which includes load status, temperature control requirements and waste heat availability, screen out data segments that meet the conditions.

[0087] A3. Time series alignment: Match the timestamps of historical data and real-time data to ensure the time series consistency of the two.

[0088] A4. Characteristic correlation check: Verify the physical correlation of the data, for example, the matching degree between the waste heat emission and the battery temperature control requirements; the final matching data set is used for subsequent input.

[0089] Based on the matched data set, the operating data of the thermal power unit and the battery group are divided into different operating condition categories according to the load status and temperature control requirements. The operating condition categories are used to provide support for the multi-scenario modeling of the joint dynamic simulation model.

[0090] It should be noted that the operating condition categories are divided according to the operating status of thermal power units and battery units, including the following types: Low load condition: The output power of the thermal power unit is low and the battery pack is in charging state.

[0091] High load conditions: The output power of the thermal power unit is high and the battery pack is in a discharging state.

[0092] Insufficient waste heat conditions: The waste heat emissions from thermal power units cannot meet the battery temperature control requirements.

[0093] Excess waste heat conditions: The waste heat emissions from thermal power units far exceed the battery temperature control requirements.

[0094] Heating condition: The battery pack needs to be heated using waste heat.

[0095] Cooling conditions: The battery pack needs to be cooled by heat exchange equipment.

[0096] According to the matched data set, the data is divided into the above categories, specifically: For the real-time load data, the working conditions are divided according to the load value determination result in S300.

[0097] The relationship between waste heat emission and battery temperature control requirements is compared to classify waste heat states.

[0098] The heating or cooling condition is determined based on the difference between the real-time temperature of the battery pack and the target temperature.

[0099] The key parameters are extracted from the data set after the working condition division to generate the input parameters of the joint dynamic simulation model. The input parameters include: power allocation parameters, waste heat utilization efficiency parameters and temperature control response time.

[0100] It should be noted that the key parameters include: the power distribution ratio between the thermal power unit and the battery pack; the efficiency of converting waste heat into battery temperature control energy; and the time required for the battery pack to adjust from the current temperature to the target temperature.

[0101] Based on the operating characteristics and operating mechanisms of thermal power units and battery packs, a joint dynamic simulation model is constructed using a dynamic simulation algorithm. The joint dynamic simulation model is used to describe the collaborative operating characteristics of thermal power units and battery packs under different loads and temperature control requirements.

[0102] It should be noted that a dynamic simulation algorithm based on state space modeling was selected to describe the dynamic behavior of the simulation object through state variables, including the power distribution, temperature control response and waste heat utilization of thermal power units and battery groups.

[0103] First, define the variables of the joint dynamic simulation model. The specific definitions are as follows: State variables, including: Indicates the output power of thermal power unit, Indicates the charging and discharging power of the battery pack. Indicates the waste heat supply power, Indicates the battery pack operating temperature.

[0104] Input variables include: real-time operation data and working condition classification results.

[0105] Output variables include: power allocation ratio, temperature control response time and operating efficiency.

[0106] The established joint dynamic simulation model is verified using the fused real-time operation data and historical operation data. The accuracy of the joint dynamic simulation model is verified by comparing the output of the joint dynamic simulation model with the actual data. The parameters of the joint dynamic simulation model are adjusted according to the verification results.

[0107] The joint dynamic simulation model describes the coordinated changes of thermal power units and battery groups in time series based on differential equations. Its mathematical expression formula is:

[0108]

[0109]

[0110] In the formula, Respectively represent the dynamic change rates of thermal power unit output power, battery pack power and battery pack temperature; They represent the nonlinear functions of thermal power unit power allocation, battery pack power adjustment and temperature control dynamic response respectively.

[0111] Subsequently, historical data and real-time data are combined to initialize the parameters of the joint dynamic simulation model; initialize the rated power range of the thermal power unit and the battery pack; initialize the temperature control target range; initialize the initial value of the waste heat utilization efficiency; and the initialization processing is based on the statistical analysis of key parameters using the matched data set.

[0112] After initialization, the numerical method is used to solve the equations of the joint dynamic simulation model, and the Runge-Kutta method is used to iteratively calculate the nonlinear equations of the joint dynamic simulation model to simulate the coordinated changes in the power output and temperature control response of the thermal power unit and the battery pack.

[0113] It should be further explained that the simulation steps of the joint dynamic simulation model are: B1. At each time step, input real-time operating data and predicted operating parameters.

[0114] B2. Calculate the state variables for the next time step according to the equations of the joint dynamic simulation model.

[0115] B3. Output the current power allocation plan, waste heat utilization strategy and battery pack temperature control data.

[0116] Based on the verified joint dynamic simulation model, the coordinated operation status of thermal power units and battery groups under different load and temperature control conditions, output power distribution plan, waste heat utilization strategy and temperature control response time are simulated to provide a reference basis for subsequent optimization and dynamic adjustment.

[0117] It should be noted that the verification is to compare the simulation output with the actual operation data. The comparison conditions are: the power allocation ratio error between the thermal power unit and the battery unit is less than 5%; the battery temperature control response time error is less than 10%.

[0118] It should be further explained that the joint dynamic simulation model adjusts the structure and parameters according to the verification results, including: improving the prediction accuracy of the temperature control response time; enhancing the fit of the waste heat utilization efficiency; and optimizing the dynamic adjustment capability of the power allocation scheme.

[0119] Step S300: Analyze the coordinated operation characteristics of the thermal power unit and the battery pack based on the joint dynamic simulation model, determine the power allocation, temperature control requirements and response time, and dynamically adjust the output power of the thermal power unit and the charge and discharge power of the battery pack in combination with real-time load data. The specific steps are as follows: Based on real-time operation data and combined with the collaborative operation characteristics analysis output by the joint dynamic simulation model, the load value is calculated in real time, and it is determined whether the current load state is low or peak.

[0120] It should be noted that the current load value is calculated based on real-time operating data, and the calculation formula is:

[0121] In the formula, Indicates the current load value. is the current power generation capacity of the thermal power unit, is the current load demand; According to the dynamic changes of real-time load values, the current load status is judged. The specific judgment rules are as follows:

[0122]

[0123] In the formula, and They are the preset load value trough and peak thresholds respectively.

[0124] When the load value is at a low point, according to the analysis results of the joint dynamic simulation model, the output power of the thermal power units is reduced first, and the excess electric energy is used to charge the battery pack.

[0125] It should be noted that according to the results of the joint dynamic simulation model analysis, the power of the thermal power units that can be reduced is determined. ; and calculate the acceptable charging power of the battery pack , so that it satisfies the following constraints:

[0126] In the formula, Indicates the maximum charging power of the battery pack. Indicates the current charging power of the battery pack.

[0127] Adjust the power output of the thermal power unit to At the same time, the battery pack is triggered to charge.

[0128] When the load value is at peak, the battery pack discharges to support peak load demand, reduce the load pressure of the thermal power unit, and supplement the output power of the thermal power unit according to demand.

[0129] It should be noted that the discharge power of the battery pack is determined according to the power allocation scheme predicted by the joint dynamic simulation model. :

[0130] In the formula, Indicates the current available discharge capacity of the battery pack.

[0131] Supplement the output power of thermal power units to meet the remaining load requirements.

[0132] According to the changing trend of real-time load data, combined with the response time and operating characteristics predicted by the joint dynamic simulation model, the power allocation plan of the thermal power unit and the battery unit is dynamically adjusted to smooth out load fluctuations.

[0133] It should be noted that the short-term load change trend is predicted, the load status classification standard is adjusted, and dynamic allocation is achieved:

[0134] In the formula, Indicates the power allocation ratio of thermal power units, Indicates the power distribution ratio of the battery pack, is the current load demand, represents the adjusted output power, Indicates the adjusted battery discharge power.

[0135] Step S400, based on the joint dynamic simulation model, the waste heat supply demand is calculated to calculate the operating temperature of the battery pack and the ambient temperature in combination with real-time monitoring, and the waste heat supply is dynamically adjusted; heating is provided for the battery pack in a low temperature environment, and cooling of the battery pack is driven by a cooling device through a heat exchange device in a high temperature environment. The specific steps are as follows: The calculation formula for the waste heat supply demand of the joint dynamic simulation model is:

[0136] In the formula, Indicates the required heat, Indicates the mass of the battery pack, represents the specific heat capacity of the battery pack, represents the target temperature, Indicates the current temperature.

[0137] Obtain the real-time operating temperature and external ambient temperature data of the battery pack, and dynamically adjust the supply of waste heat from the thermal power unit based on the predicted temperature control demand and real-time temperature data; prioritize waste heat as the temperature control energy source for the battery pack to reduce the consumption of other energy sources.

[0138] In low temperature environments, the waste heat supply is adjusted to provide heat to the battery pack, raising the operating temperature of the battery pack to a preset range.

[0139] It should be noted that in a low-temperature environment, waste heat supply is allocated to the battery pack first, and the temperature is quickly increased by adjusting the supply path and flow rate to ensure stable operation of the battery pack.

[0140] It should be further noted that the preset range is 15°C to 40°C.

[0141] In high temperature environments, waste heat is used to drive thermal exchange equipment and cooling devices are used to reduce the temperature of the battery pack.

[0142] It should be noted that in a high temperature environment, waste heat is used to drive the cooling device to reduce the battery pack temperature, and the cooling path and power distribution are optimized in combination with real-time cooling efficiency data.

[0143] It should be noted that the joint dynamic simulation model calculates the waste heat demand based on the real-time temperature of the battery pack and the ambient temperature, dynamically adjusts the supply power ratio, gives priority to meeting the temperature control target, and ensures efficient use of waste heat.

[0144] Step S500: Based on the long-term analysis of the joint dynamic simulation model, the future load fluctuation, waste heat utilization demand and battery performance change are predicted in combination with the machine learning model; the operation strategy is optimized according to the prediction results, and the output power of the thermal power unit, the charge and discharge rate of the battery pack and the temperature control parameters are dynamically adjusted to achieve closed-loop control. The specific steps are as follows: Based on the joint dynamic simulation model, by integrating historical operation data and real-time operation data, the operating characteristic data of thermal power units and battery groups under different load conditions are analyzed, including the operating efficiency, waste heat utilization rate and battery life of thermal power units and battery groups under different load conditions.

[0145] A machine learning algorithm is used to analyze the operating characteristic data and use it as training samples to build a prediction model. The trained prediction model is used to predict future load fluctuations, waste heat demand trends, and battery performance changes, providing a reference for strategy optimization.

[0146] Based on the prediction model, the future load fluctuation trend is analyzed, the occurrence time and duration of peak load and valley load are determined, and data reference is provided for power distribution adjustment.

[0147] Based on the forecast results of future load fluctuations and combined with the battery temperature control requirements, the time distribution and demand for waste heat supply are calculated, and the waste heat utilization strategy is planned in advance.

[0148] Analyze the charge and discharge cycle characteristics and service life trend of the battery pack, predict the future battery performance attenuation rate and remaining capacity, and provide support for the adjustment of the charge and discharge strategy.

[0149] It should be noted that LSTM is used to build a prediction model to process the time series characteristics of the operating characteristic data of thermal power units and battery groups, and to predict future waste heat demand and battery performance changes through vector regression. After preprocessing the historical operating data, the data with characteristics in the historical operating data are used as training samples to train the prediction model.

[0150] It should be further explained that based on the output of the prediction model, the future load fluctuation trend is analyzed, including the occurrence time and duration of peak load and valley load, to provide reference data for power allocation adjustment; and based on the predicted battery temperature control requirements, the time distribution and demand for waste heat supply are calculated, and the waste heat utilization strategy is planned in advance.

[0151] It should be further explained that the prediction model is used to estimate the charge and discharge cycle characteristics of the battery pack, analyze the battery life trend, calculate the battery performance degradation rate and remaining capacity; based on the battery performance prediction results, the charge and discharge strategy is dynamically adjusted to ensure that the battery life is maximized.

[0152] It should be further explained that the predicted waste heat demand and temperature control demand results are combined to dynamically adjust the supply priority of the waste heat of the thermal power units and plan the operating time and efficiency distribution of the heat exchange equipment.

[0153] Dynamically adjust the operating strategies of thermal power units and battery units based on the results of the prediction model.

[0154] It should be noted that according to the predicted future load fluctuation trend, the output power of thermal power units will be reduced during low load periods, and the output power will be increased during peak load periods, and the response time will be dynamically adjusted to respond to rapid load changes.

[0155] During off-peak load, the battery pack is charged first to store energy. During peak load, the battery pack is discharged to support the load demand, and the discharge rate is dynamically adjusted to extend the service life of the battery pack. The charging and discharging power calculation formula of the battery pack is:

[0156] In the formula, Indicates the charge and discharge power of the battery pack. Represents the available energy storage capacity, Indicates the load duration.

[0157] Combined with the temperature control requirements of the battery pack, the waste heat supply strategy is dynamically optimized, and the waste heat supply strategy after dynamic optimization is verified by calculating the comprehensive efficiency. The calculation formula is:

[0158] In the formula, represents the overall efficiency, represents the effective power output, Indicates the total input power.

[0159] It should be further explained that after step S500 is executed, the following steps are also included: After dynamically adjusting the operating strategy, the dynamically adjusted operating strategy is verified through a joint dynamic simulation model, and the impact of the adjustment strategy on power distribution, waste heat utilization efficiency and battery pack life is evaluated by combining real-time operating data and prediction data.

[0160] According to the feedback results of the joint dynamic simulation model, the load response time of the thermal power unit and the battery unit is calculated, and the load distribution strategy is optimized. The optimization formula is:

[0161] In the formula, Indicates the response time of the thermal power unit. Battery pack response time.

[0162] Combined with the feedback of the joint dynamic simulation model on the waste heat utilization effect, the operating parameters of the heat exchange equipment are optimized.

[0163] According to the long-term data of the battery pack's operating status, a health status assessment model is introduced to dynamically adjust the battery pack's charge and discharge strategy to extend the battery pack's service life. The health status assessment formula is expressed as:

[0164] In the formula, Indicates the health status of the battery pack. Indicates the cumulative discharge of the battery. Indicates the total discharge capacity of the battery.

[0165] A closed-loop control is formed during the verification and optimization process, and the strategy is continuously adjusted through real-time feedback from the joint dynamic simulation model to achieve stable operation of thermal power units and battery units under various load conditions, and the input parameters of the joint dynamic simulation model are dynamically updated to enhance prediction accuracy.

[0166] At this point, a method for thermal power coupled battery data monitoring and safe operation optimization has been completed.

[0167] The above is only a specific implementation of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the embodiments of the present invention, which should be included in the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for monitoring data and optimizing safe operation of thermal power coupled batteries, characterized in that: The following steps are involved: Acquire real-time operation data and historical operation data of thermal power units and battery groups, pre-process the historical operation data, and obtain the pre-processed historical operation data; The pre-processed historical operation data is combined with the real-time operation data to establish a joint dynamic simulation model, and the coordinated operation characteristics of the thermal power unit and the battery group under different load conditions are simulated through the joint dynamic simulation model; Analyze the coordinated operation characteristics of thermal power units and battery packs based on the joint dynamic simulation model, determine power allocation, temperature control requirements and response time, and dynamically adjust the output power of thermal power units and the charging and discharging power of battery packs based on real-time load data; Calculate waste heat supply demand based on a joint dynamic simulation model to calculate the battery pack operating temperature and ambient temperature combined with real-time monitoring to dynamically adjust waste heat supply; Provide heating for the battery pack in low temperature environment, and drive the cooling device to cool the battery pack in high temperature environment through the heat exchange equipment; Based on long-term analysis of the joint dynamic simulation model, combined with the machine learning model, future load fluctuations, waste heat utilization needs and battery performance changes are predicted; the operation strategy is optimized according to the prediction results, and the output power of the thermal power unit, the charge and discharge rate of the battery pack and the temperature control parameters are dynamically adjusted to achieve closed-loop control.

2. A method for monitoring data and optimizing safe operation of a thermal power coupling battery according to claim 1, characterized in that: Obtain the real-time operation data and historical operation data of the thermal power unit and the battery unit, pre-process the historical operation data, and obtain the pre-processed historical operation data, including: Real-time operation data include: load output data and waste heat emission data of thermal power units, as well as charging status data, temperature distribution data and voltage balance data of battery packs; The historical operation data include: load output data and waste heat emission data of thermal power units in the historical operation stage, as well as battery pack charging status data, temperature distribution data and voltage balance data in the historical operation stage; Preprocessing includes: data cleaning, feature extraction, data noise reduction, time series segmentation and standardization.

3. A method for monitoring data and optimizing safe operation of a thermal power coupling battery according to claim 1, characterized in that: The pre-processed historical operation data is combined with the real-time operation data to establish a joint dynamic simulation model. The joint dynamic simulation model is used to simulate the coordinated operation characteristics of the thermal power unit and the battery group under different load conditions. The specific steps are as follows: The pre-processed historical operation data is integrated with the real-time operation data to generate a comprehensive data set, which includes the load output data and waste heat emission data of the thermal power unit, as well as the charging state data, temperature distribution data and voltage balance data of the battery pack. The comprehensive data set provides a data basis for subsequent analysis; Based on the comprehensive data set, key characteristic parameters of historical operation data and real-time operation data are extracted, and feature matching is performed according to the operating conditions to generate a matched data set. The matched data set is used to ensure the temporal consistency and feature relevance of the data. Based on the matched data set, the operating data of the thermal power unit and the battery pack are divided into different operating condition categories according to the load status and temperature control requirements. The operating condition categories are used to provide support for multi-scenario modeling of the joint dynamic simulation model. Extract key parameters from the data set after the working condition division to generate input parameters of the joint dynamic simulation model, including power allocation parameters, waste heat utilization efficiency parameters and temperature control response time; Based on the operating characteristics and operating mechanisms of thermal power units and battery packs, a joint dynamic simulation model is constructed using a dynamic simulation algorithm. The joint dynamic simulation model is used to describe the collaborative operating characteristics of thermal power units and battery packs under different loads and temperature control requirements. Use the fused real-time operation data and historical operation data to verify the established joint dynamic simulation model, and verify the accuracy of the model by comparing the model output with the actual data; adjust the model parameters according to the verification results; Based on the verified joint dynamic simulation model, the coordinated operation status of thermal power units and battery groups under different load and temperature control conditions, output power distribution plan, waste heat utilization strategy and temperature control response time are simulated to provide a reference basis for subsequent optimization and dynamic adjustment.

4. A method for data monitoring and safe operation optimization of thermal power coupling batteries according to claim 1, characterized in that: Based on the joint dynamic simulation model, the coordinated operation characteristics of the thermal power unit and the battery pack are analyzed to determine the power distribution, temperature control requirements and response time. The output power of the thermal power unit and the charge and discharge power of the battery pack are dynamically adjusted in combination with real-time load data. The specific steps are as follows: Based on real-time operation data and the collaborative operation characteristics analysis output by the joint dynamic simulation model, the load value is calculated in real time, and the current load state is judged as a valley or peak; When the load value is low, according to the analysis results of the joint dynamic simulation model, the output power of the thermal power unit is reduced first, and the excess electric energy is used to charge the battery pack; When the load value is at peak, the battery pack discharges to support the peak load demand, reducing the load pressure of the thermal power unit, and supplementing the output power of the thermal power unit according to demand; According to the changing trend of real-time load data, combined with the response time and operating characteristics predicted by the joint dynamic simulation model, the power allocation plan of the thermal power unit and the battery unit is dynamically adjusted to smooth out load fluctuations.

5. A method for data monitoring and safe operation optimization of thermal power coupling batteries as claimed in claim 4, characterized in that: Based on real-time operation data and the analysis of the coordinated operation characteristics output by the joint dynamic simulation model, the load value is calculated in real time, and the current load state is judged as a valley or peak. The specific steps are as follows: According to the real-time operation data, the current load value is calculated using the following formula: ； In the formula, Indicates the current load value. is the current power generation capacity of the thermal power unit, is the current load demand; According to the dynamic changes of real-time load values, the current load status is judged. The specific judgment rules are as follows: ； ； In the formula, and They are the preset load value trough and peak thresholds respectively.

6. A method for monitoring data and optimizing safe operation of a thermal power coupling battery according to claim 4, characterized in that: The waste heat supply demand is calculated based on the joint dynamic simulation model to calculate the battery pack operating temperature and ambient temperature combined with real-time monitoring, and dynamically adjust the waste heat supply; provide heating for the battery pack in a low temperature environment, and drive the cooling device to cool the battery pack in a high temperature environment through the heat exchange device. The specific steps are as follows: The calculation formula for the waste heat supply demand of the joint dynamic simulation model is: ； In the formula, Indicates the required heat, Indicates the mass of the battery pack, represents the specific heat capacity of the battery pack, represents the target temperature, Indicates the current temperature; Obtain the real-time operating temperature and external environment temperature data of the battery pack, and dynamically adjust the supply of waste heat from the thermal power unit based on the predicted temperature control demand and real-time temperature data; give priority to using waste heat as the temperature control energy of the battery pack to reduce the consumption of other energy sources; In low temperature environment, the waste heat supply is adjusted to provide heat to the battery pack, raising the operating temperature of the battery pack to the preset range; In high temperature environments, waste heat is used to drive thermal exchange equipment and cooling devices are used to reduce the temperature of the battery pack.

7. A method for monitoring data and optimizing safe operation of a thermal power coupling battery according to claim 1, characterized in that: Based on the long-term analysis of the joint dynamic simulation model, the machine learning model is combined to predict future load fluctuations, waste heat utilization requirements and battery performance changes; the operation strategy is optimized according to the prediction results, and the output power of the thermal power unit, the charge and discharge rate of the battery pack and the temperature control parameters are dynamically adjusted to achieve closed-loop control. The specific steps are as follows: Based on the joint dynamic simulation model, by integrating historical operation data and real-time operation data, the operating characteristic data of thermal power units and battery groups under different load conditions are analyzed, including the operating efficiency, waste heat utilization rate and battery life of thermal power units and battery groups under different load conditions; Adopt machine learning algorithms, use the analyzed operating characteristic data as training samples, and build a prediction model. Use the trained prediction model to predict future load fluctuations, waste heat demand trends, and battery performance changes, providing a reference for strategy optimization. Based on the prediction model, analyze the future load fluctuation trend, determine the occurrence time and duration of peak load and valley load, and provide data reference for power distribution adjustment; Based on the prediction results of future load fluctuations and the battery temperature control requirements, the time distribution and demand of waste heat supply are calculated, and the waste heat utilization strategy is planned in advance; Analyze the charge and discharge cycle characteristics and service life trend of the battery pack, predict the future battery performance decay rate and remaining capacity, and provide support for the adjustment of the charge and discharge strategy; Dynamically adjust the operating strategies of thermal power units and battery units based on the results of the prediction model.

8. A method for monitoring data and optimizing safe operation of a thermal power coupling battery according to claim 7, characterized in that: The dynamically adjusting the operation strategies of the thermal power unit and the battery unit according to the results of the prediction model specifically includes: According to the predicted future load fluctuation trend, reduce the output power of thermal power units during low load; increase the output power during peak load, dynamically adjust the response time, and respond to the rapid load change demand; During off-peak load, the battery pack is charged first to store energy; during peak load, the battery pack is discharged to support the load demand, and the discharge rate is dynamically adjusted to extend the service life of the battery pack; the charging and discharging power calculation formula of the battery pack is: ； In the formula, Indicates the charge and discharge power of the battery pack. Represents the available energy storage capacity, Indicates the load duration; Combined with the temperature control requirements of the battery pack, the waste heat supply strategy is dynamically optimized, and the waste heat supply strategy after dynamic optimization is verified by calculating the comprehensive efficiency. The calculation formula is: ； In the formula, represents the overall efficiency, represents the effective power output, Indicates the total input power.

9. A method for data monitoring and safe operation optimization of thermal power coupling batteries as claimed in claim 8, characterized in that: After the step of dynamically adjusting the operation strategies of the thermal power unit and the battery unit according to the result of the prediction model, the method further includes: After dynamically adjusting the operation strategy, the dynamically adjusted operation strategy is verified through the joint dynamic simulation model, and the impact of the adjustment strategy on power distribution, waste heat utilization efficiency and battery pack life is evaluated by combining real-time operation data and prediction data; According to the feedback results of the simulation model, the load response time of the thermal power unit and the battery group is calculated, and the load distribution strategy is optimized. The optimization formula is: ； In the formula, Indicates the response time of the thermal power unit. Battery pack response time; Combined with the feedback of the simulation model on the waste heat utilization effect, the operating parameters of the heat exchange equipment are optimized; According to the long-term data of the battery pack's operating status, a health status assessment model is introduced to dynamically adjust the battery pack's charge and discharge strategy to extend the battery pack's service life. The health status assessment formula is expressed as: ； In the formula, Indicates the health status of the battery pack. Indicates the cumulative discharge of the battery. Indicates the total discharge capacity of the battery; A closed-loop control is formed during the verification and optimization process, and the strategy is continuously adjusted through real-time feedback from the joint dynamic simulation model to achieve stable operation of thermal power units and battery groups under various load conditions, and dynamically update the model input parameters to enhance the prediction accuracy of the simulation model.

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