Method and system for monitoring and managing carbon emission of provincial coal-fired power generation

Abstract

The invention discloses a provincial coal-fired power generation carbon emission monitoring and management method and system, and belongs to the technical field of carbon emission monitoring and management, and the method comprises the steps: building an electricity price calculation function through a preset electricity price coefficient and a preset electricity price cardinal number; establishing a carbon emission prediction model, and predicting carbon emission by using the carbon emission prediction model to obtain a carbon emission prediction value; selecting an electricity price coefficient according to the carbon emission predicted value, wherein the electricity price coefficient comprises a first coefficient, a second coefficient and a third coefficient; according to the selected electricity price coefficient, the current electricity price is obtained through calculation by means of the electricity price calculation function, and early warning information is sent out. According to the invention, through a management mechanism of linkage of electricity price and carbon emission, accurate monitoring and effective management of carbon emission of provincial coal-fired power generation can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring and management, and in particular to a provincial coal-fired power generation carbon emission monitoring and management method and system. Background Art

[0002] As global climate change becomes increasingly severe, carbon emission management has become a focus of attention. As one of the main sources of carbon emissions, the monitoring and management of carbon emissions from coal-fired power generation is of great significance for achieving emission reduction targets. However, the existing methods for monitoring and managing carbon emissions from coal-fired power generation have many shortcomings.

[0003] At present, the monitoring methods for carbon emissions at home and abroad mainly include inventory compilation method, actual measurement method, material balance method, model method, etc. When these methods are applied to provincial coal-fired power generation carbon emissions monitoring, there are often problems such as single measurement object and incomplete measurement data, which makes it difficult to meet the needs of accurate monitoring and management; at the same time, electricity price, as an important means to regulate the supply and demand relationship in the electricity market, has a close relationship with carbon emissions. However, the existing electricity price mechanism often fails to fully consider carbon emission factors, resulting in its limited role in promoting energy conservation and emission reduction; in view of the limitations of existing carbon emission monitoring methods and the complexity of the relationship between electricity price and carbon emissions, an innovative provincial coal-fired power generation carbon emission monitoring and management method is needed to achieve accurate monitoring and effective management of coal-fired power generation carbon emissions. Summary of the invention

[0004] To solve the above problems, the present invention provides a provincial coal-fired power generation carbon emission monitoring and management method and system, which adopts a management mechanism that links electricity prices with carbon emissions, and can achieve accurate monitoring and effective management of provincial coal-fired power generation carbon emissions.

[0005] The above objectives can be achieved through the following solutions:

[0006] A provincial coal-fired power generation carbon emission monitoring and management method, comprising: using a preset electricity price coefficient and a preset electricity price base to establish an electricity price calculation function, for an electricity price p, there is

[0007] p=μ*D,

[0008] In the formula, μ is the electricity price coefficient, and D is the electricity price base; a carbon emission prediction model is established, and the carbon emission prediction model is used to predict carbon emissions to obtain a carbon emission prediction value; the electricity price coefficient is selected according to the size of the carbon emission prediction value, and the electricity price coefficient includes a first coefficient, a second coefficient and a third coefficient; according to the selected electricity price coefficient, the current electricity price is calculated using the electricity price calculation function, and an early warning message is issued.

[0009] Optionally, the use of a preset electricity price coefficient and a preset electricity price base to establish an electricity price calculation function includes: collecting historical operating data of a coal-fired power plant, local historical economic indicators, historical electricity demand data and historical electricity prices to obtain a first training set; using the historical operating data of the coal-fired power plant, local historical economic indicators and historical electricity demand data as input, and historical electricity prices as output, and using the first training set to establish and train a neural network model to obtain an electricity price base prediction model; collecting current operating data of the coal-fired power plant, local current economic indicators, and current electricity demand data to obtain an electricity price basic data set; and inputting the electricity price basic data set into the electricity price base prediction model to obtain the current electricity price base.

[0010] Optionally, the establishment of the carbon emission prediction model includes: collecting historical carbon emission data, historical meteorological data, and historical power generation data of coal-fired power plants to construct a comprehensive factor data set; preprocessing the comprehensive factor data set to obtain a second training set; and using the second training set to establish and train a neural network model to obtain a carbon emission prediction model.

[0011] Optionally, the use of the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value includes: collecting current carbon emission data, current meteorological data, and current power generation data of the coal-fired power plant to obtain a real-time data set; inputting the real-time data set into the carbon emission prediction model to obtain a carbon emission prediction value.

[0012] Optionally, the method also includes: collecting actual carbon emission values ​​of coal-fired power plants; calculating the error between the actual carbon emission values ​​and the predicted carbon emission values; when the error is greater than a preset error threshold, using a back propagation algorithm to iteratively optimize the parameters in the carbon emission prediction model; when the error is less than or equal to the error threshold or the number of iterations reaches an upper limit, stopping the optimization to obtain the final carbon emission prediction model.

[0013] Optionally, selecting the electricity price coefficient according to the size of the carbon emission predicted value includes: judging whether the carbon emission predicted value is greater than a preset first threshold; if the carbon emission predicted value is greater than the first threshold, selecting the first coefficient; if the carbon emission predicted value is less than or equal to the first threshold, judging whether the carbon emission predicted value is greater than a second threshold; if the carbon emission predicted value is greater than the second threshold, selecting the second coefficient; if the carbon emission predicted value is less than or equal to the second threshold, selecting the third coefficient.

[0014] Optionally, the method further includes: collecting historical carbon emission data of each coal-fired power plant to calculate a median carbon emission; obtaining a carbon emission target value from a preset database; calculating the first threshold value using the carbon emission target value and the carbon emission median, and for the first threshold value x 1,have

[0015] x 1 =c 1 (m*z+n*G)+k 1 ,

[0016] In the formula, c 1 is the coefficient of change of the first threshold, m is the weight coefficient of the median carbon emission, Z is the median carbon emission, n is the weight coefficient of the carbon emission target value, G is the carbon emission target value, k 1 is the error coefficient of the second threshold; the second threshold is calculated using the carbon emission target value and the carbon emission median, and for the second threshold x 2 ,have

[0017] x 2 =c 2 *(m*Z+n*G)-k 2 ,

[0018] In the formula, c 2 is the coefficient of variation of the second threshold, k 2 is the error coefficient of the second threshold.

[0019] Optionally, the method further includes: collecting carbon emission data and operating data of the coal-fired power plant, and calculating the carbon emission change rate and the operating change rate; using the carbon emission change rate and the operating change rate to calculate a risk assessment value, for the risk assessment value f, there is

[0020] f=α*T+β*Q+λ,

[0021] In the formula, α is the weight coefficient of the carbon emission change rate, T is the carbon emission change rate, β is the weight coefficient of the operating change rate, Q is the operating data, and λ is the error coefficient.

[0022] Optionally, the method also includes: when the risk assessment value is greater than a preset third threshold, determining whether the risk assessment value is greater than a preset fourth threshold; if not, adjusting the variation coefficient of the first threshold, the error coefficient of the first threshold, the variation coefficient of the second threshold, and the error coefficient of the second threshold; if so, adjusting the carbon emission target value.

[0023] Based on the same inventive concept, the present invention also provides a provincial coal-fired power generation carbon emission monitoring and management system, the system comprising: a carbon emission prediction module, used to establish a carbon emission prediction model, and use the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value; an electricity price coefficient selection module, used to select the electricity price coefficient according to the size of the carbon emission prediction value, the electricity price coefficient includes a first coefficient, a second coefficient and a third coefficient; an electricity price calculation module, used to use a preset electricity price coefficient and a preset electricity price base to establish an electricity price calculation function; the electricity price calculation module is also used to calculate the current electricity price according to the selected electricity price coefficient using the electricity price calculation function; an early warning module, used to issue an early warning message based on the current electricity price.

[0024] Compared with the prior art, the present invention has the following advantages:

[0025] 1. The present invention establishes a carbon emission prediction model and uses the model to predict carbon emissions, thereby obtaining a relatively accurate carbon emission prediction value; the model is trained and optimized based on multi-dimensional information such as historical carbon emission data, historical meteorological data, and historical power generation data of coal-fired power plants, thereby improving the accuracy of the prediction;

[0026] 2. The present invention sets up an electricity price coefficient linked to the predicted carbon emission value, so that the electricity price can dynamically reflect the carbon emission situation; when the predicted carbon emission value is high, a higher electricity price coefficient is selected, thereby increasing the operating cost of the power generation enterprise and encouraging it to reduce carbon emissions;

[0027] 3. Compared with the traditional fixed electricity price or the electricity price mechanism that is simply linked to the electricity consumption, the electricity price mechanism of the present invention is more flexible and targeted; it can dynamically adjust the electricity price base and electricity price coefficient according to factors such as economic indicators, electricity demand and operating data of power plants in different regions, so that the electricity price is more in line with the actual market situation and environmental protection policy requirements;

[0028] 4. The present invention realizes real-time monitoring and early warning of carbon emissions and electricity prices by establishing an electricity price calculation function and a carbon emission prediction model; it helps to timely discover potential problems and risks, take corresponding management measures for intervention and adjustment, and improve management efficiency;

[0029] 5. By linking electricity prices with carbon emissions, the present invention encourages power generation companies to adopt more efficient power generation technologies and cleaner energy to reduce carbon emission intensity; it helps promote the green development of the coal-fired power generation industry and promote sustainable economic development.

[0030] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a flow chart of a provincial coal-fired power generation carbon emission monitoring and management method according to an embodiment of the present invention.

[0033] Figure 2 It is a flow chart of the carbon emission prediction model optimization method according to an embodiment of the present invention.

[0034] Figure 3 It is an execution flow chart of a provincial coal-fired power generation carbon emission monitoring and management method according to an embodiment of the present invention.

[0035] Figure 4 It is a flowchart of the first threshold value and the second threshold value optimization method of an embodiment of the present invention.

[0036] Figure 5 It is a schematic diagram of a provincial coal-fired power generation carbon emission monitoring and management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Reference Figure 1 An embodiment of the present invention proposes a provincial coal-fired power generation carbon emission monitoring and management method, which can realize the accurate monitoring and effective management of provincial coal-fired power generation carbon emission through the management mechanism of linking electricity price with carbon emission.

[0039] The method of this embodiment specifically includes:

[0040] Using the preset electricity price coefficient and the preset electricity price base, the electricity price calculation function is established. For the electricity price p,

[0041] p=μ*D,

[0042] In the formula, μ is the electricity price coefficient, and D is the electricity price base;

[0043] Establishing a carbon emission prediction model, and using the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value;

[0044] Selecting the electricity price coefficient according to the magnitude of the predicted carbon emission value, the electricity price coefficient comprising a first coefficient, a second coefficient and a third coefficient;

[0045] According to the selected electricity price coefficient, the current electricity price is calculated using the electricity price calculation function, and an early warning message is issued.

[0046] Specifically, based on the calculated current electricity price, an early warning message will be issued; the purpose of the early warning message is to allow relevant parties (such as regulatory authorities, power plants, etc.) to promptly understand the current electricity price situation so that appropriate measures can be taken; the content of the early warning message may include the specific value of the current electricity price, the trend of electricity price changes, possible influencing factors, etc.

[0047] Specifically, an electricity price calculation function is established through a preset electricity price coefficient and electricity price base, and combined with a carbon emission prediction model to achieve accurate monitoring and effective management of carbon emissions from coal-fired power generation. This method not only improves the accuracy of carbon emission predictions, but also incentivizes companies to reduce carbon emissions through the linkage mechanism between electricity prices and carbon emissions, which helps promote the green development of the coal-fired power generation industry.

[0048] Optionally, the using a preset electricity price coefficient and a preset electricity price base to establish an electricity price calculation function includes:

[0049] Collect historical operation data of coal-fired power plants, local historical economic indicators, historical electricity demand data and historical electricity prices to obtain the first training set;

[0050] Specifically, collect operating data of coal-fired power plants over a period of time, including power generation, coal consumption, equipment operating status, etc.; collect local economic indicator data, such as GDP growth rate, industrial added value, residents' income level, etc., which can reflect the local economic vitality and electricity demand potential; record electricity demand over a period of time, including electricity consumption and electricity load in different time periods (such as seasons, months, days); collect electricity price data over a period of time, which are the basis for training the electricity price base prediction model; by collecting the above data, we can construct the first training set, which contains all the input and output data required to establish the electricity price base prediction model.

[0051] Taking historical operation data of coal-fired power plants, local historical economic indicators and historical electricity demand data as input and historical electricity prices as output, using the first training set to establish and train a neural network model to obtain an electricity price base prediction model;

[0052] Specifically, a neural network model is used to establish an electricity price base prediction model, with historical operating data of coal-fired power plants, local historical economic indicators and historical electricity demand data as input, and historical electricity prices as output, and the neural network model is trained using the first training set; by continuously adjusting the model parameters, the model's prediction results are made as close to the actual electricity prices as possible; after training, an electricity price base prediction model is obtained, which can predict the current electricity price base based on the current operating data, economic indicators and electricity demand data.

[0053] Collect the current operation data of coal-fired power plants, local current economic indicators, and current electricity demand data to obtain the basic data set of electricity prices;

[0054] The electricity price basic data set is input into the electricity price base prediction model to obtain the current electricity price base.

[0055] For example, suppose there is a coal-fired power plant in a certain province, and it is necessary to establish an electricity price calculation function for it; the operating data of the power plant in the past year, the local economic indicator data in the past year, the electricity demand data and the electricity price data are collected; these data are organized into the first training set for training the electricity price base prediction model; a three-layer neural network model is selected as the electricity price base prediction model; the data in the first training set is divided into a training set and a validation set, and the model is trained and validated; after multiple iterations of training, the prediction accuracy of the model on the validation set has reached a high level; currently, the operating data of the power plant has been collected. The data are collected from local governments (such as daily power generation of 1 million kWh, coal consumption of 500 tons, etc.), local economic indicator data (such as GDP growth rate of 5%, industrial added value growth of 8%, etc.) and electricity demand data (such as the daily electricity consumption is predicted to be 900,000 kWh, etc.); these data are organized into a basic electricity price data set and input into the trained electricity price base prediction model; the model outputs the current electricity price base, for example, it can be 0.45 yuan / kWh; assuming that the second coefficient is selected as the electricity price coefficient according to the carbon emission prediction value, the second coefficient can be 1.2, then the current electricity price is 1.2X0.45=0.54 yuan / kWh.

[0056] Optionally, establishing a carbon emission prediction model includes:

[0057] Collect historical carbon emission data, historical meteorological data, and historical power generation data of coal-fired power plants to build a comprehensive factor data set;

[0058] Specifically, historical carbon emission data include records of carbon emissions from coal-fired power plants over a period of time in the past. These data reflect the carbon emissions of power plants under different operating conditions. Meteorological conditions have a significant impact on carbon emissions from coal-fired power plants, such as temperature, humidity, wind speed, etc. Therefore, collecting historical meteorological data is crucial to improving the accuracy of carbon emission forecasts. There is a direct relationship between power generation and carbon emissions. Collecting historical power generation data helps to understand the carbon emission characteristics of power plants under different power generation loads. By integrating the above data, a data set containing comprehensive factors is constructed. These data sets will provide data support for subsequent model training.

[0059] Preprocessing the comprehensive factor data set to obtain a second training set;

[0060] Specifically, outliers, missing values, etc. in the data set are removed to ensure the accuracy and completeness of the data; different types of data are normalized or standardized to improve the efficiency and accuracy of model training; features that have a significant impact on carbon emission prediction are selected from the comprehensive factor data set, or feature extraction techniques (such as principal component analysis) are used to reduce data dimensions and improve model performance; the data set after these preprocessing is called the second training set, which will be used for subsequent model training.

[0061] The second training set is used to establish and train a neural network model to obtain a carbon emission prediction model.

[0062] Specifically, a neural network model with two hidden layers can be selected as the carbon emission prediction model. The data in the second training set is divided into a training set and a validation set, and the model is trained and validated. During the training process, cross-validation technology is used to evaluate the performance of the model, and the parameters of the model are continuously adjusted to improve the prediction accuracy. After the training is completed, a neural network model that can predict future carbon emissions is obtained. Through the above steps, a neural network model that can predict provincial coal-fired power generation carbon emissions is successfully established, which not only improves the accuracy of carbon emission predictions, but also provides strong support for subsequent carbon emission management and electricity price adjustments.

[0063] Optionally, the using the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value includes:

[0064] Collect current carbon emission data, current meteorological data, and current power generation data of coal-fired power plants to obtain real-time data sets;

[0065] The real-time data set is input into the carbon emission prediction model to obtain a carbon emission prediction value.

[0066] For example, suppose there is a coal-fired power plant in a certain province, and a carbon emission prediction model has been established for it; the current carbon emission data of the power plant is obtained in real time through online monitoring equipment, assuming it is 500 tons / hour; the current meteorological data is obtained from the local meteorological station, including temperature of 25°C, humidity of 60%, wind speed of 3 meters / second, etc.; the current power generation data is obtained through the monitoring system of the power plant, assuming it is 1 million kWh / hour; these data are integrated to form a real-time data set; the real-time data set is input into the trained carbon emission prediction model, which may be trained based on historical data of the past few years and can identify the impact of factors such as power generation and meteorological conditions on carbon emissions; after receiving the real-time data set, the model performs complex calculations and analyses; based on the algorithms and parameters within the model, it predicts that under current conditions, the future carbon emissions of the power plant may be 510 tons / hour; after obtaining the carbon emission prediction value, it can be used in subsequent carbon emission management and electricity price adjustment, such as taking emission reduction measures or adjusting electricity price strategies to encourage power plants to reduce carbon emissions.

[0067] Alternatively, if Figure 2 As shown, the method also includes:

[0068] Collect actual carbon emissions from coal-fired power plants;

[0069] Specifically, in practical applications, it is necessary to regularly collect the actual values ​​of carbon emissions from coal-fired power plants; these actual values ​​can be obtained through online monitoring equipment, emission testing or third-party verification; the purpose of collecting actual values ​​is to compare them with the predicted values ​​of the model to evaluate the accuracy of the model.

[0070] Calculate the error between the actual carbon emissions and the predicted carbon emissions;

[0071] Specifically, the actual value of carbon emissions is compared with the predicted value of carbon emissions, and the error between them is calculated; the error can be an absolute error (i.e., the absolute value of the difference between the actual value and the predicted value) or a relative error (i.e., the ratio of the error to the actual value); the size of the error reflects the accuracy of the model prediction, and the smaller the error, the more accurate the model prediction.

[0072] When the error is greater than a preset error threshold, a back propagation algorithm is used to iteratively optimize the parameters in the carbon emission prediction model;

[0073] Specifically, when the error is greater than the preset error threshold, it means that the prediction accuracy of the model needs to be improved. At this time, the back propagation algorithm can be used to optimize the model; the back propagation algorithm is a commonly used neural network optimization algorithm, which calculates the gradient of the error and then adjusts the parameters of the model in the opposite direction of the gradient to reduce the error; during the optimization process, the training data set can be repeatedly used to iteratively train the model until the error is less than or equal to the preset error threshold or the number of iterations reaches the upper limit.

[0074] When the error is less than or equal to the error threshold or the number of iterations reaches an upper limit, the optimization is stopped to obtain a final carbon emission prediction model.

[0075] Specifically, when the error is less than or equal to the preset error threshold, it means that the prediction accuracy of the model has met the requirements. At this time, the optimization can be stopped to obtain the final carbon emission prediction model; if the number of iterations reaches the upper limit but the error is still greater than the preset error threshold, the optimization can also be stopped, but it should be noted that the prediction accuracy of the model may still need to be improved.

[0076] For example, suppose there is a coal-fired power plant in a certain province, and a carbon emission prediction model has been established for it; in the past month, the actual carbon emission values ​​of the power plant have been collected regularly, for example, once a day, and a total of 30 actual values ​​have been collected; these 30 actual values ​​are compared with the predicted values ​​of the model, and the absolute error between them is calculated; assuming that the preset error threshold is 10 tons / hour, after calculation, it is found that the error on some days exceeds this threshold, indicating that the prediction accuracy of the model needs to be improved; when it is found that the error is greater than the preset error threshold, the back propagation algorithm is used to optimize the model, and the historical data of the past year is selected as the training data set, and the model is iteratively trained. In each iteration, the gradient of the error is calculated, and the parameters of the model are adjusted in the opposite direction of the gradient; after many iterations, it is found that the error gradually decreases, and eventually is less than or equal to the preset error threshold; when the error is less than or equal to the preset error threshold, the optimization process is stopped, and the final carbon emission prediction model is obtained.

[0077] Alternatively, if Figure 3 As shown, the selection of the electricity price coefficient according to the size of the carbon emission prediction value includes:

[0078] Determining whether the carbon emission prediction value is greater than a preset first threshold;

[0079] Specifically, the carbon emission forecast value represents the future carbon emissions of coal-fired power plants. By predicting carbon emissions, the environmental performance of the power plant can be evaluated and the electricity price coefficient can be adjusted accordingly. The electricity price coefficient is a parameter used to adjust electricity prices. By changing the electricity price coefficient, the revenue of the power plant can be affected, thereby incentivizing it to reduce carbon emissions. In order to reasonably judge the carbon emission forecast value and select the corresponding electricity price coefficient, multiple thresholds need to be preset. These thresholds can be set according to policy objectives, environmental protection standards and the actual situation of the power plant.

[0080] If the predicted carbon emission value is greater than the first threshold, selecting the first coefficient;

[0081] Specifically, first determine whether the carbon emission prediction value is greater than the preset first threshold. If it is greater, it means that the carbon emissions of the power plant are high and more stringent electricity price adjustment measures are needed. Therefore, the first coefficient is selected (usually a smaller coefficient used to reduce the electricity price).

[0082] If the predicted carbon emission value is less than or equal to the first threshold, determining whether the predicted carbon emission value is greater than a second threshold;

[0083] If the predicted carbon emission value is greater than the second threshold, selecting the second coefficient;

[0084] Specifically, if the predicted carbon emission value is less than or equal to the first threshold, it is further determined whether it is greater than the second threshold; if it is greater, it means that the carbon emissions of the power plant are at a medium level and certain electricity price adjustment measures need to be taken, so the second coefficient is selected (usually a moderate coefficient used to moderately adjust the electricity price).

[0085] If the predicted carbon emission value is less than or equal to the second threshold, the third coefficient is selected.

[0086] Specifically, if the predicted carbon emission value is less than or equal to the second threshold, it means that the carbon emission of the power plant is low and has reached a good environmental protection standard, so the third coefficient (usually a higher coefficient used to increase the electricity price) is selected.

[0087] For example, suppose there is a coal-fired power plant in a province, and we hope to incentivize it to reduce carbon emissions by adjusting electricity prices; suppose the preset first threshold is 1000 tons / hour and the second threshold is 800 tons / hour; through the carbon emission prediction model, the future carbon emission prediction value of the power plant is obtained to be 900 tons / hour; first, determine whether the carbon emission prediction value (900 tons / hour) is greater than the first threshold (1000 tons / hour). Since 900 tons / hour is less than 1000 tons / hour, the condition is not met; then, determine whether the carbon emission prediction value (900 tons / hour) is greater than the second threshold ( 800 tons / hour), since 900 tons / hour is greater than 800 tons / hour, the condition is met; according to the judgment result, the second coefficient is selected as the electricity price coefficient, assuming that the second coefficient is 1.2 (that is, the electricity price is increased by 20% on the original basis); according to the selected electricity price coefficient (1.2), the electricity price of the power plant is adjusted, for example, if the original electricity price is 0.5 yuan / kWh, the adjusted electricity price is 0.6 yuan / kWh; through such a mechanism, carbon emissions can be closely combined with electricity prices, and power plants can be encouraged to reduce carbon emissions through electricity price adjustments, thereby achieving the dual goals of environmental protection and economic benefits.

[0088] Alternatively, if Figure 4 As shown, the method also includes:

[0089] Collect historical carbon emission data of each coal-fired power plant and calculate the median carbon emission;

[0090] Specifically, it is necessary to collect historical carbon emission data of each coal-fired power plant. These data may include carbon emissions and carbon emission intensity over a period of time in the past; use the collected historical carbon emission data to calculate the median carbon emissions; the median is a statistic, which means that in a set of data, half of the data values ​​are smaller than it, and half of the data values ​​are larger than it; choosing the median as a reference point can reduce the impact of extreme values ​​on the results.

[0091] Obtain carbon emission target value from a preset database;

[0092] Specifically, a carbon emission target value is obtained from a preset database; this target value may be set by a regulatory agency to guide power plants to achieve long-term goals for reducing carbon emissions.

[0093] The first threshold value is calculated using the carbon emission target value and the carbon emission median. For the first threshold value x 1 ,have

[0094] x 1 =c 1 (m*Z+n*G)+k 1 ,

[0095] In the formula, c1 is the coefficient of change of the first threshold, m is the weight coefficient of the median carbon emission, Z is the median carbon emission, n is the weight coefficient of the carbon emission target value, G is the carbon emission target value, k 1 is the error coefficient of the second threshold;

[0096] The second threshold value is calculated using the carbon emission target value and the carbon emission median. For the second threshold value x 2 ,have

[0097] x 2 =c 2 *(m*Z+n*G)-k 2 ,

[0098] In the formula, c 2 is the coefficient of variation of the second threshold, k 2 is the error coefficient of the second threshold.

[0099] For example, assuming that the median carbon emission is 900 tons / hour, the carbon emission target is 800 tons / hour, the variation coefficient of the first threshold is 1.1, the variation coefficient of the second threshold is 0.9, the weight coefficient of the median carbon emission is 0.6, the weight coefficient of the carbon emission target is 0.4, the error coefficient of the first threshold is 50, and the error coefficient of the second threshold is 30; calculate the first threshold x according to the formula 1 =1.1×(0.6×900+0.4×800)+50=996 tons / hour; Calculate the second threshold x according to the formula 2 =0.9×(0.6×900+0.4×800)-30=744 tons / hour; therefore, according to these parameters and formulas, the calculated first threshold is 996 tons / hour and the second threshold is 744 tons / hour; these thresholds can be used in subsequent electricity price adjustment mechanisms to incentivize power plants to reduce carbon emissions; if the carbon emissions of a power plant are lower than the second threshold, a higher electricity price can be obtained; if the carbon emissions are higher than the first threshold, a higher electricity price will need to be paid or other penalties will be faced.

[0100] Alternatively, if Figure 4 As shown, the method also includes:

[0101] Collect carbon emission data and operating data of coal-fired power plants, and calculate the carbon emission change rate and operating change rate;

[0102] Specifically, it is necessary to collect carbon emission data and operating data of coal-fired power plants. Carbon emission data can include annual carbon emissions, carbon emission intensity, etc.; operating data can include power generation, sales revenue, costs, etc.; calculate the difference between the total carbon emissions of the current year and the total carbon emissions of the previous year, which directly reflects the annual changes in carbon emissions of the power plant; calculate the difference between the operating data of the current year and the operating data of the previous year (for example, the difference in power generation or the difference in sales revenue), which directly reflects the annual changes in the operation of the power plant.

[0103] The risk assessment value is calculated using the carbon emission change rate and the operation change rate. For the risk assessment value f, we have

[0104] f=α*T+β*Q+λ,

[0105] In the formula, α is the weight coefficient of the carbon emission change rate, T is the carbon emission change rate, β is the weight coefficient of the operating change rate, Q is the operating data, and λ is the error coefficient.

[0106] For example, assuming that the carbon emission change rate is 0.1 (indicating that the carbon emissions are 10% different from the previous year), the operating change rate is 0.05 (indicating that the sales revenue is 5% different from the previous year), the weight coefficient of the carbon emission change rate is 0.6, the weight coefficient of the operating change rate is 0.4, and the error coefficient is 0.01; the risk assessment value is calculated according to the formula f=0.6×0.1+0.4×0.05+0.01=0.09; the risk level of the power plant is assessed by judging the risk assessment value. When the operation or carbon emissions change greatly (indicating that these changes have a greater impact on coal-fired enterprises), the carbon emission threshold is adjusted to re-judge the electricity price coefficient.

[0107] Alternatively, if Figure 4 As shown, the method also includes:

[0108] When the risk assessment value is greater than a preset third threshold, determining whether the risk assessment value is greater than a preset fourth threshold;

[0109] Specifically, first, the carbon emission data and operating data of the coal-fired power plant are collected, the carbon emission change rate and the operating change rate are calculated, and these change rates are used to calculate the risk assessment value. This risk assessment value reflects the comprehensive risk level of the power plant in terms of carbon emissions and operations; when the risk assessment value is greater than the preset third threshold, it means that the power plant is currently facing a high level of risk and may need to adjust its carbon emission threshold or carbon emission target value; next, further determine whether the risk assessment value is greater than the preset fourth threshold.

[0110] If not, adjusting the variation coefficient of the first threshold, the error coefficient of the first threshold, the variation coefficient of the second threshold, and the error coefficient of the second threshold;

[0111] Specifically, if the risk assessment value is not greater than the fourth threshold, the variation coefficient of the first threshold, the error coefficient of the first threshold, the variation coefficient of the second threshold, and the error coefficient of the second threshold are adjusted; the adjustment of these coefficients can affect the calculation of the carbon emission threshold, and then adjust the selection of the electricity price coefficient, thereby indirectly affecting the behavior of the power plant.

[0112] If so, the carbon emission target value is adjusted.

[0113] Specifically, if the risk assessment value is greater than the fourth threshold, it is considered that the risk faced by the power plant is extremely high and the carbon emission target value needs to be adjusted directly; the adjustment of the carbon emission target value is a more direct and significant means, which can directly affect the carbon emission behavior of the power plant.

[0114] For example, suppose there is a coal-fired power plant in a certain province with carbon emission data of 10 million tons this year and 9 million tons last year; operating data of power generation this year is 50 billion kWh, last year was 45 billion kWh, sales revenue this year is 2 billion yuan, last year was 1.8 billion yuan; carbon emission change rate is 0.11, and operating change rate (taking power generation as an example) is 0.11; assuming that the weight coefficient of carbon emission change rate is 0.6, the weight coefficient of operating change rate is 0.4, and the error coefficient is 0.01; the risk assessment value is f = 0.6 × 0.11 + 0.4 × 0.11 - 0.01 = 0.11; assuming that the third threshold is 0.1 , the fourth threshold is 0.15. Since the risk assessment value 0.11 is greater than the third threshold 0.1, further judgment is needed. Since the risk assessment value 0.11 is not greater than the fourth threshold 0.15, it is decided to adjust the correlation coefficient of the carbon emission threshold; for example, the coefficient of change of the first threshold is adjusted from 1.1 to 1.05, and the coefficient of change of the second threshold is adjusted from 0.9 to 0.95, so as to slightly relax the carbon emission threshold and give the power plant a certain buffer space; through this process, the present invention can dynamically adjust the carbon emission threshold or the carbon emission target value according to the risk level of the power plant, thereby achieving more refined and effective management of carbon emissions from coal-fired power generation.

[0115] Based on the same inventive concept, Figure 5 As shown, the present invention also provides a provincial coal-fired power generation carbon emission monitoring and management system, the system comprising:

[0116] A carbon emission prediction module is used to establish a carbon emission prediction model and use the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value;

[0117] An electricity price coefficient selection module, used to select the electricity price coefficient according to the magnitude of the carbon emission prediction value, the electricity price coefficient including a first coefficient, a second coefficient and a third coefficient;

[0118] An electricity price calculation module is used to establish an electricity price calculation function using a preset electricity price coefficient and a preset electricity price base; the electricity price calculation module is also used to calculate the current electricity price using the electricity price calculation function according to the selected electricity price coefficient;

[0119] The early warning module is used to issue early warning information according to the current electricity price.

[0120] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean direct connection of the lines, and the indirect connection mode can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0121] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptive change of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described in the present invention.

Claims

1. A provincial coal-fired power generation carbon emission monitoring and management method, characterized in that: The method comprises: Using the preset electricity price coefficient and the preset electricity price base, the electricity price calculation function is established. For the electricity price p, p=μ*D, In the formula, μ is the electricity price coefficient, and D is the electricity price base; Establishing a carbon emission prediction model, and using the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value; Selecting the electricity price coefficient according to the magnitude of the predicted carbon emission value, the electricity price coefficient comprising a first coefficient, a second coefficient and a third coefficient; According to the selected electricity price coefficient, the current electricity price is calculated using the electricity price calculation function, and an early warning message is issued.

2. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 1, characterized in that: The method of establishing an electricity price calculation function by using a preset electricity price coefficient and a preset electricity price base includes: Collect historical operation data of coal-fired power plants, local historical economic indicators, historical electricity demand data and historical electricity prices to obtain the first training set; Taking historical operation data of coal-fired power plants, local historical economic indicators and historical electricity demand data as input and historical electricity prices as output, using the first training set to establish and train a neural network model to obtain an electricity price base prediction model; Collect the current operation data of coal-fired power plants, local current economic indicators, and current electricity demand data to obtain the basic data set of electricity prices; The electricity price basic data set is input into the electricity price base prediction model to obtain the current electricity price base.

3. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 1, characterized in that: The establishment of the carbon emission prediction model comprises: Collect historical carbon emission data, historical meteorological data, and historical power generation data of coal-fired power plants to build a comprehensive factor data set; Preprocessing the comprehensive factor data set to obtain a second training set; The second training set is used to establish and train a neural network model to obtain a carbon emission prediction model.

4. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 1, characterized in that: The method of using the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value includes: Collect current carbon emission data, current meteorological data, and current power generation data of coal-fired power plants to obtain real-time data sets; The real-time data set is input into the carbon emission prediction model to obtain a carbon emission prediction value.

5. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 4, characterized in that: The method further comprises: Collect actual carbon emissions from coal-fired power plants; Calculate the error between the actual carbon emissions and the predicted carbon emissions; When the error is greater than a preset error threshold, a back propagation algorithm is used to iteratively optimize the parameters in the carbon emission prediction model; When the error is less than or equal to the error threshold or the number of iterations reaches an upper limit, the optimization is stopped to obtain a final carbon emission prediction model.

6. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 1, characterized in that: The selecting the electricity price coefficient according to the magnitude of the carbon emission prediction value comprises: Determining whether the predicted carbon emission value is greater than a preset first threshold; If the predicted carbon emission value is greater than the first threshold, selecting the first coefficient; If the predicted carbon emission value is less than or equal to the first threshold, determining whether the predicted carbon emission value is greater than a second threshold; If the predicted carbon emission value is greater than the second threshold, selecting the second coefficient; If the predicted carbon emission value is less than or equal to the second threshold, the third coefficient is selected.

7. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 6, characterized in that: The method further comprises: Collect historical carbon emission data of each coal-fired power plant and calculate the median carbon emission; Obtain carbon emission target value from a preset database; The first threshold value is calculated using the carbon emission target value and the carbon emission median. For the first threshold value x1, x1=c1(m*Z+n*G)+k1, Wherein, c1 is the coefficient of variation of the first threshold, m is the weight coefficient of the median carbon emission, Z is the median carbon emission, n is the weight coefficient of the carbon emission target value, G is the carbon emission target value, and k1 is the error coefficient of the second threshold; The second threshold value is calculated using the carbon emission target value and the carbon emission median. For the second threshold value x2, x2=c2*(m*Z+n*G)-k2, In the formula, c2 is the variation coefficient of the second threshold, and k2 is the error coefficient of the second threshold.

8. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 7, characterized in that: The method further comprises: Collect carbon emission data and operating data of coal-fired power plants, and calculate the carbon emission change rate and operating change rate; The risk assessment value is calculated using the carbon emission change rate and the operation change rate. For the risk assessment value f, we have f=α*T+β*Q+λ, In the formula, α is the weight coefficient of the carbon emission change rate, T is the carbon emission change rate, β is the weight coefficient of the operating change rate, Q is the operating data, and λ is the error coefficient.

9. A provincial coal-fired power generation carbon emission monitoring and management method according to claim 8, characterized in that: The method further comprises: When the risk assessment value is greater than a preset third threshold, determining whether the risk assessment value is greater than a preset fourth threshold; If not, adjusting the variation coefficient of the first threshold, the error coefficient of the first threshold, the variation coefficient of the second threshold, and the error coefficient of the second threshold; If so, the carbon emission target value is adjusted.

10. A provincial coal-fired power generation carbon emission monitoring and management system, characterized in that: The system comprises: A carbon emission prediction module is used to establish a carbon emission prediction model and use the carbon emission prediction model to predict carbon emissions to obtain a carbon emission prediction value; An electricity price coefficient selection module, used to select the electricity price coefficient according to the magnitude of the carbon emission prediction value, the electricity price coefficient including a first coefficient, a second coefficient and a third coefficient; An electricity price calculation module is used to establish an electricity price calculation function using a preset electricity price coefficient and a preset electricity price base; the electricity price calculation module is also used to calculate the current electricity price using the electricity price calculation function according to the selected electricity price coefficient; The early warning module is used to issue early warning information according to the current electricity price.

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