A large-scale energy storage power station operation monitoring management system and management method

Abstract

The present invention provides a large-scale energy storage power station operation monitoring management system and management method, which relates to the technical field of electric energy storage systems, including: a data acquisition module is responsible for collecting battery pack status and external environmental parameters in real time; a model construction module uses historical data to train the battery health status model to obtain the current health coefficient; a data processing module analyzes the operating temperature of each monomer and calculates the thermal balance coefficient; a coefficient correction module combines the environmental impact coefficient to evaluate the comprehensive health coefficient of the energy storage power station; an evaluation module calculates the profit index based on the comprehensive health coefficient, electricity price and load demand, and comprehensively evaluates the economic benefits of the energy storage power station. Through meticulous monitoring and analysis, the present invention timely identifies the risks of battery aging and performance degradation, improves the battery's charging and discharging efficiency and energy storage capacity, and provides a more comprehensive profit index evaluation, so that managers can more accurately understand the economic benefits of the energy storage power station and achieve the best charging and discharging strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage systems, and in particular to a large-scale energy storage power station operation monitoring management system and a management method. Background Art

[0002] With the continuous promotion and application of clean energy, electrochemical energy storage technology, as an efficient, flexible, reliable and low-emission energy storage technology, has broad application prospects. However, the management and control of battery life has become a key issue in energy storage power stations. The short battery life will cause the battery to need to be replaced more frequently, increasing maintenance costs, while long-term use will cause battery performance to deteriorate, reducing the efficiency and reliability of energy storage power stations. Therefore, considering battery life is crucial for the long-term operation of energy storage power stations.

[0003] In the prior art, a method and system for optimizing the operation of a large-scale energy storage power station with a publication number of CN116526529A is disclosed. The method includes: based on a preset number of charging and discharging strategies, the charging and discharging power, environmental heat loss, and refrigeration heat loss of a T time period are input into the energy storage power station operation simulation model to obtain the temperature, current, port voltage, and charge level of the energy storage power station battery, and then obtain the battery capacity loss; obtain the battery aging cost based on the battery capacity loss, and obtain energy arbitrage based on the charging and discharging power and a preset number of charging and discharging strategies; obtain a preset number of decision scores based on a preset number of battery aging costs and energy arbitrage, and determine the optimal charging and discharging strategy for the T time period based on the decision strategy based on the decision score. This method comprehensively considers the revenue size and aging cost of the energy storage power station operation process, obtains the optimal strategy for the operation control of the energy storage power station, and achieves the dual optimization of extending the service life of the energy storage power station and increasing the revenue of the energy storage power station.

[0004] However, there are still the following deficiencies. As can be seen from the above statements, although the existing technology takes into account the heat loss from the environment, it lacks a detailed analysis of the temperature distribution of each cell inside the battery pack, and fails to effectively monitor and manage the thermal equilibrium state of the battery pack, resulting in the risk of battery aging and performance degradation not being identified in a timely manner, thereby affecting the battery's charging and discharging efficiency and energy storage capacity, thereby affecting the overall health and service life of the battery; the benefit evaluation mainly focuses on the simple combination of battery aging costs and energy arbitrage, and fails to fully consider the comprehensive impact of multiple factors such as electricity price fluctuations and load demand on benefits, resulting in managers being unable to fully evaluate the economic benefits of energy storage power stations, causing energy storage power stations to fail to achieve the best charging and discharging strategy.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a large-scale energy storage power station operation monitoring management system and management method to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A large-scale energy storage power station operation monitoring and management system, comprising:

[0009] A data acquisition module is used to collect characteristic parameters of the energy storage power station in the time period t before the current moment, and external environmental parameters at the current moment. The characteristic parameters of the energy storage power station include the battery pack state and the operating temperature of each monomer of the battery pack. The battery pack state includes the actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles. The external environmental parameters include the temperature of the energy storage power station, the humidity of the energy storage power station, the electricity price and the load demand;

[0010] A model building module is used to take the actual available capacity of the battery pack in the previous t time period, the voltage in the static state, the output power and the number of charge and discharge cycles as input, and the health factor of the battery pack as a label. The health factor of the battery pack is evaluated by an expert group, train the health state model of the battery pack, obtain the trained model, input the actual available capacity of the battery pack at the current moment, the voltage in the static state and the number of charge and discharge cycles into the health state model of the battery pack, and obtain the health factor of the battery pack at the current moment;

[0011] A data processing module is used to process the operating temperature of each cell of the battery pack collected at the current moment, obtain the average temperature of all cells in the battery pack, calculate the standard deviation between the cell temperature and the average temperature based on the average temperature of all cells, and obtain the thermal balance coefficient of the battery pack at the current moment based on the standard deviation;

[0012] The coefficient correction module is used to analyze and process the temperature and humidity of the energy storage power station at the current moment, obtain the environmental impact coefficient of the energy storage power station at the current moment, analyze and process the health coefficient and thermal balance coefficient of the battery pack at the current moment, and the environmental impact coefficient of the energy storage power station, and obtain the comprehensive health coefficient of the current energy storage power station;

[0013] The evaluation module is used to analyze and process the comprehensive health coefficient, electricity price and load demand of the energy storage power station at the current moment, obtain the profit index of the energy storage power station at the current moment, and evaluate the profit of the energy storage power station based on the profit index.

[0014] Furthermore, the health status model of the battery pack adopts a convolutional neural network model, which is composed of a deep neural network based on a multilayer perceptron. The deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer. The first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function.

[0015] Furthermore, the operating temperature of each cell of the battery pack collected at the current moment is processed to obtain the average temperature of all cells in the battery pack. According to the average temperature of all cells, the standard deviation between the cell temperature and the average temperature is calculated. According to the standard deviation, the thermal balance coefficient of the battery pack at the current moment is obtained. The specific process is as follows:

[0016] Collect the operating temperature of each cell in the battery pack at the current moment. Assuming there are n cells in the battery pack, the temperature data of the cell is ;

[0017] Calculate the average value of all monomer temperatures according to the following formula:

[0018] ;

[0019] in, is the average temperature of all monomers, For the The temperature of each monomer, is the index of the cell in the battery pack, The value range is , is the number of cells in the battery pack;

[0020] Calculate the standard deviation of all monomer temperatures from the average temperature according to the following formula:

[0021] ;

[0022] in, is the standard deviation of all monomer temperatures from the average temperature;

[0023] The thermal equilibrium coefficient is calculated based on the standard deviation according to the following formula:

[0024] ;

[0025] in, is the thermal balance coefficient of the battery pack at the current moment.

[0026] Furthermore, the temperature and humidity of the energy storage power station at the current moment are analyzed and processed to obtain the environmental impact coefficient of the energy storage power station at the current moment. The specific process is as follows:

[0027] ;

[0028] in, is the environmental impact coefficient of the energy storage power station at the current moment, is the temperature of the energy storage power station at the current moment, is the humidity of the energy storage power station at the current moment, is the weight coefficient of the temperature of the energy storage power station at the current moment, is the weight coefficient of the humidity of the energy storage power station at the current moment, For the suitable temperature of the energy storage power station, For the suitable humidity of the energy storage power station, is a constant term, ,set up .

[0029] Furthermore, the health coefficient and thermal balance coefficient of the battery pack at the current moment, as well as the environmental impact coefficient of the energy storage power station, are analyzed and processed to obtain the comprehensive health coefficient of the current energy storage power station. The specific process is as follows:

[0030] ;

[0031] in, is the comprehensive health coefficient of the current energy storage power station, is the health factor of the battery pack at the current moment, is the weight coefficient of the health factor of the battery pack at the current moment, is the weight coefficient of the thermal balance coefficient of the battery pack at the current moment, is the weight coefficient of the environmental impact coefficient of the energy storage power station, , ;

[0032] Comprehensive health index The value range of is between 0 and 1;

[0033] when Close to 0, it means the battery pack is unhealthy due to battery aging, uneven temperature and adverse environmental conditions;

[0034] when Close to 1, the battery pack is healthy, showing good performance, with balanced temperatures and suitable environmental conditions.

[0035] Furthermore, the comprehensive health coefficient of the energy storage power station at the current moment, the electricity price, and the load demand are analyzed and processed to obtain the profit index of the energy storage power station at the current moment, and the profit of the energy storage power station is evaluated based on the profit index, according to the following formula:

[0036] ;

[0037] in, is the profit index of the energy storage power station at the current moment, is the current electricity price, is the load demand at the current moment, is the index of comprehensive health coefficient, ;

[0038] The value range of is [0,10), and different values ​​within this range reflect the economic performance of the energy storage power station:

[0039] when Close to 0: Indicates that the revenue of the energy storage power station is close to zero, due to low electricity prices, low load demand or poor battery health;

[0040] when Close to 10: This means that the benefits of the energy storage power station are very high, which means that there are high electricity prices, high load demand and good battery health at the same time.

[0041] To achieve the above object, the present invention also provides the following technical solutions:

[0042] A large-scale energy storage power station operation monitoring and management method, the system is used to execute any of the large-scale energy storage power station operation monitoring and management systems described above, and the specific steps include:

[0043] S1. Collect characteristic parameters of the energy storage power station in the time period t before the current moment, and the external environmental parameters at the current moment, wherein the characteristic parameters of the energy storage power station include the battery pack state and the operating temperature of each monomer of the battery pack, the battery pack state includes the actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles, and the external environmental parameters include the temperature of the energy storage power station, the humidity of the energy storage power station, the electricity price and the load demand;

[0044] S2. Take the actual available capacity, voltage in a static state, output power and number of charge and discharge cycles of the battery pack in the previous t time period as input, and the health factor of the battery pack as a label. The health factor of the battery pack is evaluated by an expert group, train the health status model of the battery pack, obtain the trained model, input the actual available capacity, voltage in a static state and number of charge and discharge cycles of the battery pack at the current moment into the health status model of the battery pack, and obtain the health factor of the battery pack at the current moment;

[0045] S3. Process the operating temperature of each cell of the battery pack collected at the current moment to obtain the average temperature of all cells in the battery pack, calculate the standard deviation between the cell temperature and the average temperature based on the average temperature of all cells, and obtain the thermal balance coefficient of the battery pack at the current moment based on the standard deviation;

[0046] S4. Analyze and process the temperature and humidity of the energy storage power station at the current moment to obtain the environmental impact coefficient of the energy storage power station at the current moment, analyze and process the health coefficient and thermal balance coefficient of the battery pack at the current moment, and the environmental impact coefficient of the energy storage power station to obtain the comprehensive health coefficient of the current energy storage power station;

[0047] S5. Perform data analysis and processing on the comprehensive health coefficient, electricity price, and load demand of the energy storage power station at the current moment to obtain the profit index of the energy storage power station at the current moment, and evaluate the profit situation of the energy storage power station based on the profit index.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention can obtain the operating temperature of each monomer in real time through the data processing module, and calculate the average temperature and its standard deviation, so as to accurately evaluate the thermal balance coefficient, which is helpful to timely identify the risks of battery aging and performance degradation, improve the battery's charging and discharging efficiency and energy storage capacity, and maintain the overall health and service life of the battery. The coefficient correction module generates an environmental impact coefficient by analyzing environmental parameters, and then combines the health coefficient and the thermal balance coefficient to provide a comprehensive health assessment of the energy storage power station. This comprehensive environmental analysis ensures the reliable and safe operation of the energy storage power station under different climatic conditions. The evaluation module combines the comprehensive health coefficient, electricity price and load demand to provide a more comprehensive benefit index evaluation, so that managers can more accurately understand the economic benefits of the energy storage power station and achieve the best charging and discharging strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0051] Figure 2 This is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0053] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0054] Embodiment 1:

[0055] See also Figure 1 , the present invention provides a technical solution:

[0056] A large-scale energy storage power station operation monitoring and management system, comprising:

[0057] A data acquisition module is used to collect characteristic parameters of the energy storage power station in the time period t before the current moment, and external environmental parameters at the current moment. The characteristic parameters of the energy storage power station include the battery pack state and the operating temperature of each monomer of the battery pack. The battery pack state includes the actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles. The external environmental parameters include the temperature of the energy storage power station, the humidity of the energy storage power station, the electricity price and the load demand;

[0058] A model building module is used to take the actual available capacity of the battery pack, the voltage in a static state, the output power and the number of charge and discharge cycles in the previous t time period as input, and the health factor of the battery pack as a label. The health factor of the battery pack is evaluated by an expert group. The larger the health factor of the battery pack, the better the performance of the corresponding battery pack. The health status model of the battery pack is trained to obtain a trained model, and the actual available capacity of the battery pack at the current moment, the voltage in a static state and the number of charge and discharge cycles are input into the health status model of the battery pack to obtain the health coefficient of the battery pack at the current moment;

[0059] A data processing module is used to process the operating temperature of each cell of the battery pack collected at the current moment, obtain the average temperature of all cells in the battery pack, calculate the standard deviation between the cell temperature and the average temperature based on the average temperature of all cells, and obtain the thermal balance coefficient of the battery pack at the current moment based on the standard deviation;

[0060] The coefficient correction module is used to analyze and process the temperature and humidity of the energy storage power station at the current moment, obtain the environmental impact coefficient of the energy storage power station at the current moment, analyze and process the health coefficient and thermal balance coefficient of the battery pack at the current moment, and the environmental impact coefficient of the energy storage power station, and obtain the comprehensive health coefficient of the current energy storage power station;

[0061] The evaluation module is used to analyze and process the comprehensive health coefficient, electricity price and load demand of the energy storage power station at the current moment, obtain the profit index of the energy storage power station at the current moment, and evaluate the profit of the energy storage power station based on the profit index.

[0062] In the embodiment, the large-scale energy storage power station operation monitoring and management system adopts a distributed architecture, which can monitor and manage multiple energy storage power stations, each of which contains tens of thousands of battery cells.

[0063] On the basis of the above embodiment, the actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles, the temperature of the energy storage power station, the humidity of the energy storage power station, the electricity price, the load demand, and the operating temperature of each monomer of the battery pack are collected by the following equipment and method:

[0064] The actual available capacity of the battery pack is calculated based on the current, voltage and time of the battery pack, while the current and voltage of the battery pack are obtained through the current sensor and voltage sensor respectively; the operating temperature of the battery is fed back in real time by being installed on each cell of the battery pack; the voltage in a static state is equal to the voltage of the battery pack; the ambient temperature and humidity around the energy storage power station are obtained in real time through the environmental sensor of the weather station; the output power of the battery pack is calculated based on the current and voltage of the battery pack, and the number of cycles is recorded by counting each charge and discharge; real-time electricity price information is obtained through the power grid system; the load demand is obtained through the load information provided by the power grid company.

[0065] Based on the above embodiment, the health status model of the battery pack adopts a convolutional neural network model, and the convolutional neural network model is composed of a deep neural network based on a multilayer perceptron. The deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer. The first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function.

[0066] In this embodiment, the input features of the deep neural network of the multilayer perceptron include: the actual available capacity of the battery pack, the voltage in a static state, the output power, and the number of charge and discharge cycles, four features;

[0067] Input layer: receives input of 4 features;

[0068] The first hidden layer has 128 neurons and uses ReLU as the activation function.

[0069] The second hidden layer has 256 neurons and also uses the ReLU activation function.

[0070] The third hidden layer has 128 neurons and uses the ReLU activation function.

[0071] Output layer: has a single neuron, the health factor of the battery pack.

[0072] The process of training the health status model of the battery pack is as follows:

[0073] The expert group will assess the health factor of the corresponding battery pack based on the actual available capacity, static voltage, output power and number of charge and discharge cycles of the battery pack in the past;

[0074] The actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles are used as input, and the health coefficient of the corresponding battery pack is used as the output label for training. The mean square error is used as the loss function. When the mean square error is When the battery is within the range, the training of the health status model of the battery pack is completed.

[0075] On the basis of the above embodiment, the operating temperature of each cell of the battery pack collected at the current moment is processed to obtain the average temperature of all cells in the battery pack. According to the average temperature of all cells, the standard deviation between the cell temperature and the average temperature is calculated. According to the standard deviation, the thermal balance coefficient of the battery pack at the current moment is obtained. The specific process is as follows:

[0076] Collect the operating temperature of each cell in the battery pack at the current moment. Assuming there are n cells in the battery pack, the temperature data of the cell is ;

[0077] Calculate the average value of all monomer temperatures according to the following formula:

[0078] ;

[0079] in, is the average temperature of all monomers, For the The temperature of each monomer, is the index of the cell in the battery pack, The value range is , is the number of cells in the battery pack;

[0080] Calculate the standard deviation of all monomer temperatures from the average temperature according to the following formula:

[0081] ;

[0082] in, is the standard deviation of all monomer temperatures from the average temperature;

[0083] The thermal equilibrium coefficient is calculated based on the standard deviation according to the following formula:

[0084] ;

[0085] in, is the thermal balance coefficient of the battery pack at the current moment.

[0086] On the basis of the above embodiment, the temperature and humidity of the energy storage power station at the current moment are analyzed and processed to obtain the environmental impact coefficient of the energy storage power station at the current moment. The specific process is as follows:

[0087] ;

[0088] in, is the environmental impact coefficient of the energy storage power station at the current moment, is the temperature of the energy storage power station at the current moment, is the humidity of the energy storage power station at the current moment, is the weight coefficient of the temperature of the energy storage power station at the current moment, is the weight coefficient of the humidity of the energy storage power station at the current moment, For the suitable temperature of the energy storage power station, For the suitable humidity of the energy storage power station, is a constant term.

[0089] Environmental impact coefficient of energy storage power station at the current moment The smaller it is, the better the battery performance of the energy storage power station at the current moment.

[0090] First, temperature and humidity The impact on energy storage power station batteries is usually not linear. In certain specific intervals, changes in environmental conditions have a significant impact on battery performance.

[0091] At extremely low or high temperatures, the chemical reaction rate of the battery may drop significantly, which can cause a dramatic change in battery performance;

[0092] humidity Too low or too high will affect the internal chemical process of the battery, resulting in a decrease in the battery's discharge capacity.

[0093] Second, at a suitable temperature (25°C) and suitable humidity (40%), the battery performance is optimal and the environmental impact coefficient is minimal.

[0094] temperature When the temperature is below 25°C or the humidity is below 40%, the battery capacity and output power decrease;

[0095] temperature When the temperature is higher than 25°C or the humidity is higher than 40%, the battery performance will decline or be damaged.

[0096] Therefore, the above function can better reflect the performance changes of the energy storage power station under different environmental conditions, can capture nonlinear effects, and can clearly characterize the temperature of the energy storage power station at the current moment. ,humidity and environmental impact factor The functional relationship between them.

[0097] Since temperature changes directly affect the chemical reaction rate inside the battery, temperature When the temperature is too low, the battery reaction rate slows down, resulting in a decrease in battery capacity. Too high may cause overheating and increase the risk of battery damage. These effects are usually more serious than humidity. More significant.

[0098] Many batteries have significant performance changes within a specific temperature range. For example, battery performance may drop sharply in environments below 0°C and above 25°C. Therefore, temperature The impact is often more prominent.

[0099] Although humidity It also affects battery performance, but its effect is usually smaller than that of temperature. For example, moderate humidity Helps keep the battery functioning properly, but under certain humidity changes, the battery's performance may not change as much as the corresponding temperature The changes are significant.

[0100] In many practical applications, experimental data and historical operating experience have shown that temperature changes have a greater impact on battery performance than humidity changes. Therefore, in the model, this is usually reflected by setting Greater than .

[0101] In battery management systems, temperature is usually the first priority. changes, as it can directly affect the safety and life of the battery.

[0102] Therefore, the weight coefficient of the temperature of the energy storage power station at the current moment is set to be greater than the weight coefficient of the humidity of the energy storage power station at the current moment, that is, , if there are no other factors affecting it, set .

[0103] On the basis of the above embodiment, the health coefficient and thermal balance coefficient of the battery pack at the current moment, as well as the environmental impact coefficient of the energy storage power station, are analyzed and processed to obtain the comprehensive health coefficient of the current energy storage power station. The specific process is as follows:

[0104] ;

[0105] in, is the comprehensive health coefficient of the current energy storage power station, is the health factor of the battery pack at the current moment, is the weight coefficient of the health factor of the battery pack at the current moment, is the weight coefficient of the thermal balance coefficient of the battery pack at the current moment, is the weight coefficient of the environmental impact coefficient of the energy storage power station.

[0106] As the health coefficient and thermal balance coefficient of the battery pack increase, the comprehensive health coefficient of the energy storage power station also increases. As the environmental impact coefficient of the energy storage power station increases, the comprehensive health coefficient of the energy storage power station decreases. Therefore, the health coefficient and thermal balance coefficient of the battery pack and the comprehensive health coefficient of the energy storage power station are positively correlated, and the environmental impact coefficient of the energy storage power station and the comprehensive health coefficient of the energy storage power station are negatively correlated. Therefore, using the above formula, the functional relationship between the health coefficient and thermal balance coefficient of the battery pack at the current moment, the environmental impact coefficient of the energy storage power station and the comprehensive health coefficient of the current energy storage power station can be expressed.

[0107] Comprehensive health index The value range is from 0 to 1.

[0108] when Close to 0, it means the battery pack is unhealthy, possibly due to battery aging, uneven temperature and adverse environmental conditions;

[0109] when Close to 1, the battery pack is healthy, showing good performance, with balanced temperatures and suitable environmental conditions.

[0110] Since the health factor of a battery pack directly reflects the status of the battery, such as remaining life, number of charge and discharge cycles, etc., the health of the battery is crucial to its performance and safety. It is usually set to the largest weight because the health factor is the most important indicator for evaluating whether a battery pack can operate safely and efficiently.

[0111] The thermal balance coefficient of the battery pack reflects the temperature uniformity between each battery cell. Excessive temperature difference will cause some batteries to age prematurely or overheat, thus affecting the performance and safety of the entire battery pack.

[0112] While thermal equilibrium is also important, its impact usually comes after battery health. The weight is generally lower than But higher than .

[0113] The environmental impact factor reflects the impact of current environmental conditions (such as temperature and humidity) on the performance of the battery pack. These factors affect the battery's charging efficiency, discharge capacity and overall life.

[0114] Although environmental factors are important, the weight of environmental impact is usually set to the lowest after considering the health and thermal balance of the battery. This is because even if the environmental conditions are not ideal, as long as the battery pack is in good health and the temperature is balanced, the battery performance can usually still be maintained within an acceptable range.

[0115] Therefore, the weight coefficient of the health factor of the battery pack is set to be greater than the weight coefficient of the thermal balance coefficient, and the weight coefficient of the thermal balance coefficient is greater than the weight coefficient of the environmental impact coefficient, that is, , if there are no other factors affecting it, set .

[0116] On the basis of the above embodiment, the comprehensive health coefficient, electricity price and load demand of the energy storage power station at the current moment are analyzed and processed to obtain the profit index of the energy storage power station at the current moment, and the profit of the energy storage power station is evaluated according to the profit index, according to the following formula:

[0117] ;

[0118] in, is the profit index of the energy storage power station at the current moment, is the current electricity price, is the load demand at the current moment, is the index of comprehensive health coefficient, .

[0119] : Indicates the comprehensive health coefficient The impact is linear. In this case, the benefit is proportional to the comprehensive health coefficient, that is, for every unit increase in the comprehensive health coefficient, the benefit index also increases by one unit.

[0120] or : Indicates nonlinear influence, which means that the impact of the comprehensive health coefficient on income is accelerated. At this time, with the improvement of health status, the increase in income will be more significant, and the increase in income will be greater than the increase brought by the linear relationship.

[0121] Electricity Prices The increase will directly increase the profit index . That is, the electricity price The higher it is, the higher the profit per unit of electricity, which directly increases the profit.

[0122] Load demand The increase in load will also improve the profitability index, because higher load demand means more power needs to be provided, thereby increasing revenue.

[0123] Comprehensive health index The increase in battery life will exponentially amplify the benefits, meaning that the better the battery health, the more significant its contribution to benefits.

[0124] The value range of is [0,10). Different values ​​within this range can reflect the economic performance of the energy storage power station:

[0125] when Close to 0: Indicates that the revenue of the energy storage power station is close to zero, due to low electricity prices, low load demand or poor battery health.

[0126] when Close to 10: This means that the benefits of the energy storage power station are very high, which means that there are high electricity prices, high load demand and good battery health at the same time.

[0127] Based on the monitored data (such as electricity price, load, and health status), the management system can optimize the operation and scheduling strategy of the energy storage power station (such as when to charge and when to discharge) to maximize profits.

[0128] In the formula , , , , The specific value of is generally determined by those skilled in the art according to actual conditions. The essence of this formula is a comprehensive analysis of weighted summation. The technicians in this field collect multiple groups of sample data, set corresponding weight coefficients for each group of sample data, substitute the set weight coefficients and the collected sample data into the formula, observe the accuracy of the model output and the rationality of the results through repeated experiments and parameter adjustments, gradually adjust these weight coefficients, and compare the performance and effect of the model under different parameter settings to find the optimal coefficient combination, screen the calculated weight coefficients and take the average, and obtain , , , , The value of .

[0129] In addition, the size of the weight coefficient is a specific value obtained by quantifying each parameter. In order to facilitate subsequent comparison, the size of the weight coefficient depends on the amount of sample data and the technical personnel in this field preliminarily set the corresponding weight coefficient for each set of sample data. It is not unique as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0130] See also Figure 2 , the present invention also provides a technical solution:

[0131] A large-scale energy storage power station operation monitoring and management method, the specific steps include:

[0132] S1. Collect characteristic parameters of the energy storage power station in the time period t before the current moment, and the external environmental parameters at the current moment, wherein the characteristic parameters of the energy storage power station include the battery pack state and the operating temperature of each monomer of the battery pack, the battery pack state includes the actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles, and the external environmental parameters include the temperature of the energy storage power station, the humidity of the energy storage power station, the electricity price and the load demand;

[0133] S2. Take the actual available capacity, voltage in a static state, output power and number of charge and discharge cycles of the battery pack in the previous t time period as input, and the health factor of the battery pack as a label. The health factor of the battery pack is evaluated by an expert group, train the health status model of the battery pack, obtain the trained model, input the actual available capacity, voltage in a static state and number of charge and discharge cycles of the battery pack at the current moment into the health status model of the battery pack, and obtain the health factor of the battery pack at the current moment;

[0134] S3. Process the operating temperature of each cell of the battery pack collected at the current moment to obtain the average temperature of all cells in the battery pack, calculate the standard deviation between the cell temperature and the average temperature based on the average temperature of all cells, and obtain the thermal balance coefficient of the battery pack at the current moment based on the standard deviation;

[0135] S4. Analyze and process the temperature and humidity of the energy storage power station at the current moment to obtain the environmental impact coefficient of the energy storage power station at the current moment, analyze and process the health coefficient and thermal balance coefficient of the battery pack at the current moment, and the environmental impact coefficient of the energy storage power station to obtain the comprehensive health coefficient of the current energy storage power station;

[0136] S5. Perform data analysis and processing on the comprehensive health coefficient, electricity price, and load demand of the energy storage power station at the current moment to obtain the profit index of the energy storage power station at the current moment, and evaluate the profit situation of the energy storage power station based on the profit index.

[0137] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0138] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A large-scale energy storage power station operation monitoring and management system, characterized in that: include: A data acquisition module is used to collect characteristic parameters of the energy storage power station in the time period t before the current moment, and external environmental parameters at the current moment. The characteristic parameters of the energy storage power station include the battery pack state and the operating temperature of each monomer of the battery pack. The battery pack state includes the actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles. The external environmental parameters include the temperature of the energy storage power station, the humidity of the energy storage power station, the electricity price and the load demand; A model building module is used to take the actual available capacity of the battery pack in the previous t time period, the voltage in the static state, the output power and the number of charge and discharge cycles as input, and the health factor of the battery pack as a label. The health factor of the battery pack is evaluated by an expert group, train the health state model of the battery pack, obtain the trained model, input the actual available capacity of the battery pack at the current moment, the voltage in the static state and the number of charge and discharge cycles into the health state model of the battery pack, and obtain the health factor of the battery pack at the current moment; A data processing module is used to process the operating temperature of each cell of the battery pack collected at the current moment, obtain the average temperature of all cells in the battery pack, calculate the standard deviation between the cell temperature and the average temperature based on the average temperature of all cells, and obtain the thermal balance coefficient of the battery pack at the current moment based on the standard deviation; The coefficient correction module is used to analyze and process the temperature and humidity of the energy storage power station at the current moment, obtain the environmental impact coefficient of the energy storage power station at the current moment, analyze and process the health coefficient and thermal balance coefficient of the battery pack at the current moment, and the environmental impact coefficient of the energy storage power station, and obtain the comprehensive health coefficient of the current energy storage power station; The profit evaluation module is used to analyze and process the comprehensive health coefficient, electricity price, and load demand of the energy storage power station at the current moment, obtain the profit index of the energy storage power station at the current moment, and evaluate the profit of the energy storage power station based on the profit index; The health coefficient and thermal balance coefficient of the battery pack at the current moment, as well as the environmental impact coefficient of the energy storage power station, are analyzed and processed to obtain the comprehensive health coefficient of the current energy storage power station. The specific process is as follows: in, is the comprehensive health coefficient of the current energy storage power station, is the health factor of the battery pack at the current moment, is the weight coefficient of the health factor of the battery pack at the current moment, is the weight coefficient of the thermal balance coefficient of the battery pack at the current moment, is the weight coefficient of the environmental impact coefficient of the energy storage power station, , ; Comprehensive health index The value range of is between 0 and 1; when Close to 0, it means the battery pack is unhealthy due to battery aging, uneven temperature and adverse environmental conditions; when Close to 1, the battery pack is healthy, showing good performance, with balanced temperature and suitable environmental conditions; The comprehensive health coefficient of the energy storage power station at the current moment, the electricity price, and the load demand are analyzed and processed to obtain the profit index of the energy storage power station at the current moment. Based on the profit index, the profit of the energy storage power station is evaluated according to the following formula: in, is the profit index of the energy storage power station at the current moment, is the current electricity price, is the load demand at the current moment, is the index of comprehensive health coefficient, ; The value range of is [0,10), and different values ​​within this range reflect the economic performance of the energy storage power station: when Close to 0: Indicates that the revenue of the energy storage power station is close to zero, due to low electricity prices, low load demand or poor battery health; when Close to 10: This means that the benefits of the energy storage power station are very high, which means that there are high electricity prices, high load demand and good battery health at the same time.

2. The large-scale energy storage power station operation monitoring and management system according to claim 1 is characterized in that: The health status model of the battery pack adopts a convolutional neural network model, which is composed of a deep neural network based on a multilayer perceptron. The deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer. The first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function.

3. The large-scale energy storage power station operation monitoring and management system according to claim 2 is characterized in that: The data of the operating temperature of each cell of the battery pack collected at the current moment is processed to obtain the average temperature of all cells in the battery pack. According to the average temperature of all cells, the standard deviation between the cell temperature and the average temperature is calculated. According to the standard deviation, the thermal balance coefficient of the battery pack at the current moment is obtained. The specific process is as follows: Collect the operating temperature of each cell in the battery pack at the current moment. Assuming there are n cells in the battery pack, the temperature data of the cell is ; Calculate the average value of all monomer temperatures according to the following formula: in, is the average temperature of all monomers, For the The temperature of each monomer, is the index of the cell in the battery pack, The value range is , is the number of cells in the battery pack; Calculate the standard deviation of all monomer temperatures from the average temperature according to the following formula: in, is the standard deviation of all monomer temperatures from the average temperature; The thermal equilibrium coefficient is calculated based on the standard deviation according to the following formula: in, is the thermal balance coefficient of the battery pack at the current moment.

4. The large-scale energy storage power station operation monitoring and management system according to claim 3 is characterized by: The temperature and humidity of the energy storage power station at the current moment are analyzed and processed to obtain the environmental impact coefficient of the energy storage power station at the current moment. The specific process is as follows: in, is the environmental impact coefficient of the energy storage power station at the current moment, is the temperature of the energy storage power station at the current moment, is the humidity of the energy storage power station at the current moment, is the weight coefficient of the temperature of the energy storage power station at the current moment, is the weight coefficient of the humidity of the energy storage power station at the current moment, For the suitable temperature of the energy storage power station, For the suitable humidity of the energy storage power station, is a constant term, ,set up .

5. A large-scale energy storage power station operation monitoring and management method, the system is used to execute a large-scale energy storage power station operation monitoring and management system according to any one of claims 1 to 4, characterized in that: The specific steps include: S1. Collect characteristic parameters of the energy storage power station in the time period t before the current moment, and the external environmental parameters at the current moment, wherein the characteristic parameters of the energy storage power station include the battery pack state and the operating temperature of each monomer of the battery pack, the battery pack state includes the actual available capacity of the battery pack, the voltage in the static state, the output power and the number of charge and discharge cycles, and the external environmental parameters include the temperature of the energy storage power station, the humidity of the energy storage power station, the electricity price and the load demand; S2. Take the actual available capacity, voltage in a static state, output power and number of charge and discharge cycles of the battery pack in the previous t time period as input, and the health factor of the battery pack as a label. The health factor of the battery pack is evaluated by an expert group, train the health status model of the battery pack, obtain the trained model, input the actual available capacity, voltage in a static state and number of charge and discharge cycles of the battery pack at the current moment into the health status model of the battery pack, and obtain the health factor of the battery pack at the current moment; S3. Process the data of the operating temperature of each cell of the battery pack collected at the current moment to obtain the average temperature of all cells in the battery pack, calculate the standard deviation between the cell temperature and the average temperature based on the average temperature of all cells, and obtain the thermal balance coefficient of the battery pack at the current moment based on the standard deviation; S4. Analyze and process the temperature and humidity of the energy storage power station at the current moment to obtain the environmental impact coefficient of the energy storage power station at the current moment, analyze and process the health coefficient and thermal balance coefficient of the battery pack at the current moment, and the environmental impact coefficient of the energy storage power station to obtain the comprehensive health coefficient of the current energy storage power station; S5. Perform data analysis and processing on the comprehensive health coefficient, electricity price, and load demand of the energy storage power station at the current moment to obtain the profit index of the energy storage power station at the current moment, and evaluate the profit situation of the energy storage power station based on the profit index.

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