Electric power ecological big data collection and storage database for thermal power industry

Abstract

The invention discloses an electric power ecological big data collection and storage database for the thermal power industry, and belongs to the technical field of thermal power big data storage. The method is realized through two steps of constructing an industry electricity carbon model and constructing a provincial electricity carbon model. According to the method, annual and monthly carbon emission monitoring of the industry is realized on the basis of a measurement result of an electricity-carbon analysis model in combination with the characteristics of an energy structure, a production process and the like of thermal power generation, and carbon emission prediction can be performed on the industry by differentially collecting the total carbon emission amount, the carbon emission intensity, the carbon emission structure and the carbon emission change trend analysis over the years; the method is convenient for upgrading industrial equipment, assists in improving the industrial carbon emission monitoring precision, further improves the clean, efficient and flexible level of a coal power unit, and promotes the clean and low-carbon transformation of the power industry. According to the invention, carbon emission monitoring in the whole province is further realized based on a model measurement result of an electricity-carbon analysis model in the thermal power generation industry.

Description

Technical Field

[0001] The present invention relates to the field of thermal power big data storage technology, and more specifically, to a database for collecting and storing electric power ecological big data for the thermal power industry. Background Art

[0002] At present, the forest carbon sink is calculated by calculating the carbon storage of the forest at different time points, which indirectly describes the changes in forest carbon sink. The sample site survey method, model construction method, and remote sensing monitoring method are mostly based on this principle. Micrometeorology is the only method that can directly measure the carbon sink of the ecosystem.

[0003] Traditional carbon emission monitoring methods mainly rely on self-reported data from enterprises or offline calculations, which have problems such as untimely data collection and low accuracy. With the development of big data technology, it is possible to use big data platforms to conduct real-time monitoring and analysis of carbon emissions in the power industry. The power ecological monitoring system based on big data can adopt more refined and dynamic methods to improve the accuracy and timeliness of carbon emission monitoring.

[0004] The electric carbon analysis model is a commonly used tool in the industry. It estimates carbon emissions by combining energy consumption data of thermal power generation, carbon emission factors in the power generation process, and information such as the power transmission network. By building industry electric carbon models and provincial electric carbon models, a comprehensive analysis of carbon emissions in different regions and industries can be achieved. This not only helps the government formulate reasonable emission reduction policies, but also helps companies optimize production processes and reduce carbon emissions. Summary of the invention

[0005] 1. Technical problems to be solved

[0006] In response to the problems existing in the prior art, the purpose of the present invention is to provide a database for collecting and storing power ecological big data for the thermal power industry, which can improve the accuracy of carbon emission monitoring and promote the clean and low-carbon transformation of the power industry.

[0007] 2. Technical solution

[0008] To solve the above problems, the present invention adopts the following technical solutions.

[0009] A power ecological big data collection and storage database for the thermal power industry includes the following steps:

[0010] 1) Constructing an industry electricity carbon model: Based on the energy consumption data of thermal power generation in each province, the inter-provincial power high-order transmission data, and the total power inflow and power consumption data of each province, combined with the network analysis method and the inter-provincial high-order power transmission model, the indirect carbon emission factors of electricity in each industry in the region are calculated, and the electricity carbon model of each industry in the province is constructed;

[0011] 2) Construct a provincial electricity-carbon model: Based on the industry electricity-carbon model in step 1), construct a provincial electricity-carbon model.

[0012] According to the above characteristics, a specific province is set, which has n industries and monitors carbon emissions. The formula is: ecc m It represents the electricity-CO2 conversion coefficient of industry m in the province, and zm represents the electricity consumption of department m in the province.

[0013] In some embodiments, the electricity-CO2 conversion coefficient ecc m The estimation includes the following steps:

[0014] 1) Calculate the historical electricity-CO2 conversion coefficient;

[0015] 2) An econometric regression model was constructed to predict future coefficients.

[0016] According to the above characteristics, the formula of the electricity-CO2 conversion coefficient is as follows: Non-power-related CO2 emissions by sector It can be calculated by the following formula:

[0017]

[0018] In some embodiments, the electricity-related CO2 emissions of industry m in region i can be calculated by the following formula:

[0019]

[0020] The CO2 emission factor for electricity consumption in region i can be calculated as the total electricity-related CO2 emissions divided by the total electricity consumption,

[0021]

[0022] According to the above characteristics, the matrix E C The estimation is done by the grid method, which includes the following steps:

[0023] 1) Construct a diagonal matrix of carbon dioxide emissions from power generation in each region;

[0024] 2) Construct a power generation and consumption matrix (H) to link power generation and consumption in different regions through the transmission network;

[0025] 3) Calculate the specific emission flow matrix E C , the matrix H converts the carbon dioxide emissions generated by power generation into and consumption Contacted:

[0026]

[0027] 3. Beneficial effects

[0028] Compared with the prior art, the advantages of the present invention are:

[0029] 1) Based on the calculation results of the "electricity-carbon analysis model", the present invention combines the energy structure, production process and other characteristics of thermal power generation to realize annual and monthly carbon emissions monitoring of the industry. By differentially collecting the total carbon emissions, carbon emission intensity, carbon emission structure and carbon emission change trend analysis over the years, the industry can be predicted to achieve carbon emissions, so as to facilitate equipment upgrades in the industry, assist in improving the accuracy of carbon emission monitoring in different industries, further improve the clean, efficient and flexible level of coal-fired power units, and promote the clean and low-carbon transformation of the power industry.

[0030] 2) Based on the calculation results of the "electricity-carbon analysis model" in the thermal power generation industry, the present invention further realizes carbon emission monitoring across the province. By "measuring carbon by electricity", combined with the total carbon emissions of enterprises, carbon emission intensity, carbon emission structure, and carbon emission change trend analysis, and verified and compared with the electricity-carbon model data results, the accuracy of carbon emission monitoring is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of a database for collecting and storing electric power ecological big data for the thermal power industry according to the present invention. DETAILED DESCRIPTION

[0032] The technical solutions 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; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0033] Example

[0034] A power ecological big data collection and storage database for the thermal power industry includes the following steps:

[0035] 1) Constructing an industry electricity carbon model: Based on the energy consumption data of thermal power generation in each province, the inter-provincial power high-order transmission data, and the total power inflow and power consumption data of each province, combined with the network analysis method and the inter-provincial high-order power transmission model, the indirect carbon emission factors of electricity in each industry in the region are calculated, and the electricity carbon model of each industry in the province is constructed;

[0036] 2) Construct a provincial electricity-carbon model: Based on the industry electricity-carbon model in step 1), construct a provincial electricity-carbon model.

[0037] According to the above characteristics, a specific province is set, which has n industries and monitors carbon emissions. The formula is: Denoted as formula 1, ecc m It represents the electricity-CO2 conversion coefficient of industry m in the province, and zm represents the electricity consumption of department m in the province.

[0038] In some embodiments, the electricity-CO2 conversion coefficient ecc m The estimation includes the following steps:

[0039] 1) Calculate the historical electricity-CO2 conversion coefficient;

[0040] 2) An econometric regression model was constructed to predict future coefficients.

[0041] According to the above characteristics, the formula of the electricity-CO2 conversion coefficient is as follows: Denoted as formula 2.

[0042] in are CO2 emissions not related to electricity (emissions from energy consumption and industrial processes); is the CO2 emissions associated with electricity (including CO2 emissions from self-generated electricity and inflowing electricity). and It can reflect the emission characteristics of fossil fuel combustion and electricity consumption in different sectors. m is the electricity consumption of sector m in a certain region. Finally, the electricity-CO2 conversion coefficient in Eq. is adopted. This formula is used to establish the correlation between energy consumption, carbon emissions and electricity consumption, reflecting the different carbon emission characteristics of sectors.

[0043] Using the IPCC inventory methodology, non-power-related CO2 emissions from the sector It can be calculated by the following formula:

[0044] Denoted as formula 3.

[0045] where cq mk represents the consumption of the kth fossil fuel in industry m; cef k represents the CO2 emission factor of the kth fossil fuel; cq mc Indicates cement production, cef ce Indicates CO2 emissions per unit of cement production.

[0046] The CO2 emission factors of various types of fossil fuels can be calculated using the following formula:

[0047] Denoted as formula 4.

[0048] where v k refers to the net calorific value of fossil fuel k; f k is the carbon content coefficient of fossil fuel k; o k represents the oxidation ratio of the fossil fuel K; 44 / 12 is a constant that converts carbon to CO2.

[0049] In some embodiments, the electricity-related CO2 emissions of industry m in region i can be calculated by the following formula:

[0050] Denoted as formula 5;

[0051] in represents the CO2 emission factor of electricity consumed in region i; z im Represents the electricity consumption of industry m in region i.

[0052] The CO2 emission factor of electricity consumption in region i can be calculated as the total electricity-related CO2 emissions divided by the total electricity consumption. More importantly, the total electricity-related CO2 emissions of a specific region are the sum of the emissions contained in the electricity consumed in each region, as expressed in Equation 6.

[0053]

[0054] where ef c is a row vector whose elements represents the CO2 emission factor of electricity consumed by region i; E C is a matrix containing carbon emissions, whose (i, j)th element represents the carbon emissions embodied in electricity coming from region i but consumed by region j; is a diagonal matrix, c i represents the power consumption of area i.

[0055] According to the above characteristics, the matrix E C The estimation is done by the grid method, which includes the following steps:

[0056] 1) Construct a diagonal matrix of CO2 emissions from power generation in each region; CO2 emissions from thermal power generation in region i It can be obtained by the following formula: Denoted as formula 7.

[0057] where cq ik Indicates the usage amount of fuel k used for thermal power generation in area i.

[0058] 2) Construct a power generation and consumption matrix (H) to link power generation and consumption in different regions through the transmission network; in the grid method, each grid can be regarded as a node, and power transmission between different grids can be regarded as an edge. In the present invention, each province in China can be regarded as a grid or a node, and the total inflow and outflow of each node in the transmission network is determined by the current between different provinces. The sum of local power generation and direct inflow is equal to the sum of local power consumption and direct outflow.

[0059] Assuming that there are n regions in the present invention, the total current of one region can be expressed as:

[0060]

[0061] It is recorded as formula 8, where x i represents the total current in region i; p i represents the power generation of region i; T ij represents the amount of electricity transferred from region i to region j; c i is the electricity consumption of region i.

[0062] The power transmission between regions is expressed by an n×n matrix T:

[0063]

[0064] Based on the total power inflow or outflow of each region, the direct outflow matrix B can be defined as: denoted as Equation 10;

[0065]

[0066] In the formula, the (i, j)th element It is the proportion of power transmitted from region i to region j in the total current of region i.

[0067]

[0068] It can be converted into a matrix equation, expressed as Equation 11;

[0069]

[0070] where p is a 1×n vector representing the power generation of each region.

[0071] It can be further rewritten as the following equation:

[0072]

[0073] Where I is an identity matrix; G = (IB) -1 =I+B+B 2+B3+… is the total outflow coefficient matrix, whose element g ij represents the ratio of electricity transmitted from region i to region j in the total power generation of region i; B represents the direct transmission of electricity to other regions without passing through the transit region; B 2 is the inter-regional transmission of electricity through a transit area; B 3 represents the power transmitted through two transit regions, and so on. Represents the total amount of power flowing into zone j.

[0074] The electricity production-consumption matrix H, which links the power generation and consumption in different regions, is expressed as Equation 13;

[0075]

[0076] Among them, the (i, j)th element H ij =g ij *c j / x j It represents the ratio of unit power generated in area i to that consumed by area j.

[0077] 3) Calculate the specific emission flow matrix E c , the matrix H converts the carbon dioxide emissions generated by power generation into and consumption Connected: recorded as formula 14;

[0078]

[0079]

[0080] in represents the CO2 emissions involved in the transmission of electricity from region i to region j.

[0081] Then, the CO2 emission factor of electricity consumption in region i can be obtained by Equation 6 In addition, electricity-related carbon emissions from the industry The electricity consumption of the industry can be calculated by m and its CO2 emission factor The total carbon dioxide emissions of industry m are non-electricity related carbon emissions. and electricity-related emissions To avoid double counting, is to redistribute carbon emissions from power generation to all sectors based on electricity consumption, which means Emissions from thermal power generation will not be included. Finally, the electricity-CO2 conversion coefficient (ecc) of industry m in region i can be calculated by Equation 2 m ).

[0082] Based on the historical values ​​of the electricity-CO2 conversion coefficient, the electricity-CO2 conversion coefficient (ecc m )’s p-order autoregressive AR(p) model is as follows:

[0083]

[0084] Finally, the carbon emissions of provinces and regions are obtained through Equation 1.

[0085] Aiming at the indicator calculation requirements of ecological big data, the present invention uses web crawler technology to realize the automatic acquisition of network big data, and establishes a unified data storage format and database management system; through data cleaning and preprocessing operations, the data obtained from different websites are normalized; data verification and quality control are carried out, and data fusion and grid calculation are performed on multi-source network big data. Finally, the spatiotemporal conversion of point data and surface data is realized, and the data is stored and managed in a unified spatiotemporal framework to form a network ecological big data set.

[0086] On the basis of the existing remote sensing information fusion methods, in response to the needs of quantitative evaluation of specific ecological indicators, multi-source homogeneous remote sensing data are fused at the data level to obtain spatiotemporal data with low redundancy and high quality (time, spectrum, and spatial resolution), providing a multi-level and rich data foundation for feature-level fusion and decision-level fusion; multi-source heterogeneous remote sensing data are fused at the feature level to obtain spatiotemporal feature data directly related to decision analysis; multi-source heterogeneous remote sensing data are fused at the decision level to ultimately form relevant model methods and remote sensing products with high precision, high frequency, and high spatial resolution.

[0087] Based on the calculation results of the "Electricity-Carbon Analysis Model", combined with the energy structure, production process and other characteristics of thermal power generation, annual and monthly carbon emissions monitoring of the industry is achieved. Through differentiated collection of historical carbon emissions, carbon emission intensity, carbon emission structure, and carbon emission change trend analysis, carbon emissions forecasts can be made for the industry to facilitate equipment upgrades in the industry, assist in improving the accuracy of carbon emissions monitoring in different industries, further improve the clean, efficient and flexible level of coal-fired power units, and promote the clean and low-carbon transformation of the power industry.

[0088] Based on the calculation results of the "electricity-carbon analysis model" in the thermal power generation industry, we will further realize carbon emission monitoring across the province. By "measuring carbon with electricity", combined with the analysis of the total carbon emissions, carbon emission intensity, carbon emission structure, and carbon emission change trends of enterprises, and verifying and comparing with the electricity-carbon model data results, we will improve the accuracy of carbon emission monitoring.

[0089] The above is only a preferred specific implementation of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved conception within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A database for collecting and storing big data on power ecology in the thermal power industry, characterized in that The steps include: 1) Constructing an industry electricity carbon model: Based on the energy consumption data of thermal power generation in each province, the inter-provincial power high-order transmission data, and the total power inflow and power consumption data of each province, combined with the network analysis method and the inter-provincial high-order power transmission model, the indirect carbon emission factors of electricity in each industry in the region are calculated, and the electricity carbon model of each industry in the province is constructed; 2) Construct a provincial electricity-carbon model: Based on the industry electricity-carbon model in step 1), construct a provincial electricity-carbon model.

2. According to claim 1, a power ecological big data collection and storage database for the thermal power industry is characterized in that Assume a specific province with n industries to monitor carbon emissions. The formula is: ecc m It represents the electricity-CO2 conversion coefficient of industry m in the province, and zm represents the electricity consumption of department m in the province.

3. The electric power ecological big data collection and storage database for the thermal power industry according to claim 2 is characterized in that The electricity-CO2 conversion factor ecc m The estimation includes the following steps: 1) Calculate the historical electricity-CO2 conversion coefficient; 2) An econometric regression model was constructed to predict future coefficients.

4. The electric power ecological big data collection and storage database for the thermal power industry according to claim 3 is characterized in that The formula for the electricity-CO2 conversion factor is as follows: Non-power-related CO2 emissions by sector It can be calculated by the following formula:

5. The electric power ecological big data collection and storage database for the thermal power industry according to claim 2 is characterized in that The electricity-related CO2 emissions of industry m in region i can be calculated using the following formula: The CO2 emission factor for electricity consumption in region i can be calculated as the total electricity-related CO2 emissions divided by the total electricity consumption, 6. The electric power ecological big data collection and storage database for the thermal power industry according to claim 5 is characterized in that Matrix E C The estimation is done by the grid method, which includes the following steps: 1) Construct a diagonal matrix of carbon dioxide emissions from power generation in each region; 2) Construct a power generation and consumption matrix (H) to link power generation and consumption in different regions through the transmission network; 3) Calculate the specific emission flow matrix E C , the matrix H converts the carbon dioxide emissions generated by power generation into and consumption Contacted:

Images