Energy storage power station operation environment influence monitoring method, system, equipment and medium

Abstract

The invention discloses an energy storage power station operation environment influence monitoring method, system, device and medium, and the method comprises the steps: firstly determining an environment influence factor type corresponding to an operation stage and an energy storage type according to the operation stage and the energy storage type of a target energy storage power station, and then obtaining prediction feature data corresponding to the environment influence factor type from a database, and finally, fusing prediction results to obtain the influence degree of the operation environment in the current operation stage. Aiming at the characteristics of different energy storage technologies, the process flow and pollution production links of the energy storage power station in the construction period, the operation period and the decommissioning period are studied, the environmental influence factors in each stage are analyzed and identified, and the characteristic data related to the environmental influence are integrated; the first prediction feature data and the second prediction feature data are systematically processed and analyzed, prediction results of different models are fused, influences of various environmental factors are comprehensively considered, and the accuracy and reliability of prediction of the environmental influence degree are improved.

Description

Technical Field

[0001] The present invention relates to a means for monitoring the operating environment of a power station, belongs to the field of environmental monitoring, and in particular to a method, system, equipment and medium for monitoring the impact of the operating environment of an energy storage power station. Background Art

[0002] As a key technology to promote the transformation of energy structure, energy storage technology can provide large-scale access to renewable energy and improve the efficiency of power systems and regional energy systems, with good safety and economy. However, few people have conducted relevant research on the impact and protection of different energy storage technologies on the environment. In relevant literature, energy storage power station construction projects have not yet been included in environmental impact assessment management. In recent years, environmental complaints or disputes regarding dust, noise, electromagnetics, fire, explosion and other problems that may arise during the construction and operation of energy storage power station projects have shown an increasing trend year by year, and the contradiction between the development of energy storage technology and the environment has become increasingly prominent.

[0003] There are great differences in the technical routes of different energy storage systems, which leads to significant differences in the impact of different types of energy storage power stations on the environment. Energy storage power station construction projects usually go through three stages: construction, operation, and retirement. At different stages, different types of energy storage power stations will also have different impacts on the environment at different stages. For example, the impact of pumped storage power stations on the environment is due to complex process flows and many pollution-producing links. Different types of environmental impact factors also have certain differences in their impact on the environment, which makes the factors affecting the environment complex and difficult to achieve more accurate monitoring. Therefore, there is an urgent need for a more accurate means to solve the above problems in the existing technology. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art and to provide a method, system, device and medium for monitoring the impact of the operating environment of an energy storage power station with higher accuracy.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for monitoring the impact of energy storage power station operation environment, comprising:

[0006] S1. According to the current operation stage of the target energy storage power station, determine from a first mapping relationship a first type of environmental impact factor corresponding to the current operation stage; according to the energy storage type of the target energy storage power station, determine from a second mapping relationship a second type of environmental impact factor corresponding to the energy storage type;

[0007] The first mapping relationship is constructed according to different operating stages of the energy storage power station and different types of environmental impact factors corresponding to each operating stage; the operating stage includes the construction period, the operating period and the decommissioning period;

[0008] The second mapping relationship is constructed according to different energy storage types and different types of environmental impact factors corresponding to each energy storage type; the energy storage types include pumped storage power stations, cold and heat storage power stations, battery energy storage power stations, compressed air energy storage power stations and flywheel energy storage power stations;

[0009] The environmental impact factor refers to the type of environmental impact that affects the current environment;

[0010] S2. Acquire first prediction feature data corresponding to the first type from a preset monitoring database; acquire second prediction feature data corresponding to the second type from a preset monitoring database;

[0011] The preset monitoring database refers to a database used to store various parameters in different time periods of the current energy storage power station;

[0012] S3, inputting the first prediction feature data into a first prediction model to obtain a first prediction result output by the first prediction model; inputting the second prediction feature data into a second prediction model to obtain a second prediction result output by the second prediction model;

[0013] S4. Integrate the first prediction result and the second prediction result to obtain the impact degree of the operating environment of the target energy storage power station in the current operating stage.

[0014] In step S1, determining the first type of environmental impact factor corresponding to the current operation stage from the first mapping relationship specifically includes:

[0015] In the case where the operation stage is the construction period, determining from the first mapping relationship that the first type of environmental impact factors is construction noise, construction wastewater, construction dust, and construction waste residue;

[0016] In the case where the operation stage is the operation period, determining from the first mapping relationship that the first type of environmental impact factors is electromagnetic radiation, operation noise, operation wastewater, and operation solid waste;

[0017] When the operation stage is the decommissioning period, the first type of environmental impact factors determined from the first mapping relationship is decommissioning solid waste, demolition noise, and demolition dust.

[0018] The step S1, before determining the second type of environmental impact factor corresponding to the energy storage type from the second mapping relationship, further includes:

[0019] For any energy storage type, obtaining environmental data of each environmental impact factor in the energy storage power station corresponding to the energy storage type in different time periods from the preset monitoring database;

[0020] All environmental data in each period are processed by averaging to obtain the mean value of environmental data corresponding to each period;

[0021] Determining a second type of environmental impact factor corresponding to the energy storage type according to the degree of influence of the average value of each environmental data corresponding to all time periods on the current environment;

[0022] All energy storage types are traversed to obtain the second type of environmental impact factor corresponding to each energy storage type, so as to construct the second mapping relationship.

[0023] The determining of the second type of environmental impact factor corresponding to the energy storage type according to the influence degree of each environmental data mean value corresponding to all time periods on the current environment specifically includes:

[0024] Construct a data set based on the mean value of each environmental data corresponding to all time periods;

[0025] Confirm the second type by following any one or any combination of the following steps:

[0026] The first method is to process the data set according to a preset principal component analysis algorithm to obtain a first candidate environmental impact factor, and determine the first candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type;

[0027] The second method is to process the data set according to sensitivity analysis to obtain a second candidate environmental impact factor, and determine the second candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type;

[0028] The third type: determining a third candidate environmental impact factor according to the intersection of the first candidate environmental impact factor and the second candidate environmental impact factor, and determining the third candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type.

[0029] In step S2, the method for obtaining the first prediction feature data and the second prediction feature data specifically includes:

[0030] According to the first type and the second type, obtaining the original data of each environmental impact factor at different times from a preset monitoring database;

[0031] For each environmental impact factor, normalize all original data to obtain all normalized data corresponding to the environmental impact factor;

[0032] Processing all normalized data according to a preset box counting method to obtain fractal dimension prediction features corresponding to the environmental impact factors;

[0033] All environmental influencing factors are traversed, and all fractal dimension prediction features are determined as the first prediction feature data and the second prediction feature data.

[0034] In step S3, the method for obtaining the first prediction model specifically includes:

[0035] For any operation stage, the initial model is trained according to the sample data of all the first prediction feature data corresponding to the operation stage and the sample data of the operating environment impact degree corresponding to each first prediction feature data to obtain the prediction model corresponding to the operation stage;

[0036] Traversing all operation stages, obtaining a construction period prediction model, an operation period prediction model, and a retirement period prediction model, and determining a corresponding first prediction model according to the current operation stage;

[0037] In step S3, the method for obtaining the second prediction model specifically includes:

[0038] For any energy storage type, the initial model is trained according to the sample data of all second prediction feature data corresponding to the energy storage type and the sample data of the operating environment impact degree corresponding to each second prediction feature data to obtain a prediction model corresponding to the energy storage type;

[0039] Traverse all energy storage types to obtain pumped storage power station prediction models, cold and heat storage power station prediction models, battery energy storage power station prediction models, compressed air energy storage power station prediction models and flywheel energy storage power station prediction models, and determine the corresponding second prediction model according to the energy storage type of the target energy storage power station.

[0040] In step S4, the expression of the impact degree of the operating environment is as follows:

[0041]

[0042] Where: E is the impact of the operating environment, n 1 is the number of types of the first prediction feature data, n 2 is the number of types of the second prediction feature data, P 1 is the first prediction result, P 2 is the second prediction result.

[0043] A system for monitoring the impact of energy storage power station operation environment, which is applied to the above method, and includes:

[0044] A determination unit, configured to determine, according to a current operation stage of a target energy storage power station, a first type of environmental impact factor corresponding to the current operation stage from a first mapping relationship; and, according to an energy storage type of the target energy storage power station, determine, from a second mapping relationship, a second type of environmental impact factor corresponding to the energy storage type;

[0045] The first mapping relationship is constructed according to different operating stages of the energy storage power station and different types of environmental impact factors corresponding to each operating stage; the operating stage includes the construction period, the operating period and the decommissioning period;

[0046] The second mapping relationship is constructed according to different energy storage types and different types of environmental impact factors corresponding to each energy storage type; the energy storage types include pumped storage power stations, cold and heat storage power stations, battery energy storage power stations, compressed air energy storage power stations and flywheel energy storage power stations;

[0047] The environmental impact factor refers to the type of environmental impact that affects the current environment;

[0048] an acquisition unit, configured to acquire first prediction feature data corresponding to the first type from a preset monitoring database; and acquire second prediction feature data corresponding to the second type from a preset monitoring database;

[0049] The preset monitoring database refers to a database used to store various parameters in different time periods of the current energy storage power station;

[0050] An input unit, used for inputting the first prediction feature data into a first prediction model to obtain a first prediction result output by the first prediction model; and inputting the second prediction feature data into a second prediction model to obtain a second prediction result output by the second prediction model;

[0051] A fusion unit is used to fuse the first prediction result and the second prediction result to obtain the degree of impact of the operating environment of the target energy storage power station in the current operating stage.

[0052] A monitoring device for the impact of energy storage power station operation environment, comprising:

[0053] The device comprises a processor and a memory;

[0054] The memory is used to store computer program code and transmit the computer program code to the processor;

[0055] The processor is used to execute the above-mentioned method for monitoring the impact of the operating environment of the energy storage power station according to the instructions in the computer program code.

[0056] A storage medium for monitoring the impact of an energy storage power station on the operating environment stores a computer program, which implements the above-mentioned method for monitoring the impact of an energy storage power station on the operating environment when executed by a processor.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] In a method, system, device and medium for monitoring the operating environmental impact of an energy storage power station of the present invention, the method first determines the type of environmental impact factor corresponding to the operating stage and the energy storage type according to the operating stage and energy storage type of the target energy storage power station, then obtains the prediction feature data corresponding to the environmental impact factor type from a database, and inputs it into a prediction model for prediction, and finally integrates the prediction results to obtain the degree of operating environmental impact in the current operating stage; in the application of this design, based on the characteristics of different energy storage technologies, studies the process flow and pollution-generating links of the energy storage power station during the construction period, operation period and decommissioning period, analyzes and identifies the environmental impact factors in each stage, and integrates the feature data related to the environmental impact, systematically processes and analyzes the first prediction feature data and the second prediction feature data, and integrates the prediction results of different models, comprehensively considers the influence of various environmental factors, and improves the accuracy and reliability of the prediction of the environmental impact degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the method steps of the present invention.

[0060] Figure 2 It is a schematic diagram of the system structure of the present invention.

[0061] Figure 3 It is a schematic diagram of the device structure of the present invention.

[0062] In the figure: determination unit 1, acquisition unit 2, input unit 3, fusion unit 4, processor 5, memory 6, computer program code 61. DETAILED DESCRIPTION

[0063] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0064] Embodiment 1:

[0065] See also Figure 1 , a method for monitoring the impact of energy storage power station operation environment, comprising:

[0066] S1. According to the current operation stage of the target energy storage power station, determine from a first mapping relationship a first type of environmental impact factor corresponding to the current operation stage; according to the energy storage type of the target energy storage power station, determine from a second mapping relationship a second type of environmental impact factor corresponding to the energy storage type;

[0067] The first mapping relationship is constructed according to different operating stages of the energy storage power station and different types of environmental impact factors corresponding to each operating stage; the operating stage includes the construction period, the operating period and the decommissioning period;

[0068] The second mapping relationship is constructed according to different energy storage types and different types of environmental impact factors corresponding to each energy storage type; the energy storage types include pumped storage power stations, cold and heat storage power stations, battery energy storage power stations, compressed air energy storage power stations and flywheel energy storage power stations;

[0069] The environmental impact factor refers to the type of environmental impact that affects the current environment;

[0070] In this embodiment, the monitored object is an energy storage power station. The energy storage power station will go through three stages during operation, namely, the construction period, the operation period and the decommissioning period. For different operation stages, the environmental impact factors that need to be considered will be different. The environmental impact factors are environmental impact types that can have a certain impact on the current environment.

[0071] Optionally, during the construction period of the energy storage power station, land reclamation, building construction, equipment installation and other activities may be involved. These activities may cause environmental impacts such as soil disturbance, surface water pollution, and noise disturbance. It is necessary to pay attention to environmental management and monitoring during the construction period to ensure that environmental impacts are controlled and reduced;

[0072] During the operation period of the energy storage power station, the daily operation and maintenance of the energy storage equipment may generate environmental impacts such as noise, electromagnetic radiation, and waste heat. It is necessary to monitor and manage the environmental impacts during the operation period to ensure a balance between the continuous operation of the facility and environmental protection.

[0073] During the decommissioning period of energy storage power stations, the facilities need to be dismantled and cleaned, which may cause environmental problems such as waste disposal, soil restoration, and water source protection. Environmental impact assessment and treatment are required during the decommissioning period to ensure the compliance of the facilities and environmental protection. Therefore, the present invention can accurately analyze the degree of environmental impact of energy storage power stations at different operating stages.

[0074] Furthermore, in step S1, determining the first type of the environmental impact factor corresponding to the current operation stage from the first mapping relationship specifically includes:

[0075] In the case where the operation stage is the construction period, determining from the first mapping relationship that the first type of environmental impact factors is construction noise, construction wastewater, construction dust, and construction waste residue;

[0076] In the case where the operation stage is the operation period, determining from the first mapping relationship that the first type of environmental impact factors is electromagnetic radiation, operation noise, operation wastewater, and operation solid waste;

[0077] When the operation stage is the decommissioning period, the first type of environmental impact factors determined from the first mapping relationship is decommissioning solid waste, demolition noise, and demolition dust.

[0078] Furthermore, the step S1, before determining the second type of the environmental impact factor corresponding to the energy storage type from the second mapping relationship, further includes:

[0079] For any energy storage type, obtaining environmental data of each environmental impact factor in the energy storage power station corresponding to the energy storage type in different time periods from the preset monitoring database;

[0080] All environmental data in each period are processed by averaging to obtain the mean value of environmental data corresponding to each period;

[0081] Determining a second type of environmental impact factor corresponding to the energy storage type according to the degree of influence of the average value of each environmental data corresponding to all time periods on the current environment;

[0082] All energy storage types are traversed to obtain the second type of environmental impact factor corresponding to each energy storage type, so as to construct the second mapping relationship.

[0083] In this embodiment, the preset monitoring database is pre-constructed and is used to store various parameters in different time periods of the current energy storage power station. The various parameter data in the preset monitoring database are determined by various sensors or manual records, and then all environmental data in each time period are averaged to obtain the mean of the environmental data corresponding to each time period. All environmental data in each time period are averaged, which can reduce the interference of accidental factors on data analysis, thereby obtaining more stable and reliable representative data.

[0084] Each parameter data corresponds to a type of environmental data, and then according to the degree of influence of the average value of each environmental data corresponding to all time periods on the current environment, multiple environmental data with greater influence on the environment are determined as environmental influence factors corresponding to the energy storage type, and the parameter names of these environmental data are determined as the second type of environmental influence factors corresponding to the energy storage type; finally, all energy storage types are traversed to obtain the second type of environmental influence factors corresponding to each energy storage type, and the second mapping relationship is constructed based on this. This embodiment traverses all energy storage types to ensure that each energy storage type can obtain the second type of environmental influence factors corresponding to it, which helps to construct a complete second mapping relationship and effectively reflects the impact of different types of environmental influence factors in each energy storage type on the environment.

[0085] Furthermore, the second type of environmental impact factor corresponding to the energy storage type is determined according to the degree of influence of the average value of each environmental data corresponding to all time periods on the current environment, specifically including:

[0086] Construct a data set based on the mean value of each environmental data corresponding to all time periods;

[0087] Confirm the second type by following any one or any combination of the following steps:

[0088] The first method is to process the data set according to a preset principal component analysis algorithm to obtain a first candidate environmental impact factor, and determine the first candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type;

[0089] In the specific implementation of the first scheme, the data set is first standardized so that each variable has a mean of 0 and a variance of 1. Then, based on the standardized data set, the covariance matrix is ​​calculated. The covariance matrix can reflect the correlation between the variables. Then, the eigenvalues ​​and the corresponding eigenvectors are calculated from the covariance matrix, and the eigenvalues ​​and the corresponding eigenvectors are sorted from large to small according to the eigenvalues. The principal component with a larger eigenvalue accounts for a larger variance in the data. Then, the principal component whose cumulative contribution rate reaches a certain threshold is selected, and a principal component matrix is ​​constructed based on the selected eigenvectors. The matrix contains the selected principal components. The original data is then projected onto the selected principal components to obtain a new data set after dimensionality reduction. Then, the eigenvectors of each principal component are analyzed to see which original variables contribute the most to the principal component, thereby determining the first candidate environmental impact factor, and determining the first candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type.

[0090] The second method is to process the data set according to sensitivity analysis to obtain a second candidate environmental impact factor, and determine the second candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type;

[0091] When the second solution is implemented, the single-factor sensitivity analysis method can be used to analyze the impact of each factor change on the result one by one, or the range analysis can be used to determine the range of factor changes and observe its impact on the result; or the probability distribution can be used to simulate the impact of different factors, and a preset mathematical model or regression analysis can be used to establish the relationship between the energy storage type and the influencing factors, and the values ​​of each environmental influencing factor can be changed respectively, and then the results of these changes on the environmental impact of the energy storage type are observed, and the results are recorded, and finally the degree of influence of each factor on the result is calculated; indicators such as variance decomposition and effect size can be used to determine the second alternative environmental impact factor that contributes the most to the result among all environmental influencing factors, and the second alternative environmental impact factor is determined as the second type of environmental impact factor corresponding to the energy storage type.

[0092] The third type: determining a third candidate environmental impact factor according to the intersection of the first candidate environmental impact factor and the second candidate environmental impact factor, and determining the third candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type.

[0093] During the specific implementation of the third solution, a third candidate environmental impact factor is determined according to the intersection of the first candidate environmental impact factor and the second candidate environmental impact factor, and the third candidate environmental impact factor is determined as the second type of environmental impact factor corresponding to the energy storage type.

[0094] For example, the first candidate environmental impact factor includes environmental impact factors A, B, C, D, E, and F, and the second candidate environmental impact factor includes environmental impact factors B, C, D, E, F, and G, then the third candidate environmental impact factor is determined to be the intersection of the first and second environmental impact factors, that is, B, C, D, E, and F. This embodiment can identify factors with common characteristics among multiple environmental impact factors by taking the intersection of the results obtained by the first solution and the second solution. Such overlapping factors are often more representative and can more accurately reflect their impact on the environment, thereby making the environmental impact prediction more accurate.

[0095] S2. Acquire first prediction feature data corresponding to the first type from a preset monitoring database; acquire second prediction feature data corresponding to the second type from a preset monitoring database;

[0096] The preset monitoring database refers to a database used to store various parameters in different time periods of the current energy storage power station;

[0097] In this embodiment, the steps of obtaining the first and second prediction feature data may be performed sequentially or in parallel.

[0098] Furthermore, in step S2, the method for obtaining the first prediction feature data and the second prediction feature data specifically includes:

[0099] According to the first type and the second type, obtaining the original data of each environmental impact factor at different times from a preset monitoring database;

[0100] For each environmental impact factor, normalize all original data to obtain all normalized data corresponding to the environmental impact factor;

[0101] Processing all normalized data according to a preset box counting method to obtain fractal dimension prediction features corresponding to the environmental impact factors;

[0102] All environmental influencing factors are traversed, and all fractal dimension prediction features are determined as the first prediction feature data and the second prediction feature data.

[0103] In the application of this embodiment, according to the first type or the second type, data fields related to environmental impact factors can be extracted from a preset monitoring database and identified;

[0104] For example, for air quality monitoring around energy storage power stations, data such as nitrogen oxides and sulfur dioxide are extracted, and then for each environmental impact factor, its original data at different time points are obtained; for example, first obtain the data of each environmental impact factor at t 1 ,t 2 ,t 3 ...t n The data value at the moment is then normalized for the original data of each environmental impact factor to reduce the inconsistency between different dimensions. After normalization, the normalized data set of all environmental impact factors is obtained. The box counting method is then used to calculate the fractal dimension of each environmental impact factor. At a certain scale, the normalized data is divided into small intervals, and the number of data points contained in each box is counted. The size of the box is changed, and the above steps are repeated. The number of boxes at different scales is recorded. The fractal dimension is calculated by plotting the relationship between the number of boxes (N) log and the box scale (ε) log. The expression is as follows:

[0105]

[0106] Where: D is the fractal dimension; dlog(N) refers to differentiating the logarithm of the number N when calculating the fractal dimension; dlog(ε) refers to differentiating the logarithm of the box size ε, and then selecting a suitable linear interval and performing linear regression to obtain an estimate of the fractal dimension.

[0107] Further, for each environmental impact factor, the above fractal dimension calculation is performed to obtain the corresponding fractal dimension prediction feature, and all environmental impact factors are traversed to determine all fractal dimension prediction features as the first prediction feature data or the second prediction feature data. The first prediction feature data or the second prediction feature data can be expressed as (D A , D B , ..., D n ), where: D n is the fractal dimension prediction feature corresponding to the environmental impact factor n.

[0108] In this embodiment, the fractal dimension is usually used to describe objects with self-similarity or complex spatial distribution characteristics. In the entire life cycle of the energy storage power station, the distribution characteristics of different influencing factors may not all have obvious fractal characteristics, but the time scales corresponding to different moments will make the characteristic value changes of the environmental influencing factors show similar fluctuation patterns or trends. Predicting the degree of environmental impact by analyzing dimensional characteristics can quantify environmental complexity. Environmental pollution often shows complex variability, and the fractal dimension can quantify this variability. The application of fractal dimension in the energy storage power station system can evaluate the time scale distribution of environmental influencing factors, thereby more accurately predicting the impact of the degree of environmental impact.

[0109] S3, inputting the first prediction feature data into a first prediction model to obtain a first prediction result output by the first prediction model; inputting the second prediction feature data into a second prediction model to obtain a second prediction result output by the second prediction model;

[0110] Furthermore, in step S3, the method for obtaining the first prediction model specifically includes:

[0111] For any operation stage, the initial model is trained according to the sample data of all the first prediction feature data corresponding to the operation stage and the sample data of the operating environment impact degree corresponding to each first prediction feature data to obtain the prediction model corresponding to the operation stage;

[0112] Traversing all operation stages, obtaining a construction period prediction model, an operation period prediction model, and a retirement period prediction model, and determining a corresponding first prediction model according to the current operation stage;

[0113] When this embodiment is applied, if the first prediction feature data is a fractal dimension prediction feature, then in this embodiment, the initial model is trained according to the sample data of all fractal dimension prediction features corresponding to the operation stage, and the sample data of the degree of operating environment impact corresponding to the sample data of each fractal dimension prediction feature, to obtain a prediction model corresponding to the operation stage, and the above-mentioned model construction steps are respectively performed for the construction period, operation period and decommissioning period, so as to obtain a construction period prediction model, an operation period prediction model and a decommissioning period prediction model, and finally, the first prediction model can be determined according to the current operation stage of the target energy storage power station.

[0114] In step S3, the method for obtaining the second prediction model specifically includes:

[0115] For any energy storage type, the initial model is trained according to the sample data of all second prediction feature data corresponding to the energy storage type and the sample data of the operating environment impact degree corresponding to each second prediction feature data to obtain a prediction model corresponding to the energy storage type;

[0116] Traverse all energy storage types to obtain pumped storage power station prediction models, cold and heat storage power station prediction models, battery energy storage power station prediction models, compressed air energy storage power station prediction models and flywheel energy storage power station prediction models, and determine the corresponding second prediction model according to the energy storage type of the target energy storage power station.

[0117] When this embodiment is applied, for each energy storage type, sample data of all second prediction feature data corresponding to the energy storage type and sample data of the degree of impact of the operating environment corresponding to each second prediction feature data sample data are collected in advance. On the basis of having sufficient sample data, the initial model is trained to generate a prediction model for a specific energy storage type, and then different energy storage types are traversed to build an independent prediction model for each type. After determining the prediction models corresponding to all energy storage types, the second prediction model is determined according to the energy storage type of the target energy storage power station. The present invention not only improves the accuracy and adaptability of the prediction, but also optimizes the use of resources by personalized construction of prediction models for different types of energy storage power stations, combined with feature data and environmental influencing factors.

[0118] S4. Integrate the first prediction result and the second prediction result to obtain the impact degree of the operating environment of the target energy storage power station in the current operating stage.

[0119] Furthermore, in step S4, the fusing of the first prediction result and the second prediction result specifically includes:

[0120]

[0121] Where: E is the impact of the operating environment, n 1 is the number of types of the first prediction feature data, n 2 is the number of types of the second prediction feature data, P 1 is the first prediction result, P 2 is the second prediction result.

[0122] In this embodiment, not only the impact of the energy storage type and the operating stage on the operating environment is fully considered, but also the consideration weights of these two dimensions are taken into account, which are determined according to the number of types of the first prediction feature data and the proportion of the number of types of the second prediction feature data, thereby making the prediction result more accurate.

[0123] Optionally, other formulas may be used to obtain the degree of impact of the operating environment of the target energy storage power station in the current operating stage. The specific expression is as follows:

[0124]

[0125] Where: E is the impact of the operating environment, n 1 is the number of types of the first prediction feature data, n 2 is the number of types of the second prediction feature data, P 1 is the first prediction result, P 2 is the second prediction result, k 1 , k 2 is the adjustment factor for adjusting the weight, and its value range is between [0, 1]. 1 is the prediction uncertainty of the first prediction result, U 2 is the prediction uncertainty of the second prediction result.

[0126] The U 1 , U 2 The uncertainty is determined by a variety of factors, including the choice of model type, the uncertainty of parameter estimation in the model, the uncertainty of prediction errors caused by possible noise, missing values ​​and outliers in the data, etc. These can be calculated according to the preset formula or manually adjusted according to the actual situation.

[0127] Optionally, in step S4, after obtaining the operating environment impact degree of the target energy storage power station in the current operating stage, the method further includes:

[0128] When it is determined that the impact degree of the operating environment is greater than or equal to the preset impact degree value, a response instruction is generated, wherein the response instruction is used to instruct manual review of the target energy storage power station in the current operating stage.

[0129] Optionally, the present invention can also execute different monitoring strategies according to the degree of impact of the operating environment. For example, when it is determined that the degree of impact of the operating environment is greater than or equal to the preset impact value, the staff is instructed to go to the target energy storage power station to manually review the target energy storage power station at the current operating stage. When it is determined that the degree of impact of the operating environment is less than the preset impact value, but greater than or equal to the preset monitoring response threshold, only an alarm log is generated to record and mark the first type of raw data and the second type of raw data corresponding to the preset monitoring database corresponding to the current degree of impact of the operating environment, waiting for staff analysis. When it is determined that the degree of impact of the operating environment is less than the preset monitoring response threshold, it means that the environmental monitoring of the current target energy storage power station at the current operating stage is normal.

[0130] Embodiment 2:

[0131] See also Figure 2 , a system for monitoring the impact of energy storage power station operation environment, the system comprising:

[0132] Determining unit 1, used to determine, according to the current operation stage of the target energy storage power station, a first type of environmental impact factor corresponding to the current operation stage from a first mapping relationship; according to the energy storage type of the target energy storage power station, determine, from a second mapping relationship, a second type of environmental impact factor corresponding to the energy storage type;

[0133] The first mapping relationship is constructed according to different operating stages of the energy storage power station and different types of environmental impact factors corresponding to each operating stage; the operating stage includes the construction period, the operating period and the decommissioning period;

[0134] The second mapping relationship is constructed according to different energy storage types and different types of environmental impact factors corresponding to each energy storage type; the energy storage types include pumped storage power stations, cold and heat storage power stations, battery energy storage power stations, compressed air energy storage power stations and flywheel energy storage power stations;

[0135] The environmental impact factor refers to the type of environmental impact that affects the current environment;

[0136] Further, the determining unit 1 determines the first type according to the following steps:

[0137] In the case where the operation stage is the construction period, determining from the first mapping relationship that the first type of environmental impact factors is construction noise, construction wastewater, construction dust, and construction waste residue;

[0138] In the case where the operation stage is the operation period, determining from the first mapping relationship that the first type of environmental impact factors is electromagnetic radiation, operation noise, operation wastewater, and operation solid waste;

[0139] When the operation stage is the decommissioning period, the first type of environmental impact factors determined from the first mapping relationship is decommissioning solid waste, demolition noise, and demolition dust.

[0140] Further, the determining unit 1 determines the second mapping relationship and the second type according to the following steps:

[0141] For any energy storage type, obtaining environmental data of each environmental impact factor in the energy storage power station corresponding to the energy storage type in different time periods from the preset monitoring database;

[0142] All environmental data in each period are processed by averaging to obtain the mean value of environmental data corresponding to each period;

[0143] Determining a second type of environmental impact factor corresponding to the energy storage type according to the degree of influence of the average value of each environmental data corresponding to all time periods on the current environment;

[0144] All energy storage types are traversed to obtain the second type of environmental impact factor corresponding to each energy storage type, so as to construct the second mapping relationship.

[0145] The determining of the second type of environmental impact factor corresponding to the energy storage type according to the influence degree of each environmental data mean value corresponding to all time periods on the current environment specifically includes:

[0146] Construct a data set based on the mean value of each environmental data corresponding to all time periods;

[0147] Confirm the second type by following any one or any combination of the following steps:

[0148] The first method is to process the data set according to a preset principal component analysis algorithm to obtain a first candidate environmental impact factor, and determine the first candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type;

[0149] The second method is to process the data set according to sensitivity analysis to obtain a second candidate environmental impact factor, and determine the second candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type;

[0150] The third type: determining a third candidate environmental impact factor according to the intersection of the first candidate environmental impact factor and the second candidate environmental impact factor, and determining the third candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type.

[0151] Acquisition unit 2, used to acquire first prediction feature data corresponding to the first type from a preset monitoring database; acquire second prediction feature data corresponding to the second type from a preset monitoring database;

[0152] The preset monitoring database refers to a database used to store various parameters in different time periods of the current energy storage power station;

[0153] Furthermore, the acquisition unit 2 acquires the first and second prediction feature data according to the following steps:

[0154] According to the first type and the second type, obtaining the original data of each environmental impact factor at different times from a preset monitoring database;

[0155] For each environmental impact factor, normalize all original data to obtain all normalized data corresponding to the environmental impact factor;

[0156] Processing all normalized data according to a preset box counting method to obtain fractal dimension prediction features corresponding to the environmental impact factors;

[0157] All environmental influencing factors are traversed, and all fractal dimension prediction features are determined as the first prediction feature data and the second prediction feature data.

[0158] Input unit 3, used for inputting the first prediction feature data into the first prediction model to obtain the first prediction result output by the first prediction model; inputting the second prediction feature data into the second prediction model to obtain the second prediction result output by the second prediction model;

[0159] Furthermore, the input unit 3 obtains the first and second prediction models according to the following steps:

[0160] The method for obtaining the first prediction model specifically includes:

[0161] For any operation stage, the initial model is trained according to the sample data of all the first prediction feature data corresponding to the operation stage and the sample data of the operating environment impact degree corresponding to each first prediction feature data to obtain the prediction model corresponding to the operation stage;

[0162] Traversing all operation stages, obtaining a construction period prediction model, an operation period prediction model, and a retirement period prediction model, and determining a corresponding first prediction model according to the current operation stage;

[0163] The method for obtaining the second prediction model specifically includes:

[0164] For any energy storage type, the initial model is trained according to the sample data of all second prediction feature data corresponding to the energy storage type and the sample data of the operating environment impact degree corresponding to each second prediction feature data to obtain a prediction model corresponding to the energy storage type;

[0165] Traverse all energy storage types to obtain pumped storage power station prediction models, cold and heat storage power station prediction models, battery energy storage power station prediction models, compressed air energy storage power station prediction models and flywheel energy storage power station prediction models, and determine the corresponding second prediction model according to the energy storage type of the target energy storage power station.

[0166] The fusion unit 4 is used to fuse the first prediction result and the second prediction result to obtain the impact degree of the operating environment of the target energy storage power station in the current operating stage.

[0167] Furthermore, the fusion unit 4 calculates the degree of impact of the operating environment according to the following formula:

[0168]

[0169] Where: E is the impact of the operating environment, n 1 is the number of types of the first prediction feature data, n 2 is the number of types of the second prediction feature data, P 1 is the first prediction result, P 2 is the second prediction result.

[0170] Embodiment 3:

[0171] See also Figure 3 , a device for monitoring the impact of operating environment of an energy storage power station, the device comprising a processor 5 and a memory 6;

[0172] The memory 6 is used to store the computer program code 61 and transmit the computer program code 61 to the processor 5;

[0173] The processor 5 is used to execute the method for monitoring the impact of operating environment of an energy storage power station described in Example 1 according to the instructions in the computer program code 61 .

[0174] Embodiment 4:

[0175] A storage medium for monitoring the impact of an energy storage power station operation on the environment stores a computer program, and when the computer program is executed by a processor, the method for monitoring the impact of an energy storage power station operation on the environment described in Example 1 is implemented.

[0176] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0177] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0178] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, SMalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0179] The above-mentioned device and non-temporary computer-readable storage medium can refer to the specific description of a method for monitoring the impact of the operating environment of an energy storage power station and its beneficial effects, which will not be repeated here.

[0180] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for monitoring the impact of energy storage power station operation environment, characterized in that: include: S1. According to the current operation stage of the target energy storage power station, determine a first type of environmental impact factor corresponding to the current operation stage from a first mapping relationship; According to the energy storage type of the target energy storage power station, determining a second type of environmental impact factor corresponding to the energy storage type from a second mapping relationship; The first mapping relationship is constructed and obtained according to different operating stages of the energy storage power station and different types of environmental impact factors corresponding to each operating stage; the operating stage includes the construction period, the operating period and the decommissioning period; The second mapping relationship is constructed according to different energy storage types and different types of environmental impact factors corresponding to each energy storage type; the energy storage types include pumped storage power stations, cold and heat storage power stations, battery energy storage power stations, compressed air energy storage power stations and flywheel energy storage power stations; The environmental impact factor refers to the type of environmental impact that affects the current environment; S2. Acquire first prediction feature data corresponding to the first type from a preset monitoring database; Acquire second prediction feature data corresponding to the second type from a preset monitoring database; The preset monitoring database refers to a database used to store various parameters in different time periods of the current energy storage power station; S3, inputting the first prediction feature data into a first prediction model to obtain a first prediction result output by the first prediction model; inputting the second prediction feature data into a second prediction model to obtain a second prediction result output by the second prediction model; S4. Integrate the first prediction result and the second prediction result to obtain the impact degree of the operating environment of the target energy storage power station in the current operating stage.

2. A method for monitoring the impact of energy storage power station operation environment according to claim 1, characterized in that: In step S1, determining the first type of environmental impact factor corresponding to the current operation stage from the first mapping relationship specifically includes: In the case where the operation stage is the construction period, determining from the first mapping relationship that the first type of environmental impact factors is construction noise, construction wastewater, construction dust, and construction waste residue; In the case where the operation stage is the operation period, determining from the first mapping relationship that the first type of environmental impact factors is electromagnetic radiation, operation noise, operation wastewater, and operation solid waste; When the operation stage is the decommissioning period, the first type of environmental impact factors determined from the first mapping relationship is decommissioning solid waste, demolition noise, and demolition dust.

3. A method for monitoring the impact of energy storage power station operation environment according to claim 1, characterized in that: The step S1, before determining the second type of environmental impact factor corresponding to the energy storage type from the second mapping relationship, further includes: For any energy storage type, obtaining environmental data of each environmental impact factor in the energy storage power station corresponding to the energy storage type in different time periods from the preset monitoring database; All environmental data in each period are processed by averaging to obtain the mean value of environmental data corresponding to each period; Determining a second type of environmental impact factor corresponding to the energy storage type according to the degree of influence of the average value of each environmental data corresponding to all time periods on the current environment; All energy storage types are traversed to obtain the second type of environmental impact factor corresponding to each energy storage type, so as to construct the second mapping relationship.

4. A method for monitoring the impact of energy storage power station operation environment according to claim 3, characterized in that: The determining of the second type of environmental impact factor corresponding to the energy storage type according to the influence degree of each environmental data mean value corresponding to all time periods on the current environment specifically includes: Construct a data set based on the mean value of each environmental data corresponding to all time periods; Confirm the second type by following any one or any combination of the following steps: The first method is: processing the data set according to a preset principal component analysis algorithm to obtain a first candidate environmental impact factor, and determining the first candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type; The second method is to process the data set according to sensitivity analysis to obtain a second candidate environmental impact factor, and determine the second candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type; The third type: determining a third candidate environmental impact factor according to the intersection of the first candidate environmental impact factor and the second candidate environmental impact factor, and determining the third candidate environmental impact factor as the second type of environmental impact factor corresponding to the energy storage type.

5. The method for monitoring the impact of energy storage power station operation environment according to claim 1, characterized in that: In step S2, the method for obtaining the first prediction feature data and the second prediction feature data specifically includes: According to the first type and the second type, obtaining the original data of each environmental impact factor at different times from a preset monitoring database; For each environmental impact factor, normalize all original data to obtain all normalized data corresponding to the environmental impact factor; Processing all normalized data according to a preset box counting method to obtain fractal dimension prediction features corresponding to the environmental impact factors; All environmental influencing factors are traversed, and all fractal dimension prediction features are determined as the first prediction feature data and the second prediction feature data.

6. A method for monitoring the impact of energy storage power station operation environment according to claim 1, characterized in that: In step S3, the method for obtaining the first prediction model specifically includes: For any operation stage, the initial model is trained according to the sample data of all the first prediction feature data corresponding to the operation stage and the sample data of the operating environment impact degree corresponding to each first prediction feature data to obtain the prediction model corresponding to the operation stage; Traversing all operation stages, obtaining a construction period prediction model, an operation period prediction model, and a retirement period prediction model, and determining a corresponding first prediction model according to the current operation stage; In step S3, the method for obtaining the second prediction model specifically includes: For any energy storage type, the initial model is trained according to the sample data of all second prediction feature data corresponding to the energy storage type and the sample data of the operating environment impact degree corresponding to each second prediction feature data to obtain a prediction model corresponding to the energy storage type; Traverse all energy storage types to obtain pumped storage power station prediction models, cold and heat storage power station prediction models, battery energy storage power station prediction models, compressed air energy storage power station prediction models and flywheel energy storage power station prediction models, and determine the corresponding second prediction model according to the energy storage type of the target energy storage power station.

7. A method for monitoring the impact of energy storage power station operation environment according to claim 1, characterized in that: In step S4, the expression of the impact degree of the operating environment is as follows: Wherein: E is the impact degree of the operating environment, n1 is the number of types of the first prediction feature data, n2 is the number of types of the second prediction feature data, P1 is the first prediction result, and P2 is the second prediction result.

8. A monitoring system for the impact of energy storage power station operation environment, characterized in that: The system is applied to the method described in any one of claims 1 to 7, and the system comprises: A determination unit (1) is used to determine, according to a current operation stage of a target energy storage power station, a first type of environmental impact factor corresponding to the current operation stage from a first mapping relationship; and according to an energy storage type of the target energy storage power station, determine, from a second mapping relationship, a second type of environmental impact factor corresponding to the energy storage type; The first mapping relationship is constructed and obtained according to different operating stages of the energy storage power station and different types of environmental impact factors corresponding to each operating stage; the operating stage includes the construction period, the operating period and the decommissioning period; The second mapping relationship is constructed according to different energy storage types and different types of environmental impact factors corresponding to each energy storage type; the energy storage types include pumped storage power stations, cold and heat storage power stations, battery energy storage power stations, compressed air energy storage power stations and flywheel energy storage power stations; The environmental impact factor refers to the type of environmental impact that affects the current environment; An acquisition unit (2) is used to acquire first prediction feature data corresponding to the first type from a preset monitoring database; and acquire second prediction feature data corresponding to the second type from a preset monitoring database; The preset monitoring database refers to a database used to store various parameters in different time periods of the current energy storage power station; An input unit (3) is used to input the first prediction feature data into a first prediction model to obtain a first prediction result output by the first prediction model; and input the second prediction feature data into a second prediction model to obtain a second prediction result output by the second prediction model; A fusion unit (4) is used to fuse the first prediction result and the second prediction result to obtain the degree of impact of the operating environment of the target energy storage power station in the current operating stage.

9. A monitoring device for the impact of energy storage power station operation environment, characterized in that: include: The device comprises a processor (5) and a memory (6); The memory (6) is used to store computer program code (61) and transmit the computer program code (61) to the processor (5); The processor (5) is used to execute the method for monitoring the impact of operating environment of an energy storage power station according to any one of claims 1 to 7 according to the instructions in the computer program code (61).

10. A storage medium for monitoring the impact of energy storage power station operation environment, on which a computer program is stored, characterized in that: When the computer program is executed by a processor, the method for monitoring the impact of operating environment of an energy storage power station described in any one of claims 1 to 7 is implemented.

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