CN117575258B - Coal-fired power plant deep peak shaving method and device considering wastewater treatment - Google Patents
Coal-fired power plant deep peak shaving method and device considering wastewater treatment Download PDFInfo
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Abstract
The invention relates to the technical field of deep peak shaving, in particular to a method and a device for deep peak shaving of a coal-fired power plant in consideration of wastewater treatment. The method focuses on environmental protection while power peak shaving by considering the wastewater treatment condition, thereby being beneficial to slowing down the negative influence of coal-fired power generation on the local environment; not only is the adjustment of the power generation power focused, but also a plurality of factors such as the running state of the wastewater treatment equipment, the water sample detection result and the like are comprehensively considered, so that the running state and the environmental influence of the coal-fired power plant can be more comprehensively evaluated. The peak regulation decision is carried out by constructing a wastewater data classification model and a wastewater treatment evaluation model so as to improve the accuracy and adaptability of the model and enable the model to be better suitable for the characteristics of different coal-fired power plants; by analyzing the space-time correlation coefficient matrix between the waste water treatment energy efficiency evaluation index and the generated power, the correlation between the waste water treatment efficiency and the generated power can be more specifically known, and the maximum regulation and control limit of each coal-fired power plant can be more specifically determined.
Description
Technical Field
The invention relates to the technical field of deep peak shaving, in particular to a method and a device for deep peak shaving of a coal-fired power plant in consideration of wastewater treatment.
Background
The electric power peak regulation is to carry out planned cooperative adjustment on the generated power of a plurality of coal-fired power plants in the district according to the electricity consumption requirement of the electric power user side in the district, so that the requirements of stable operation and electric power supply of the electric network are met to a certain extent.
However, the existing power peak shaving method lacks consideration on environmental factors, a large amount of waste water is generated in coal-fired power generation, and if the requirements of stable operation and power supply of a power grid are pursued, the waste water treatment problem of a coal-fired power plant is ignored, so that the local environment is easily and negatively influenced.
Therefore, there is a need for a coal-fired power plant deep peak shaving method considering wastewater treatment to solve the above technical problems.
Disclosure of Invention
In order to solve the technical problems, the invention provides a coal-fired power plant deep peak shaving method taking the balance of environmental protection and power supply into consideration, which is helpful for promoting clean energy and green development and considering wastewater treatment.
In a first aspect, the present invention provides a coal-fired power plant depth peaking method considering wastewater treatment, the method comprising:
Acquiring historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation;
Constructing a power generation wastewater data classification model, and carrying out data identification on each piece of historical power generation wastewater treatment information by utilizing the power generation wastewater data classification model to obtain the running states of wastewater treatment equipment in different time windows of each coal-fired power plant, the treated wastewater sample detection results and the power generation in the corresponding time window; the operation state of the wastewater treatment equipment comprises electric power, wastewater treatment efficiency, vibration and noise; the wastewater sample detection result comprises heavy metal concentration and type, organic pollutant concentration and type, and nutrient salt concentration and type;
Inputting the running state of the wastewater treatment equipment into a pre-constructed wastewater treatment equipment evaluation model to obtain wastewater treatment equipment evaluation parameters;
inputting the wastewater sample detection result into a pre-constructed wastewater sample evaluation model to obtain wastewater sample evaluation parameters;
Carrying out weighted calculation on the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters in the same coal-fired power plant and the same time window to obtain a wastewater treatment energy efficiency evaluation index;
Performing correlation analysis on the waste water treatment energy efficiency evaluation indexes in different time windows of each coal-fired power plant and the generated power in the corresponding time windows to obtain a space-time correlation coefficient matrix between the waste water treatment efficiency and the generated power of each coal-fired power plant;
Setting an energy efficiency evaluation index threshold value of wastewater treatment based on the environmental grade of the area where the coal-fired power plant is located;
Traversing the space-time correlation coefficient matrix of each coal-fired power plant according to the wastewater treatment energy efficiency evaluation index threshold value to obtain the power generation with the maximum correlation with the wastewater treatment energy efficiency evaluation index threshold value, and taking the power generation with the maximum correlation as the maximum regulation and control limit of the coal-fired power plant participating in the power grid peak shaving.
In a second aspect, the present invention also provides a coal-fired power plant depth peaking unit considering wastewater treatment, the system comprising:
the data acquisition module is used for acquiring historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation; the historical power generation wastewater treatment information comprises the operation state of wastewater treatment equipment of each coal-fired power plant in different time windows, the detection result of the treated wastewater sample and the power generation power in the corresponding time window;
The data classification module is used for storing the power generation wastewater data classification model, carrying out data identification on each historical power generation wastewater treatment information by utilizing the power generation wastewater data classification model, and obtaining the operation state of wastewater treatment equipment and wastewater sample detection results in different time windows of each coal-fired power plant;
The operation state of the wastewater treatment equipment comprises electric power, wastewater treatment efficiency, vibration and noise; the wastewater sample detection result comprises heavy metal concentration and type, organic pollutant concentration and type, and nutrient salt concentration and type; generating power within a corresponding time window;
The wastewater treatment equipment evaluation module is used for inputting the operation state of the wastewater treatment equipment into a pre-stored wastewater treatment equipment evaluation model to obtain wastewater treatment equipment evaluation parameters;
The wastewater sample evaluation module is used for inputting the wastewater sample detection result into a pre-stored wastewater sample evaluation model to obtain wastewater sample evaluation parameters;
The wastewater treatment energy efficiency evaluation index calculation module is used for carrying out weighted calculation on the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters in the same coal-fired power plant and the same time window to obtain a wastewater treatment energy efficiency evaluation index;
the space-time correlation coefficient matrix calculation module is used for carrying out correlation analysis on the waste water treatment energy efficiency evaluation index in different time windows of each coal-fired power plant and the power generation power in the corresponding time window to obtain a space-time correlation coefficient matrix between the waste water treatment efficiency and the power generation power of each coal-fired power plant;
The environment grade threshold setting module is used for setting an energy efficiency evaluation index threshold of wastewater treatment according to the environment grade of the area where the coal-fired power plant is located;
The power generation power determining module is used for traversing the space-time correlation coefficient matrix of each coal-fired power plant according to the wastewater treatment energy efficiency evaluation index threshold value to obtain the power generation power with the maximum correlation with the wastewater treatment energy efficiency evaluation index threshold value, and taking the power generation power with the maximum correlation as the maximum regulation and control limit of the coal-fired power plant participating in the power grid peak regulation.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the processor implements the method according to any embodiment of the present invention.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method according to any of the embodiments of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
By considering the wastewater treatment condition, the invention pays attention to environmental protection while carrying out power peak shaving; this helps to mitigate the negative impact of coal-fired power generation on the local environment, particularly reducing pollution of water resources and soil by wastewater emissions; the invention not only pays attention to the adjustment of the power generation, but also comprehensively considers a plurality of factors such as the running state of the wastewater treatment equipment, the water sample detection result and the like; this helps to more fully evaluate the operating conditions and environmental impact of the coal-fired power plant;
by constructing a wastewater data classification model and a wastewater treatment evaluation model, the invention uses a data driving mode to carry out peak shaving decision; the accuracy and the adaptability of the model can be improved, so that the model is better suitable for the characteristics of different coal-fired power plants; by analyzing the space-time correlation coefficient matrix between the wastewater treatment energy efficiency evaluation index and the power generation power, the invention can more specifically know the correlation between the wastewater treatment efficiency and the power generation power; this helps to more specifically determine the maximum control credit for each coal-fired power plant;
The method has certain self-adaptability by setting the energy efficiency evaluation index threshold of the wastewater treatment according to the environmental level of the area where the coal-fired power plant is located, and can be adjusted according to the environmental standards of different areas so as to better adapt to local environmental requirements; by focusing on wastewater treatment, the present invention helps promote sustainable development of the power industry, taking into account the balance of environmental protection and power supply, helping to promote clean energy and green development.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a coal-fired power plant depth peaking method considering wastewater treatment provided by an embodiment of the present invention;
FIG. 2 is a hardware architecture diagram of an electronic device according to an embodiment of the present invention;
FIG. 3 is a block diagram of a coal-fired power plant depth peaking unit considering wastewater treatment according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a coal-fired power plant depth peaking method considering wastewater treatment, which includes:
step S1, acquiring historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation;
S2, constructing a power generation wastewater data classification model, and carrying out data identification on each piece of historical power generation wastewater treatment information by utilizing the power generation wastewater data classification model to obtain the operation state of wastewater treatment equipment in different time windows of each coal-fired power plant, the detection result of the treated wastewater sample and the power generation in the corresponding time window; the operating state of the wastewater treatment equipment comprises electric power, wastewater treatment efficiency, vibration and noise; the detection result of the wastewater sample comprises the concentration and the type of heavy metals, the concentration and the type of organic pollutants and the concentration and the type of nutrient salts;
s3, inputting the running state of the wastewater treatment equipment into a pre-constructed wastewater treatment equipment evaluation model to obtain wastewater treatment equipment evaluation parameters;
s4, inputting a wastewater sample detection result into a pre-constructed wastewater sample evaluation model to obtain wastewater sample evaluation parameters;
s5, carrying out weighted calculation on the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters of the same coal-fired power plant in the same time window to obtain a wastewater treatment energy efficiency evaluation index;
s6, carrying out correlation analysis on the waste water treatment energy efficiency evaluation indexes in different time windows of each coal-fired power plant and the generated power in the corresponding time windows to obtain a space-time correlation coefficient matrix between the waste water treatment efficiency and the generated power of each coal-fired power plant;
Step S7, setting an energy efficiency evaluation index threshold value of wastewater treatment based on the environmental grade of the area where the coal-fired power plant is located;
And S8, traversing the space-time correlation coefficient matrix of each coal-fired power plant according to the wastewater treatment energy efficiency evaluation index threshold value to obtain the power generation with the maximum correlation with the wastewater treatment energy efficiency evaluation index threshold value, and taking the power generation with the maximum correlation as the maximum regulation and control limit of the coal-fired power plant participating in the power grid peak regulation.
In the embodiment, by considering the wastewater treatment condition, the invention pays attention to environmental protection while carrying out power peak shaving; this helps to mitigate the negative impact of coal-fired power generation on the local environment, particularly reducing pollution of water resources and soil by wastewater emissions; the invention not only pays attention to the adjustment of the power generation, but also comprehensively considers a plurality of factors such as the running state of the wastewater treatment equipment, the water sample detection result and the like; this helps to more fully evaluate the operating conditions and environmental impact of the coal-fired power plant; by constructing a wastewater data classification model and a wastewater treatment evaluation model, the invention uses a data driving mode to carry out peak shaving decision; the accuracy and the adaptability of the model can be improved, so that the model is better suitable for the characteristics of different coal-fired power plants; by analyzing the space-time correlation coefficient matrix between the wastewater treatment energy efficiency evaluation index and the power generation power, the invention can more specifically know the correlation between the wastewater treatment efficiency and the power generation power; this helps to more specifically determine the maximum control credit for each coal-fired power plant; the method has certain self-adaptability by setting the energy efficiency evaluation index threshold of the wastewater treatment according to the environmental level of the area where the coal-fired power plant is located, and can be adjusted according to the environmental standards of different areas so as to better adapt to local environmental requirements; by focusing on wastewater treatment, the present invention helps promote sustainable development of the power industry, taking into account the balance of environmental protection and power supply, helping to promote clean energy and green development.
The manner in which the individual steps shown in fig. 1 are performed is described below.
For step S1:
S1, acquiring historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation is very critical; this step aims at collecting power generation wastewater treatment information about the coal-fired power plant, including the operating state of wastewater treatment equipment, the detection result of the treated wastewater sample, the power generation within the corresponding time window, and the like; the information has important significance for subsequent deep peak shaving analysis and decision making; in carrying out step S1, the historical power generation wastewater treatment information may be obtained specifically by:
S11, data acquisition: collecting historical power generation wastewater treatment information from coal-fired power plants participating in power grid regulation; the method comprises the following steps of operating state of wastewater treatment equipment, water sample detection results, power generation and the like; the collection of wastewater treatment information should cover different time periods to ensure the diversity and comprehensiveness of data;
S12, data cleaning and arrangement: cleaning the collected historical data to remove abnormal values and incomplete data; the cleaned data are arranged into a format suitable for subsequent modeling and analysis, so that the consistency and usability of the data are ensured;
S13, establishing a data file: establishing a detailed data file for each coal-fired power plant participating in power grid regulation, wherein the data file comprises information such as equipment type, wastewater treatment process, wastewater sample detection method and the like; the establishment of the data file is helpful for background understanding of subsequent model construction and evaluation;
S14, data security and privacy protection: during data acquisition and processing, protection of sensitive information must be ensured, including but not limited to, commercial confidentiality of coal-fired power plants, specific configuration of wastewater treatment equipment, and the like; encryption, anonymization and other means are adopted to ensure the privacy of the data and simultaneously provide enough information for analysis;
s15, verifying the data quality: quality verification is carried out on the tidied data, and the accuracy and the reliability of the data are ensured; verifying the rationality of the data by using statistical methods or knowledge of domain experts, such as checking whether the wastewater detection result meets environmental standards;
s16, establishing a data storage and management system: designing and establishing a reasonable data storage and management system, and ensuring the safety and traceability of data; the storage and management of data is critical to tracking and analyzing historical information throughout the peak shaving process.
In the step, when historical power generation wastewater treatment information is collected, the step emphasizes the diversity of data, namely, different time periods and coal-fired power plants are covered, so that more comprehensive and diversified data support can be provided, and the finding of rules and trends in the data support is facilitated; when the data are collected, the accuracy and the reliability of the data are emphasized, abnormal values and incomplete data are removed through cleaning and data arrangement, and the accuracy and the consistency of the data are ensured; meanwhile, through data quality verification, the rationality and accuracy of the data can be further ensured;
In the data acquisition and processing process, the step emphasizes the safety and privacy protection of the data, adopts means such as encryption, anonymization and the like, so that the data privacy can be ensured and enough information can be provided for analysis; in addition, a data storage and management system is established, so that the safety and traceability of the data are ensured; this step emphasizes the traceability of the data, and by building a data storage and management system, historical information can be tracked and managed; this provides reliable support and reference for subsequent depth peaking analysis and decisions;
in summary, the advantages of the step S1 are mainly reflected in the aspects of integrity, diversity, accuracy, safety, traceability and the like, and reliable data support is provided for subsequent deep peak shaving analysis and decision.
For step S2:
in the step S2, a power generation wastewater data classification model is designed, and the operation state of wastewater treatment equipment in different time windows of each coal-fired power plant, the detection result of the treated wastewater sample and the power generation power in the corresponding time window are obtained through data identification of historical power generation wastewater treatment information; the goal of this step is to provide the basis data for subsequent wastewater treatment efficacy evaluations; the specific steps of constructing the power generation wastewater data classification model are as follows:
S21, data collection and pretreatment: the method comprises the steps that historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation is required to be collected; the method comprises the steps of operating state of wastewater treatment equipment, wastewater sample detection result and power generation; the data collection comprises means such as field monitoring and sensor data acquisition; the collected original data needs to be preprocessed, including abnormal value removal, missing value processing and the like;
s22, feature extraction: extracting features related to wastewater treatment and generated power from the raw data; the method comprises the characteristics of the running states of wastewater treatment equipment such as electric power, wastewater treatment efficiency, vibration, noise and the like, and the characteristics of the detection results of wastewater samples such as heavy metal concentration, organic pollutant concentration, nutrient salt concentration and the like;
S23, data tagging: in order to train the supervised learning model, the data needs to be labeled; the data can be divided into different categories according to preset standards; for example, the operating state of wastewater treatment facilities may be classified as normal, abnormal; the detection result of the wastewater sample can be divided into standard reaching, exceeding and the like;
S24, model selection and training: selecting a proper machine learning or deep learning model to perform classification tasks; selectable models include decision trees, support vector machines, neural networks, etc.; model training is carried out by using marked data, and model parameters are adjusted to improve classification accuracy;
s25, model evaluation: evaluating the constructed model by using another part of historical data, and checking the generalization performance of the model; the evaluation index may include accuracy, recall, F1 score, etc.;
S26, model application: and applying the trained model to data of historical power generation wastewater treatment information, classifying the data in each time window, and obtaining the running state of wastewater treatment equipment, the wastewater sample detection result and the type information of power generation.
In the step, historical power generation wastewater treatment information of a plurality of coal-fired power plants is obtained through field monitoring and sensor data acquisition, so that the comprehensiveness and the authenticity of the data are ensured; the preprocessing step is helpful for improving the data quality, removing abnormal values and processing missing values, and ensuring the reliability of model training;
By extracting the characteristics of the original data, various aspects related to the wastewater treatment and the power generation are considered, including the operation state of the wastewater treatment equipment and various characteristics of the wastewater sample detection result; this helps the model to better understand the data and improves the performance of the classification model;
S2, different machine learning or deep learning models, such as decision trees, support vector machines and neural networks, are considered; this choice of diversity allows the selection of the most appropriate model according to the nature of the particular problem, increasing the flexibility of the model;
the trained model is applied to data of historical power generation wastewater treatment information, and category information of wastewater treatment equipment running state, wastewater sample detection result and power generation power in each time window is obtained; the actual problem is solved, and basic data is provided for evaluating the wastewater treatment efficiency;
In conclusion, the S2 step realizes the process of constructing a power generation wastewater data classification model and carrying out data identification on historical power generation wastewater treatment information through the steps of data collection, pretreatment, feature extraction, data tagging, model selection and training, model evaluation, actual application and the like; the method has the advantages that based on data, the classification accuracy can be improved and reliable application support can be provided by combining the technologies of feature engineering, supervised learning and the like.
For step S3:
s3, inputting the running state of the wastewater treatment equipment into a pre-constructed wastewater treatment equipment evaluation model to obtain wastewater treatment equipment evaluation parameters; this step aims at evaluating the operation state of the wastewater treatment plant of the coal-fired power plant to ensure that it effectively treats wastewater and reduces the impact on the environment; the method specifically comprises the following steps:
S31, selecting a proper evaluation model: selecting a proper evaluation model according to the characteristics and the evaluation requirements of the wastewater treatment equipment; a model based on data driving or a model based on a physical model can be selected; for example, the data-driven based model may learn the operating state and performance of the device from historical data using machine learning or deep learning algorithms; the model based on the physical model is to build a mathematical model according to the physical characteristics of the wastewater treatment equipment and simulate the operation process of the equipment;
s32, preparing evaluation data: preparing corresponding input data according to the selected evaluation model; for the data-driven based model, it is necessary to prepare history data related to the operation state of the wastewater treatment apparatus, such as the power consumption of the apparatus, the wastewater treatment efficiency, vibration, noise, and the like; for models based on physical models, it is necessary to provide physical parameters, operating conditions, etc. of the device;
S33, model training and parameter adjustment: training the selected evaluation model by using the prepared data, and adjusting model parameters to optimize the evaluation result; through the training process, the model learns the characteristics and rules in the historical data, and provides support for subsequent evaluation of wastewater treatment equipment;
S34, model evaluation and optimization: evaluating the trained evaluation model by using another part of the historical data, and checking the performance of the model; the evaluation index may include accuracy, recall, F1 score, etc.; according to the evaluation result, the model is adjusted and optimized to improve the generalization performance and accuracy of the model;
S35, applying an evaluation model: applying the optimized evaluation model to an actual scene, and evaluating the wastewater treatment equipment of each coal-fired power plant; inputting the operation state of the wastewater treatment equipment into an evaluation model to obtain corresponding evaluation parameters such as the efficiency, stability and the like of the equipment; these evaluation parameters will provide a reference basis for the subsequent depth peaking decisions.
In conclusion, the step S3 realizes effective evaluation of the coal-fired power plant wastewater treatment equipment by selecting a proper evaluation model, preparing evaluation data, training and adjusting model parameters, evaluating and optimizing the model, applying the evaluation model and the like. This step helps to ensure proper operation of the wastewater treatment facility, reduce environmental impact, and provide reference information regarding facility performance for deep peak shaving decisions.
For step S4:
S4, inputting a wastewater sample detection result into a pre-constructed wastewater sample evaluation model to obtain wastewater sample evaluation parameters; the method mainly aims at evaluating the concentration and the type of various pollutants in a wastewater sample to know the effect of wastewater treatment and possible environmental influence; the following is a detailed description of the construction of an evaluation model of wastewater treatment facilities:
S41, selecting a proper evaluation model: selecting a proper evaluation model according to the characteristics and evaluation requirements of the wastewater sample; a model based on data driving or a model based on a physical model can be selected; for example, a data-driven based model may use machine learning or deep learning algorithms to learn the quality characteristics and regularity of a water sample from historical water quality data; the model based on the physical model is to build a mathematical model according to the physical and chemical characteristics of the wastewater sample, and simulate the change process of the water sample;
S42, preparing evaluation data: preparing corresponding input data according to the selected evaluation model; for data-driven based models, it is necessary to prepare historical detection data related to wastewater samples, such as heavy metal concentration and type, organic pollutant concentration and type, nutrient salt concentration and type, etc.; for a model based on a physical model, the physical and chemical parameters, detection conditions and the like of a water sample are required to be provided;
S43, model training and parameter adjustment: training the selected evaluation model by using the prepared data, and adjusting model parameters to optimize the evaluation result; through the training process, the model learns the characteristics and rules in the historical data, and provides support for subsequent wastewater sample evaluation;
S44, model evaluation and optimization: evaluating the trained evaluation model by using another part of the historical data, and checking the performance of the model; the evaluation index may include accuracy, recall, F1 score, etc.; according to the evaluation result, the model is adjusted and optimized to improve the generalization performance and accuracy of the model;
S45, applying an evaluation model: the optimized evaluation model is applied to an actual scene, and the wastewater sample of each coal-fired power plant is evaluated; inputting the detection result of the wastewater sample into an evaluation model to obtain corresponding evaluation parameters, such as the quality grade, the pollutant content and the like of the water sample; these evaluation parameters will provide a reference basis for the subsequent depth peaking decisions.
In summary, the step S4 can establish an evaluation model of the wastewater treatment equipment by selecting a suitable evaluation model, preparing evaluation data, training and adjusting model parameters, evaluating and optimizing the model, applying the evaluation model and the like, wherein the model can understand and extract key information from the detection result of the wastewater sample, and provides powerful support for the subsequent evaluation of the wastewater treatment energy efficiency.
For step S5:
S5, comprehensively calculating the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters of each coal-fired power plant by a weighted calculation method to obtain a wastewater treatment energy efficiency evaluation index; the index can be used for measuring the wastewater treatment efficiency of the coal-fired power plant and further evaluating the influence of the wastewater treatment efficiency on the generated power; in step S5, the weight calculation is performed by:
S51, determining the weight of an evaluation parameter of wastewater treatment equipment and an evaluation parameter of a wastewater sample; the weight can be determined according to the running state of the equipment and the importance of the water sample detection result; for example, if the device operating status has a greater impact on wastewater treatment efficiency, a greater weight may be assigned, while the water sample detection results have a lesser impact on wastewater treatment efficiency, a lesser weight may be assigned;
s52, multiplying the determined weight by the wastewater treatment equipment evaluation parameter and the wastewater sample evaluation parameter to obtain a weighted evaluation parameter of each coal-fired power plant;
And S53, summing the weighted evaluation parameters to obtain the wastewater treatment energy efficiency evaluation index of each coal-fired power plant.
In determining the weights, the weights may be determined by the following method:
a. Expert scoring: inviting experts in the related fields to score the importance of the evaluation parameters of the wastewater treatment equipment and the evaluation parameters of the wastewater sample, and then carrying out statistics and analysis on the scoring result to obtain the weight of each parameter;
b. Historical data method: the historical data is analyzed to obtain the influence degree of the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters on the wastewater treatment efficiency, so that the weight of each parameter is determined;
c. the experimental method comprises the following steps: by the experimental test method, the influence degree of the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters on the wastewater treatment efficiency can be obtained, and then the weight of each parameter is determined.
In summary, the weighted calculation of step S5 is a comprehensive analysis, and by considering the parameters related to the wastewater treatment and their different contribution degrees in the overall effect, an index reflecting the wastewater treatment efficiency is obtained, and this index is used in the subsequent steps for performing the correlation analysis with the generated power, thereby determining the maximum regulation limit of the coal-fired power plant.
For step S6:
In step S6, performing a correlation analysis on the wastewater treatment energy efficiency evaluation index in different time windows of each coal-fired power plant and the generated power in the corresponding time window, which generally relates to a correlation analysis method in statistics; the space-time correlation coefficient matrix is a two-dimensional array matrix, wherein elements represent correlation coefficients between the wastewater treatment energy efficiency evaluation index and the power generation power; the same row represents the correlation coefficient between the same power generation power and different waste water treatment energy efficiency evaluation indexes, and the same column represents the correlation coefficient between the same waste water treatment energy efficiency evaluation index and different power generation powers; the method for carrying out correlation analysis on the wastewater treatment energy efficiency evaluation index and the generated power specifically comprises the following steps:
S61, collecting data: the method comprises the steps of firstly collecting data of waste water treatment energy efficiency evaluation indexes and power generation power in different time windows of each coal-fired power plant, wherein the data can be obtained through a historical record, a real-time monitoring system or other related data sources.
S62, data cleaning and pretreatment: and cleaning the collected data to remove abnormal values, missing values or invalid data so as to ensure the accuracy of data analysis, and preprocessing the data, for example, normalizing or normalizing the data so as to eliminate the influence of data dimension on analysis results.
S63, calculating correlation: the correlation between the wastewater treatment energy efficiency evaluation index and the generated power is measured by using indexes such as a correlation coefficient (such as a pearson correlation coefficient) or a spearman rank correlation coefficient, and the indexes can help to determine the degree and direction of linear or nonlinear relation between two variables. The following are specific calculation steps and formulas:
S631, for each coal-fired power plant, setting an energy efficiency evaluation index set of wastewater treatment as X= { X 1,x2,...,xn }, and setting a power generation set as Y= { Y 1,y2,...,yn };
S632, sorting the data in the sets X and Y, and sorting each element in the sets X and Y according to the order from small to large to obtain new sets X d＝{xd1,xd2,...,xdn and Y d＝{yd1,yd2,...,ydn;
S633, calculating a difference D (i) between each data pair (x di,ydi), where i=1, 2,..n, which represent the degree of difference between the wastewater treatment energy efficiency evaluation index and the generated power;
s634, calculating a correlation coefficient R between an energy efficiency evaluation index of wastewater treatment and the generated power, wherein the calculation formula of R is as follows:
Where Σ represents the sum of the differences D (i) for all data pairs (x di,ydi), n being the total number of data pairs;
And judging the correlation between the wastewater treatment energy efficiency evaluation index and the generated power according to the value of R, wherein the value range of R is between-1 and 1, the positive value of R represents positive correlation, the negative value of R represents negative correlation, and R=0 represents no correlation.
S64, establishing a space-time correlation matrix: based on the correlation coefficient obtained by calculation, a space-time correlation matrix is established, the correlation degree between the wastewater treatment efficiency and the power generation power in different time periods can be displayed, the correlation mode between the wastewater treatment efficiency and the power generation power can be presented by the matrix, and the specific space-time correlation matrix is as follows:
Wherein the same row represents the correlation coefficient between the same power generation power and different waste water treatment energy efficiency evaluation indexes, and the same column represents the correlation coefficient between the same waste water treatment energy efficiency evaluation index and different power generation powers; r i nm represents a correlation coefficient between the nth generated power and the mth wastewater treatment energy efficiency evaluation index in the ith coal-fired power plant.
In the step, the relation between the waste water treatment energy efficiency evaluation index and the power generation power in different time windows of each coal-fired power plant can be comprehensively evaluated; by establishing a space-time correlation coefficient matrix, the correlation between different power generation powers and waste water treatment energy efficiency evaluation indexes can be considered at the same time, and a comprehensive view angle is provided; the space-time correlation coefficient matrix provides visual explanation of the relation strength and direction; through elements in the matrix, whether the correlation between the wastewater treatment energy efficiency evaluation index and the generated power is positive correlation, negative correlation or no correlation can be known; this facilitates a deep understanding of the pattern of correlation between wastewater treatment effectiveness and generated power; according to the method, analysis is carried out based on actual data, and the relation between the waste water treatment energy efficiency evaluation index and the power generation power can be more objectively revealed through a correlation analysis method in statistics; this helps to make more accurate and reliable decisions;
in conclusion, the step S6 illustrates a process based on a data analysis and statistics method to quantify the correlation between the wastewater treatment efficiency and the generated power, and provides a scientific basis for peak shaving of the coal-fired power plant; this process requires appropriate data, statistical tools and domain expertise to ensure reliability and applicability of the analysis results.
For step S7:
s7, setting a threshold value of an energy efficiency evaluation index of wastewater treatment, and carrying out subsequent judgment and operation based on the threshold value; in the step, the key of setting the threshold value of the energy efficiency evaluation index of wastewater treatment is to understand the environmental grade of the area where the coal-fired power plant is located; the method specifically comprises the following steps:
S71, understanding the environment level: evaluating the environmental level of a specific geographic position where the coal-fired power plant is located; the environmental level relates to the factors of local ecological environment, water resource condition, soil quality and the like; can be obtained by environmental monitoring data of local environmental protection departments, related research reports and environmental assessment of local governments; knowing the evaluation criteria of the local environment level, including for example criteria on water quality, air quality, soil pollution etc.;
s72, setting an energy efficiency evaluation index threshold value of wastewater treatment: based on the understanding of the environmental grade, formulating an energy efficiency evaluation index threshold value of wastewater treatment; this threshold can be set to a relatively high value if the environmental level of the area in which it is located requires a higher environmental criterion; conversely, if the environmental level is low, the threshold may be adjusted accordingly;
S74, consider sustainable development: factors of sustainable development can also be considered when setting the threshold; this includes rational utilization of water resources, potential impact of wastewater on the local ecosystem, etc.; ensuring that the efficiency evaluation index threshold of wastewater treatment is conducive to achieving a sustainable development goal;
S75, updating periodically: the environmental level may change over time; therefore, the wastewater treatment energy efficiency evaluation index threshold needs to be updated periodically to adapt to new environmental requirements.
In the whole process, it is important to communicate and cooperate with the experts in the field of local environmental protection departments and professionals to ensure that the set threshold meets the requirements of the actual environment; in addition, clarity and openness are also critical, ensuring that the relevant stakeholders will understand and accept the set wastewater treatment energy efficiency rating index threshold.
For step S8:
S8, finding the most suitable power generation power participating in power grid peak shaving through the correlation analysis of the waste water treatment energy efficiency evaluation index and the power generation power so as to ensure that the negative influence on the environment is reduced to the greatest extent while the power demand is met; the method specifically comprises the following substeps:
S81, determining the coal-fired power plant participating in power grid peak shaving based on the environmental grade of the area where the coal-fired power plant is located and a set wastewater treatment energy efficiency evaluation index threshold; for each coal-fired power plant, according to the correlation with the waste water treatment energy efficiency evaluation index in the time-space correlation coefficient matrix, selecting the power generation power with the maximum correlation with the waste water treatment energy efficiency evaluation index threshold as the maximum regulation and control limit of the power plant participating in power grid peak regulation;
S82, carrying out planned cooperative adjustment on the power generation power of a plurality of coal-fired power plants in the district according to the determined coal-fired power plants participating in the peak shaving of the power grid and the corresponding maximum regulation and control limits; the method is characterized in that the requirements of stable operation and power supply of a power grid are met, and meanwhile, the energy efficiency of wastewater treatment of the coal-fired power plant is ensured to reach a set threshold value so as to reduce the negative influence on the local environment;
S83, monitoring and adjusting the waste water treatment equipment and the power generation power of the coal-fired power plant participating in the peak shaving of the power grid through a real-time monitoring and data acquisition system; when any abnormal condition is found or the set threshold value is not met, corresponding emergency response measures are triggered in time so as to ensure the stable operation of the power grid and the safety and reliability of power supply.
In conclusion, the S8 step realizes the deep peak shaving of the internal combustion coal power plant in the district on the basis of comprehensively considering the environmental protection and the power peak shaving requirement; by setting the threshold of the energy efficiency evaluation index of the wastewater treatment and traversing the space-time correlation coefficient matrix of each coal-fired power plant, the power generation with the largest correlation with the energy efficiency evaluation index of the wastewater treatment is selected as the maximum regulation and control limit, and the aim of electric power peak regulation is achieved on the premise of meeting the environmental protection requirement.
As shown in FIG. 2, the embodiment of the invention provides a coal-fired power plant deep peak shaving device considering wastewater treatment. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. In terms of hardware, as shown in fig. 2, a hardware architecture diagram of an electronic device where a depth peaking device of a coal-fired power plant is located, which is provided by an embodiment of the present invention and considers wastewater treatment, besides a processor, a memory, a network interface, and a nonvolatile memory shown in fig. 2, the electronic device where the device is located in the embodiment may generally include other hardware, such as a forwarding chip responsible for processing a message, and so on. Taking a software implementation as an example, as shown in fig. 3, the device in a logic sense is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of an electronic device where the device is located and running the computer program.
As shown in fig. 3, the depth peak shaver device for a coal-fired power plant, which considers wastewater treatment and includes:
the data acquisition module is used for acquiring historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation; the historical power generation wastewater treatment information comprises the operation state of wastewater treatment equipment of each coal-fired power plant in different time windows, the detection result of the treated wastewater sample and the power generation power in the corresponding time window;
The data classification module is used for storing the power generation wastewater data classification model, carrying out data identification on each historical power generation wastewater treatment information by utilizing the power generation wastewater data classification model, and obtaining the operation state of wastewater treatment equipment and wastewater sample detection results in different time windows of each coal-fired power plant;
The operation state of the wastewater treatment equipment comprises electric power, wastewater treatment efficiency, vibration and noise; the wastewater sample detection result comprises heavy metal concentration and type, organic pollutant concentration and type, and nutrient salt concentration and type; generating power within a corresponding time window;
The wastewater treatment equipment evaluation module is used for inputting the operation state of the wastewater treatment equipment into a pre-stored wastewater treatment equipment evaluation model to obtain wastewater treatment equipment evaluation parameters;
The wastewater sample evaluation module is used for inputting the wastewater sample detection result into a pre-stored wastewater sample evaluation model to obtain wastewater sample evaluation parameters;
The wastewater treatment energy efficiency evaluation index calculation module is used for carrying out weighted calculation on the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters in the same coal-fired power plant and the same time window to obtain a wastewater treatment energy efficiency evaluation index;
the space-time correlation coefficient matrix calculation module is used for carrying out correlation analysis on the waste water treatment energy efficiency evaluation index in different time windows of each coal-fired power plant and the power generation power in the corresponding time window to obtain a space-time correlation coefficient matrix between the waste water treatment efficiency and the power generation power of each coal-fired power plant;
The environment grade threshold setting module is used for setting an energy efficiency evaluation index threshold of wastewater treatment according to the environment grade of the area where the coal-fired power plant is located;
The power generation power determining module is used for traversing the space-time correlation coefficient matrix of each coal-fired power plant according to the wastewater treatment energy efficiency evaluation index threshold value to obtain the power generation power with the maximum correlation with the wastewater treatment energy efficiency evaluation index threshold value, and taking the power generation power with the maximum correlation as the maximum regulation and control limit of the coal-fired power plant participating in the power grid peak regulation.
In this embodiment, compared with the conventional power peak shaving method, the system has the following advantages:
Comprehensively considering environmental factors: the system not only pays attention to the requirements of stable operation and power supply of the power grid, but also fully considers the influence of the coal-fired power plant on the environment; by acquiring historical power generation wastewater treatment information and utilizing the information to evaluate the efficiency of wastewater treatment equipment and the quality of wastewater samples, the system can evaluate the influence of coal-fired power plants on the environment and optimize peak regulation strategies to reduce negative influence on the environment;
powerful data acquisition and analysis capabilities: the system is provided with a data acquisition module and a data classification module, and can acquire and process a large amount of historical power generation wastewater treatment information; by utilizing a pre-constructed power generation wastewater data classification model, the system can accurately identify and classify data, so that the operation state of wastewater treatment equipment, wastewater sample detection results, power generation and other information of each coal-fired power plant in different time windows can be obtained;
Evaluation and optimization are combined: the system evaluates the wastewater treatment equipment and the wastewater sample by a wastewater treatment equipment evaluation module, a wastewater sample evaluation module and a wastewater treatment energy efficiency evaluation index calculation module, and calculates a wastewater treatment energy efficiency evaluation index; the index can be used for measuring the wastewater treatment effect of the coal-fired power plant and can be used for carrying out correlation analysis with the generated power; through traversing the space-time correlation coefficient matrix, the system can find the power generation with the maximum correlation with the wastewater treatment energy efficiency evaluation index threshold, so as to optimize the peak regulation strategy of the coal-fired power plant;
Dynamic adjustment and prediction capabilities: the system can dynamically adjust the peak shaving strategy according to the environment and the change of the wastewater treatment effect; by setting the environmental level threshold, the system can monitor the environmental condition of the area where the coal-fired power plant is located in real time and adjust the power generation power according to the requirement; in addition, by analyzing historical data and a prediction model, the system can also predict future environment and wastewater treatment effect, so that the power generation power is adjusted in advance to meet the power grid requirement and reduce the negative influence on the environment;
Automation and intelligence: the system realizes automatic and intelligent operation, and reduces the requirement of manual intervention; the processes of data acquisition, classification, evaluation, optimization and the like can be automatically completed by the system, so that the efficiency and the accuracy are improved; in addition, the system can also perform self-learning and optimization according to actual conditions, and the performance and adaptability of the system are improved continuously;
Adaptability and extensibility: the system has good adaptability and expansibility; the system can adapt to different requirements of coal-fired power plants and power grids, and can be expanded and customized according to the requirements; through modularized design, the system can easily add new functions or expand existing functions so as to meet the changing requirements and technological progress;
Improving the sustainability of the power supply: by optimizing the peak shaving strategy and considering environmental factors, the system is conducive to improving the sustainability of power supply; the method reduces the negative influence on the environment while meeting the power demand, and provides support for the sustainable development in the future.
It will be appreciated that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on a coal-fired power plant depth peaking device that takes into account wastewater treatment. In other embodiments of the invention, a coal-fired power plant depth peaking device that contemplates wastewater treatment may include more or fewer components than illustrated, or may combine certain components, or split certain components, or may be a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The content of information interaction and execution process between the modules in the device is based on the same conception as the embodiment of the method of the present invention, and specific content can be referred to the description in the embodiment of the method of the present invention, which is not repeated here.
The embodiment of the invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for deeply regulating the peak of the coal-fired power plant considering wastewater treatment in any embodiment of the invention is realized.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the computer program when being executed by a processor causes the processor to execute the depth peaking method of the coal-fired power plant considering wastewater treatment in any embodiment of the invention.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media in which program code may be stored, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. A coal-fired power plant deep peak shaving method considering wastewater treatment is characterized by comprising the following steps of:
Acquiring historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation;
Constructing a power generation wastewater data classification model, and carrying out data identification on each piece of historical power generation wastewater treatment information by utilizing the power generation wastewater data classification model to obtain the running states of wastewater treatment equipment in different time windows of each coal-fired power plant, the treated wastewater sample detection results and the power generation in the corresponding time window; the operation state of the wastewater treatment equipment comprises electric power, wastewater treatment efficiency, vibration and noise; the wastewater sample detection result comprises heavy metal concentration and type, organic pollutant concentration and type, and nutrient salt concentration and type;
Inputting the running state of the wastewater treatment equipment into a pre-constructed wastewater treatment equipment evaluation model to obtain wastewater treatment equipment evaluation parameters;
inputting the wastewater sample detection result into a pre-constructed wastewater sample evaluation model to obtain wastewater sample evaluation parameters;
Carrying out weighted calculation on the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters in the same coal-fired power plant and the same time window to obtain a wastewater treatment energy efficiency evaluation index;
Performing correlation analysis on the waste water treatment energy efficiency evaluation indexes in different time windows of each coal-fired power plant and the generated power in the corresponding time windows to obtain a space-time correlation coefficient matrix between the waste water treatment efficiency and the generated power of each coal-fired power plant;
Setting an energy efficiency evaluation index threshold value of wastewater treatment based on the environmental grade of the area where the coal-fired power plant is located;
Traversing a space-time correlation coefficient matrix of each coal-fired power plant according to the wastewater treatment energy efficiency evaluation index threshold value to obtain the power generation power with the maximum correlation with the wastewater treatment energy efficiency evaluation index threshold value, and taking the power generation power with the maximum correlation as the maximum regulation and control limit of the coal-fired power plant participating in the power grid peak shaving;
the space-time correlation coefficient matrix is as follows:
Wherein the same row represents the correlation coefficient between the same power generation power and different waste water treatment energy efficiency evaluation indexes, and the same column represents the correlation coefficient between the same waste water treatment energy efficiency evaluation index and different power generation powers; Representing a correlation coefficient between the nth generated power and the mth wastewater treatment energy efficiency evaluation index in the ith coal-fired power plant;
A method of calculating a correlation coefficient between an energy efficiency evaluation index for wastewater treatment and generated power, comprising:
Setting the wastewater treatment energy efficiency evaluation index set of the coal-fired power plant as X= { X 1,x2,...,xn }, and setting the power generation set as Y= { Y 1,y2,...,yn };
Ordering the data in the sets X and Y, and ordering each element in the sets X and Y according to the order from small to large to obtain new sets X d={xd1,xd2,...,xdn and Y d={yd1,yd2,...,ydn;
Calculating a difference D (i) between each data pair (x di,ydi), wherein i=1, 2..n, the difference representing the degree of difference between the wastewater treatment energy efficiency evaluation index and the generated power;
the calculation formula for calculating the correlation coefficient R between the wastewater treatment energy efficiency evaluation index and the generated power is as follows:
where Σ represents the sum of the differences D (i) for all data pairs (x di,ydi), n being the total number of data pairs;
The construction method of the power generation wastewater data classification model comprises the following steps:
Collecting historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation, wherein the historical power generation wastewater treatment information comprises the running state of wastewater treatment equipment, a wastewater sample detection result and power generation;
Extracting characteristics related to wastewater treatment and power generation from the raw data, wherein the characteristics of the operation state of the wastewater treatment equipment comprise power consumption, wastewater treatment efficiency, vibration and noise; the characteristics of the detection result of the wastewater sample comprise heavy metal concentration, organic pollutant concentration, nutrient salt concentration and the like;
dividing the data into different categories according to preset standards;
Selecting a decision tree model as a framework of a power generation wastewater data classification model to perform classification tasks;
Training, evaluating and optimizing the decision tree model;
Applying the trained model to data of historical power generation wastewater treatment information, classifying the data in each time window, and obtaining the running state of wastewater treatment equipment, a wastewater sample detection result and power generation;
a method of calculating an energy efficiency evaluation index for wastewater treatment comprising:
Determining the weight of an evaluation parameter of wastewater treatment equipment and an evaluation parameter of a wastewater sample; the weight confirmation method comprises an expert scoring method, a historical data method and an experimental method;
Multiplying the determined weight with the wastewater treatment equipment evaluation parameter and the wastewater sample evaluation parameter to obtain a weighted evaluation parameter of each coal-fired power plant;
Summing the weighted evaluation parameters to obtain an energy efficiency evaluation index of wastewater treatment of each coal-fired power plant;
The construction method of the wastewater sample evaluation model comprises the following steps:
selecting an evaluation model according to the characteristics and the evaluation requirements of the wastewater sample; the evaluation model adopts any one of a model based on data driving or a model based on a physical model;
Preparing corresponding input data according to the selected evaluation model; for a model based on data driving, historical detection data related to a wastewater sample is required to be prepared, and for a model based on a physical model, physicochemical parameters and detection conditions of the water sample are required to be provided;
Training the selected evaluation model by using the prepared data, and adjusting model parameters to optimize the evaluation result;
evaluating the trained evaluation model by using another part of the historical data, and checking the performance of the model;
And applying the optimized evaluation model to an actual scene, and evaluating the wastewater sample of the coal-fired power plant.
2. The method of claim 1, wherein the method of obtaining historical power generation wastewater treatment information for a plurality of coal-fired power plants involved in grid regulation comprises:
collecting historical power generation wastewater treatment information from coal-fired power plants participating in power grid regulation;
Cleaning the collected historical data, removing abnormal values and incomplete data, and arranging the cleaned data into a format suitable for subsequent modeling and analysis;
Establishing a data file for each coal-fired power plant participating in power grid regulation;
and designing and establishing a data storage and management system, and ensuring the safety and traceability of data.
3. A coal-fired power plant depth peaking device taking into account wastewater treatment, characterized in that it is based on the method of any one of claims 1-2, comprising:
the data acquisition module is used for acquiring historical power generation wastewater treatment information of a plurality of coal-fired power plants participating in power grid regulation; the historical power generation wastewater treatment information comprises the operation state of wastewater treatment equipment of each coal-fired power plant in different time windows, the detection result of the treated wastewater sample and the power generation power in the corresponding time window;
The data classification module is used for storing the power generation wastewater data classification model, carrying out data identification on each historical power generation wastewater treatment information by utilizing the power generation wastewater data classification model, and obtaining the operation state of wastewater treatment equipment and wastewater sample detection results in different time windows of each coal-fired power plant;
The operation state of the wastewater treatment equipment comprises electric power, wastewater treatment efficiency, vibration and noise; the wastewater sample detection result comprises heavy metal concentration and type, organic pollutant concentration and type, and nutrient salt concentration and type; generating power within a corresponding time window;
The wastewater treatment equipment evaluation module is used for inputting the operation state of the wastewater treatment equipment into a pre-stored wastewater treatment equipment evaluation model to obtain wastewater treatment equipment evaluation parameters;
The wastewater sample evaluation module is used for inputting the wastewater sample detection result into a pre-stored wastewater sample evaluation model to obtain wastewater sample evaluation parameters;
The wastewater treatment energy efficiency evaluation index calculation module is used for carrying out weighted calculation on the wastewater treatment equipment evaluation parameters and the wastewater sample evaluation parameters in the same coal-fired power plant and the same time window to obtain a wastewater treatment energy efficiency evaluation index;
the space-time correlation coefficient matrix calculation module is used for carrying out correlation analysis on the waste water treatment energy efficiency evaluation index in different time windows of each coal-fired power plant and the power generation power in the corresponding time window to obtain a space-time correlation coefficient matrix between the waste water treatment efficiency and the power generation power of each coal-fired power plant;
The environment grade threshold setting module is used for setting an energy efficiency evaluation index threshold of wastewater treatment according to the environment grade of the area where the coal-fired power plant is located;
The power generation power determining module is used for traversing the space-time correlation coefficient matrix of each coal-fired power plant according to the wastewater treatment energy efficiency evaluation index threshold value to obtain the power generation power with the maximum correlation with the wastewater treatment energy efficiency evaluation index threshold value, and taking the power generation power with the maximum correlation as the maximum regulation and control limit of the coal-fired power plant participating in the power grid peak regulation.
4. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the method of any of claims 1-2 when the computer program is executed.
5. A computer readable storage medium, having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of any of claims 1-2.
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| CN111222711A (en) * | 2020-01-16 | 2020-06-02 | 大连理工大学 | Index linkage analysis-based multi-objective optimization method for peak shaving scheduling of electric power system |
| CN112380776A (en) * | 2020-11-24 | 2021-02-19 | 华南理工大学 | Power load control method for reactor state transition probability estimation distribution |
| CN115422714A (en) * | 2022-08-01 | 2022-12-02 | 浙江大学 | Knowledge condition hybrid driving running state monitoring method for gas turbine |
| CN117053172A (en) * | 2023-09-18 | 2023-11-14 | 苏州西热节能环保技术有限公司 | Comprehensive utilization and cooperative peak shaving system for tail end wastewater of power plant and application method |
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| CN112380776A (en) * | 2020-11-24 | 2021-02-19 | 华南理工大学 | Power load control method for reactor state transition probability estimation distribution |
| CN115422714A (en) * | 2022-08-01 | 2022-12-02 | 浙江大学 | Knowledge condition hybrid driving running state monitoring method for gas turbine |
| CN117053172A (en) * | 2023-09-18 | 2023-11-14 | 苏州西热节能环保技术有限公司 | Comprehensive utilization and cooperative peak shaving system for tail end wastewater of power plant and application method |
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