Power system carbon emission metering method based on dynamic electric carbon emission factor

Abstract

The invention discloses an electric power system carbon emission metering method based on a dynamic electric carbon emission factor, and relates to the technical field of electric power systems, and the method comprises the following steps: building a monitoring network through multiple types of sensors, and collecting the operation data of each link of an electric power system; according to the collected data and the external environment data, generating a dynamic electric carbon emission factor by using an algorithm; based on the dynamic electric carbon emission factor, calculating the total carbon emission amount of the power system; a carbon emission calculation result is monitored in real time, and a calculation model and parameters are fed back and adjusted. A monitoring network is constructed through multiple types of sensors, comprehensive acquisition of operation data of a power system is achieved, dynamic electric carbon emission factors are generated in combination with external environment data, the limitation of traditional fixed factor metering is broken through, and metering accuracy is remarkably improved. The total amount of carbon emission is calculated on the basis of dynamic electric carbon emission factors, a calculation model is optimized through a real-time monitoring and feedback adjustment mechanism, and real-time and accurate metering of the carbon emission of the electric power system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and specifically to a method for measuring carbon emissions in a power system based on dynamic electricity carbon emission factors. Background Art

[0002] With the increasing global attention to climate change issues, the power industry, as a key area of carbon emissions, accurately measuring its carbon emissions has become the key to achieving energy conservation and emission reduction. Traditional methods for measuring carbon emissions in power systems mostly use fixed electricity carbon emission factors, which cannot reflect the dynamic changes of factors such as energy structure, region, and time during the power generation process. This method leads to a large deviation between the measured carbon emission results and the actual situation, and cannot provide accurate basis for carbon emission management and emission reduction decision-making in the power industry. In addition, traditional measurement methods lack a real-time monitoring and feedback adjustment mechanism, making it difficult to adapt to the complex and changeable operating characteristics of power systems and unable to meet the increasingly strict carbon emission supervision requirements. Summary of the Invention

[0003] To solve the above technical problems, a method for measuring carbon emissions in a power system based on dynamic electricity carbon emission factors is provided, and this technical solution solves the above problems.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A method for measuring carbon emissions in a power system based on dynamic electricity carbon emission factors, comprising the following steps:

[0006] Construct a monitoring network with multiple types of sensors to collect operation data of each link in the power system;

[0007] Generate dynamic electricity carbon emission factors by using algorithms based on the collected data and external environment data;

[0008] Based on the dynamic electricity carbon emission factors, calculate the total carbon emissions of the power system;

[0009] Real-time monitor the carbon emission calculation results and feedback to adjust the calculation model and parameters.

[0010] Preferably, the step of constructing a monitoring network with multiple types of sensors to collect operation data of each link in the power system specifically includes:

[0011] The sensor nodes and the data aggregation unit are connected through a wired or wireless communication network;

[0012] Different types of sensor nodes are distributed in the power generation, transmission, distribution, and power consumption links;

[0013] The data aggregation unit performs preliminary processing and storage on the data transmitted by the sensor nodes.

[0014] Preferably, the generation of the dynamic electricity carbon emission factor based on the collected data and the external environment data specifically includes:

[0015] The external environment data includes regional energy structure, time, and meteorological data;

[0016] Using data mining technology, screen out the key features affecting the electricity carbon emission factor from the collected data and the external environment data;

[0017] Based on these data and features, construct a calculation model for the dynamic electricity carbon emission factor.

[0018] Preferably, when using data mining technology to screen out the key features affecting the electricity carbon emission factor from the collected data and the external environment data, the mutual information algorithm is specifically adopted, and the mutual information calculation formula is:

[0019]

[0020] Among them, I(X; Y) is the mutual information between variables X and Y, p(x, y) is the joint probability distribution of X and Y, and p(x) and p(y) are the marginal probability distributions of X and Y respectively.

[0021] Preferably, the construction of the calculation model for the dynamic electricity carbon emission factor based on these data and features specifically includes:

[0022] To reflect the influence degree of different factors on the electricity carbon emission factor, a weighted summation method is used to construct the calculation model, and the formula is:

[0023]

[0024] Among them, C is the dynamic electricity carbon emission factor, w i is the weight of each influencing factor, and x i is the data value of each influencing factor.

[0025] Preferably, the calculation of the total carbon emissions of the power system based on the dynamic electricity carbon emission factor specifically includes:

[0026] Obtain the electricity quantity data of each link of the power system;

[0027] Multiply the dynamic electricity carbon emission factor by the electricity quantity data of the corresponding link to obtain the carbon emissions of each link;

[0028] Accumulate the carbon emissions of each link to obtain the total carbon emissions of the power system.

[0029] Preferably, the calculation formula for the total carbon emissions of the power system is as follows:

[0030] E j = C × Q j

[0031]

[0032] Among them, E j is the carbon emission of the j-th link, Q j is the electricity consumption of the j-th link, and E is the total carbon emission of the power system.

[0033] Preferably, the real-time monitoring of the carbon emission calculation results and the feedback adjustment of the calculation model and parameters specifically include:

[0034] Comparing the carbon emission calculation results with the preset standards;

[0035] According to the comparison results, use the feedback control algorithm to adjust the parameters of the calculation model;

[0036] Optimize the calculation model of the dynamic electricity carbon emission factor.

[0037] Preferably, the feedback control algorithm adopts the PID control algorithm, and its control law is:

[0038]

[0039] Among them, u(t) is the output of the controller, e(t) is the deviation signal of the system, and K p , K i , K d are the proportional coefficient, integral coefficient, and differential coefficient respectively.

[0040] Preferably, after adjusting the parameters of the calculation model and optimizing the calculation model, recalculate the dynamic electricity carbon emission factor and the total carbon emission of the power system to form a closed-loop optimization control process.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: By constructing a monitoring network with multiple types of sensors, comprehensive collection of power system operation data is realized, and combined with external environmental data, a dynamic electricity carbon emission factor is generated, breaking through the limitations of traditional fixed factor measurement and significantly improving the measurement accuracy. Based on the dynamic electricity carbon emission factor, the total carbon emission is calculated, and the calculation model is optimized through real-time monitoring and feedback adjustment mechanisms, realizing real-time and accurate measurement of the carbon emission of the power system, providing reliable data support for the carbon emission management and emission reduction decision-making of the power industry, and helping the power industry achieve the energy conservation and emission reduction goals. Description of the Drawings

[0042] Figure 1 is the method flow chart of the present invention. Detailed Embodiments

[0043] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variants.

[0044] Referring to Figure 1 as shown, a power system carbon emission measurement method based on a dynamic electricity carbon emission factor includes the following steps:

[0045] Construct a monitoring network with multiple types of sensors to collect the operation data of each link of the power system;

[0046] Generate a dynamic electricity carbon emission factor by using an algorithm based on the collected data and external environment data;

[0047] Calculate the total carbon emissions of the power system based on the dynamic electricity carbon emission factor;

[0048] Real-time monitor the carbon emission calculation results and feedback to adjust the calculation model and parameters.

[0049] Specifically, first, construct a monitoring network with multiple types of sensors, which is the basis for data collection. Different types of sensors can sense various physical quantities in each link of the power system, such as operation data like current, voltage, power, etc., so as to comprehensively and accurately obtain the actual operation status of the power system in each link. Then, generate a dynamic electricity carbon emission factor by using a specific algorithm based on the collected operation data and external environment data. External environment data will affect the carbon emissions in the power production process. For example, different regional energy structures, with different proportions of hydropower, thermal power, wind power, etc., will result in different carbon emission situations; time factors, the electricity demand and power generation methods vary in different seasons and time periods; meteorological data, such as light and wind speed, will affect the output of renewable energy power generation. By comprehensively considering these factors through the algorithm, the generated dynamic electricity carbon emission factor can more accurately reflect the carbon emission characteristics in the power production process. Next, calculate the total carbon emissions of the power system based on the dynamic electricity carbon emission factor. This is to apply the generated factor to the actual carbon emission measurement to provide a quantitative result for the carbon emission situation of the power system. Finally, real-time monitor the carbon emission calculation results and feedback to adjust the calculation model and parameters. By continuously comparing the calculation results with the actual situation, optimize the model and parameters so that the measurement method can adapt to the dynamic changes of the power system. This method breaks the limitations of traditional fixed electricity carbon emission factor measurement, and can reflect the carbon emission situation of the power system in real time and accurately. The dynamic electricity carbon emission factor takes into account the dynamic changes of various factors, making the measurement results closer to the actual situation, providing more reliable data support for power enterprises to formulate energy conservation and emission reduction strategies, and helping the power industry achieve green and sustainable development.

[0050] The construction of a monitoring network with multiple types of sensors to collect the operation data of each link in the power system specifically includes:

[0051] The sensor nodes are connected to the data aggregation unit through a wired or wireless communication network;

[0052] Sensor nodes of different types are distributed in the power generation, transmission, distribution, and consumption links;

[0053] The data aggregation unit performs preliminary processing and storage on the data transmitted by the sensor nodes.

[0054] Specifically, the specific method of constructing the monitoring network to collect operation data is further refined. The sensor nodes are connected to the data aggregation unit through a wired or wireless communication network. This flexible connection method can be selected according to the actual layout and environmental conditions of the power system. Wired connections have high stability and are suitable for scenarios with relatively stable environments and short distances; wireless connections have the advantages of convenient installation and strong scalability and are suitable for scattered and remote monitoring points. Sensor nodes of different types are distributed in the power generation, transmission, distribution, and consumption links, comprehensively covering all key parts of the power system. Sensors in the power generation link can monitor the operating efficiency, fuel consumption, etc. of the generator set; the transmission link can monitor line losses, transmission power, etc.; the distribution link can monitor the load conditions of transformers, etc.; the consumption link can understand the electricity consumption patterns and load changes of users. The data aggregation unit performs preliminary processing and storage on the data transmitted by the sensor nodes, performs operations such as data cleaning, screening, and integration on the data, removes noise and invalid data, and stores the processed data at the same time, facilitating subsequent analysis and use. Through this distributed and comprehensive monitoring network, detailed operation data of each link in the power system can be obtained, providing a rich and accurate data basis for the generation of subsequent dynamic electricity carbon emission factors and the calculation of the total carbon emissions. At the same time, the preliminary processing and storage functions of the data aggregation unit improve the quality and usability of the data, reducing the workload and difficulty of subsequent data processing.

[0055] The generation of dynamic electricity carbon emission factors based on the collected data and external environmental data specifically includes:

[0056] External environmental data covers regional energy structure, time, and meteorological data;

[0057] Using data mining techniques, key features affecting the electricity carbon emission factors are screened out from the collected data and external environmental data;

[0058] Based on these data and features, a dynamic electricity carbon emission factor calculation model is constructed.

[0059] Specifically, the process of generating dynamic electricity carbon emission factors is described in detail. The external environmental data covers regional energy structure, time, and meteorological data, which are important factors affecting carbon emissions in power production. The regional energy structure determines the main mode of power production. Regions with a high proportion of thermal power have relatively high carbon emissions, while regions with a high proportion of clean energy such as hydropower and wind power have relatively low carbon emissions. The time factor affects electricity demand and power generation methods. For example, during the peak air-conditioning electricity consumption in summer and the peak heating electricity consumption in winter, the power generation mix may be different at different times. Meteorological data has a significant impact on renewable energy power generation. When the sunlight is sufficient, the output of solar power generation is large, and when the wind speed is appropriate, the efficiency of wind power generation is high. Data mining technology is used to screen out the key features that affect electricity carbon emission factors from the collected data and external environmental data. Data mining technology can discover hidden patterns and relationships from a large amount of data and identify the factors that have a greater impact on electricity carbon emission factors. Based on these screened data and features, a dynamic electricity carbon emission factor calculation model is constructed. The model can comprehensively consider the influence of various factors and generate corresponding dynamic electricity carbon emission factors according to different input data. By considering external environmental data and using data mining technology to construct a calculation model, the dynamic electricity carbon emission factor can more accurately reflect the carbon emissions in the power production process; by screening key features and constructing the model, the scientificity and reliability of the factor are improved, providing strong support for accurately measuring carbon emissions in the power system.

[0060] The data mining technology is used to screen out the key features that affect electricity carbon emission factors from the collected data and external environmental data. Specifically, the mutual information algorithm is adopted, and the mutual information calculation formula is as follows:

[0061]

[0062] Among them, I(X; Y) is the mutual information between variables X and Y, p(x, y) is the joint probability distribution of X and Y, and p(x) and p(y) are the marginal probability distributions of X and Y respectively.

[0063] Specifically, the mutual information algorithm for screening key features from the collected data and external environmental data is defined. The mutual information algorithm is used to measure the correlation between two variables. By calculating the mutual information between variables, it is possible to determine which factors have a strong linear correlation with the electricity carbon emission factor. In this method, by calculating the mutual information between each factor in the collected data and external environmental data, factors with larger mutual information values are selected as key features. These key features have a greater impact on the electricity carbon emission factor and can more accurately reflect the carbon emission changes during the power generation process. Therefore, the mutual information algorithm provides a scientific and effective method for screening key features. By screening out key features, the complexity of the calculation model can be reduced, and the calculation efficiency and accuracy of the model can be improved. At the same time, the main factors affecting the electricity carbon emission factor can be grasped more precisely, making the generation of dynamic electricity carbon emission factors more scientific and reasonable.

[0064] Based on these data and features, constructing a dynamic electricity carbon emission factor calculation model specifically includes:

[0065] To reflect the influence degree of different factors on the electricity carbon emission factor, a weighted summation method is used to construct the calculation model, and the formula is:

[0066]

[0067] where C is the dynamic electricity carbon emission factor, w i is the weight of each influencing factor, and x i is the data value of each influencing factor.

[0068] Specifically, the specific method of constructing the dynamic electricity carbon emission factor calculation model is described. To reflect the influence degree of different factors on the electricity carbon emission factor, a weighted summation method is used to construct the calculation model. Each influencing factor is assigned a weight, and the magnitude of the weight represents the influence degree of the factor on the electricity carbon emission factor. The data values of each influencing factor are specific values obtained from the collected data and external environmental data. By multiplying the data values of each influencing factor by the corresponding weights and then summing them up, the dynamic electricity carbon emission factor is obtained. The weighted summation calculation model can comprehensively consider the influence of different factors on the electricity carbon emission factor, and through the setting of weights, the influence degree of each factor can be flexibly adjusted. This model is simple and easy to understand, convenient to calculate, and can adjust the weights and factors according to different power systems and environmental conditions, improving the adaptability and accuracy of the dynamic electricity carbon emission factor.

[0069] Based on the dynamic electricity carbon emission factor, calculating the total carbon emission of the power system specifically includes:

[0070] Obtain the electricity quantity data of each link of the power system;

[0071] Multiply the dynamic electricity carbon emission factor by the electricity quantity data of the corresponding link to obtain the carbon emissions of each link;

[0072] Accumulate the carbon emissions of each link to obtain the total carbon emissions of the power system.

[0073] Specifically, the specific steps for calculating the total carbon emissions of the power system based on the dynamic electricity carbon emission factor are described. First, obtain the electricity quantity data of each link in the power system. These data can be collected through the sensor nodes in the monitoring network and reflect the actual electricity consumption or generation of each link. Then, multiply the dynamic electricity carbon emission factor by the electricity quantity data of the corresponding link to obtain the carbon emissions of each link. Since the dynamic electricity carbon emission factor represents the carbon emissions per unit of electricity, the actual carbon emissions of each link can be obtained through multiplication. Finally, accumulate the carbon emissions of each link to obtain the total carbon emissions of the power system. This calculation method is simple and intuitive, and can accurately calculate the total carbon emissions of each link and the whole of the power system. By calculating the carbon emissions of each link separately, the carbon emission contributions of different links in the power system can be clearly understood, providing a basis for power enterprises to formulate targeted energy conservation and emission reduction measures.

[0074] The calculation formula for the total carbon emissions of the power system is as follows:

[0075] E j = C × Q j

[0076]

[0077] where E j is the carbon emissions of the j-th link, Q j is the electricity quantity of the j-th link, and E is the total carbon emissions of the power system.

[0078] Specifically, the calculation formula for the total carbon emissions of the power system is given. In the formula, the carbon emissions of the j-th link are obtained by multiplying the electricity quantity of this link by the dynamic electricity carbon emission factor, which is consistent with the method for calculating the carbon emissions of each link described in claim 6. Then, the carbon emissions of each link are accumulated to obtain the total carbon emissions of the power system. The clear calculation formula provides a standardized method for calculating the total carbon emissions of the power system, making the calculation process more accurate and reliable. At the same time, the formula is expressed simply and clearly, facilitating practical application and promotion.

[0079] The real-time monitoring of the carbon emission calculation results and the feedback adjustment of the calculation model and parameters specifically include:

[0080] Compare the carbon emission calculation results with the preset standards;

[0081] Adjust the parameters of the calculation model using a feedback control algorithm according to the comparison result;

[0082] Optimize the calculation model of the dynamic electricity carbon emission factor.

[0083] Specifically, the process of real-time monitoring of the carbon emission calculation results and feedback adjustment of the calculation model and parameters is described. First, compare the carbon emission calculation results with a preset standard. The preset standard can be a national or local carbon emission index, or an energy conservation and emission reduction goal set by the power enterprise itself. Through the comparison, the gap between the calculation result and the standard can be found, and it can be judged whether the carbon emission of the power system meets the requirements. Then, adjust the parameters of the calculation model using a feedback control algorithm according to the comparison result. The feedback control algorithm can automatically adjust the parameters of the model according to the deviation situation, making the calculation result closer to the preset standard. Finally, optimize the calculation model of the dynamic electricity carbon emission factor. By continuously adjusting and optimizing the model, the accuracy and adaptability of the model can be improved.

[0084] The feedback control algorithm adopts a PID control algorithm, and its control law is:

[0085]

[0086] where u(t) is the output of the controller, e(t) is the deviation signal of the system, and K p , K i , K d are the proportional coefficient, integral coefficient, and differential coefficient respectively.

[0087] Specifically, it is clarified that the feedback control algorithm adopts a PID control algorithm. The PID control algorithm is a classic control algorithm that processes the deviation signal of the system through three links: proportional, integral, and differential, and outputs a control signal. In this method, the deviation signal of the system is the difference between the carbon emission calculation result and the preset standard. The proportional coefficient determines the response speed of the controller to the deviation signal, the integral coefficient is used to eliminate the steady-state error of the system, and the differential coefficient can predict the change trend of the deviation signal and make adjustments in advance. By adjusting these three coefficients, the output of the controller can more accurately control the parameters of the calculation model and optimize the calculation model of the dynamic electricity carbon emission factor.

[0088] After adjusting the parameters of the calculation model and optimizing the calculation model, recalculate the dynamic electricity carbon emission factor and the total carbon emission of the power system to form a closed-loop optimization control process.

[0089] Specifically, it is described that after adjusting the calculation model parameters and optimizing the calculation model, the dynamic electricity carbon emission factor and the total carbon emission of the power system are recalculated to form a closed-loop optimization control process. After completing one feedback adjustment, according to the new model parameters and the optimized model, the dynamic electricity carbon emission factor and the total carbon emission of the power system are recalculated. Then, the calculation results are compared with the preset standards again. If there are still deviations, the feedback adjustment continues until the calculation results meet the preset standards. The closed-loop optimization control process enables the carbon emission measurement method of the power system to continuously adapt to the dynamic changes of the power system and maintain the accuracy and reliability of the measurement results. Through continuous feedback adjustment and recalculation, errors in the model can be detected and corrected in a timely manner, improving the performance and adaptability of the model. At the same time, it helps power enterprises achieve continuous energy conservation and emission reduction and promotes the green development of the power industry.

[0090] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A method for measuring carbon emissions in a power system based on dynamic electricity carbon emission factors, characterized in that, Including the following steps: Construct a monitoring network with multiple types of sensors to collect operation data of each link in the power system; Based on the collected data and external environment data, use algorithms to generate dynamic electricity carbon emission factors; Based on the dynamic electricity carbon emission factors, calculate the total carbon emissions of the power system; Monitor the carbon emission calculation results in real time, and feedback to adjust the calculation model and parameters.

2. The power system carbon emission measurement method based on dynamic electricity carbon emission factors according to claim 1, wherein The specific steps of constructing a monitoring network with multiple types of sensors to collect operation data of each link in the power system include: The sensor nodes and the data aggregation unit are connected through a wired or wireless communication network; Different types of sensor nodes are distributed in the power generation, transmission, distribution, and power consumption links; The data aggregation unit performs preliminary processing and storage on the data transmitted by the sensor nodes.

3. The power system carbon emission measurement method based on dynamic electricity carbon emission factors according to claim 1, wherein, The specific steps of generating dynamic electricity carbon emission factors based on the collected data and external environment data using algorithms include: The external environment data covers regional energy structure, time, and meteorological data; Adopt data mining technology to screen out the key features affecting the electricity carbon emission factors from the collected data and external environment data; Based on these data and features, construct a calculation model for dynamic electricity carbon emission factors.

4. The power system carbon emission measurement method based on dynamic electricity carbon emission factors according to claim 3, characterized in that The specific method of adopting data mining technology to screen out the key features affecting the electricity carbon emission factors from the collected data and external environment data is to use the mutual information algorithm, and the mutual information calculation formula is: Where I(X;Y) is the mutual information between variables X and Y, p(x,y) is the joint probability distribution of X and Y, and p(x) and p(y) are the marginal probability distributions of X and Y respectively.

5. The method for measuring carbon emissions of a power system based on a dynamic electricity carbon emission factor according to claim 3, wherein The specific steps of constructing a calculation model for dynamic electricity carbon emission factors based on these data and features include: To reflect the influence degree of different factors on the electricity carbon emission factors, a weighted summation method is adopted to construct the calculation model, and the formula is: Among them, C is the dynamic electricity carbon emission factor, w i is the weight of each influencing factor, and x i is the data value of each influencing factor.

6. The method for measuring carbon emissions of a power system based on a dynamic electricity carbon emission factor according to claim 1, wherein The specific steps of calculating the total carbon emissions of the power system based on the dynamic electricity carbon emission factors include: Obtain the electricity quantity data of each link in the power system; Multiply the dynamic electricity carbon emission factors by the electricity quantity data of the corresponding links to obtain the carbon emissions of each link; Accumulate the carbon emissions of each link to obtain the total carbon emissions of the power system.

7. The power system carbon emission measurement method based on dynamic electricity carbon emission factors according to claim 6, characterized in that The calculation formula for the total carbon emissions of the power system is as follows: E j = C × Q j Among them, E j is the carbon emission of the j-th link, Q j is the electricity consumption of the j-th link, and E is the total carbon emission of the power system.

8. The power system carbon emission measurement method based on dynamic electricity carbon emission factors according to claim 1, characterized in that The specific steps of monitoring the carbon emission calculation results in real time and feedback adjusting the calculation model and parameters include: Compare the carbon emission calculation results with the preset standards; According to the comparison results, use the feedback control algorithm to adjust the parameters of the calculation model; Optimize the calculation model of the dynamic electricity carbon emission factors.

9. The power system carbon emission measurement method based on dynamic electricity carbon emission factors according to claim 8, wherein, The feedback control algorithm adopts the PID control algorithm, and its control law is: where u(t) is the output of the controller, e(t) is the deviation signal of the system, and K p , K i , K d are the proportional coefficient, integral coefficient, and derivative coefficient, respectively.

10. The power system carbon emission measurement method based on dynamic electricity carbon emission factors according to claim 1, wherein, After adjusting the calculation model parameters and optimizing the calculation model, recalculate the dynamic electricity carbon emission factors and the total carbon emissions of the power system to form a closed-loop optimization control process.

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