CN117611190A - Regional power system carbon emission measuring and calculating method, device and medium - Google Patents
Regional power system carbon emission measuring and calculating method, device and medium Download PDFInfo
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Abstract
The invention discloses a regional power system carbon emission measuring and calculating method, a regional power system carbon emission measuring and calculating device and a regional power system carbon emission measuring and calculating medium. The method comprises the following steps: acquiring relevant data of carbon emission measurement and analysis in key industries and areas, screening available data, complementing missing data and detecting abnormal values from the relevant data by adopting a LOF and KNN data preprocessing method, and determining effective relevant data; determining regional carbon emission influencing factors, and calculating contribution degrees of the regional carbon emission influencing factors by using an exponential decomposition method; determining the association relationship between regional carbon emission influencing factors and main economic activities; constructing a regional power system carbon emission measuring and calculating analysis model; the electricity consumption data of each industry and each region in the current month is input into a regional power system carbon emission measuring, calculating and analyzing model to obtain the production process data and the energy activity level data of each industry and each region in the current month, and then the carbon discharge capacity of each industry and each region in the current month is calculated by adopting factors provided by IPCC.
Description
Technical Field
The invention relates to the technical field of low-carbon power, in particular to a method, a device and a medium for measuring and calculating carbon emission of a regional power system.
Background
In the industrialization stage of China, the energy power demand is continuously increased, the economic development and the carbon emission still have strong coupling relation, the power industry is used as the main force army of carbon emission reduction, the research on the association relation between the carbon emission and the economy of a power system is urgently needed, the carbon emission condition of the power system is analyzed from the systematic view, and the double-control target decomposition and realization of the carbon emission in the power-assisted areas and countries are realized.
The carbon emission energy source in the power industry is various, the consumption path is complex, the carbon distribution of each power generation and utilization enterprise is unclear, and the carbon emission reduction target is ambiguous, so that accurate and reliable carbon emission statistics and accurate decomposition of the carbon emission reduction target are difficult to realize. In addition, in the development of the power system, along with the access of renewable energy sources, the power system presents the supply-demand balance between the randomly fluctuating load and the randomly fluctuating power supply due to the inherent intermittent and fluctuating output characteristics of the renewable energy sources such as distributed photovoltaic, energy storage and the like, the structural morphology is radically changed, and the carbon metering characteristics and the lack of methods of the power system are considered, so that the regional power system carbon emission measurement and calculation analysis based on the systematic visual angle lacks data support. In conclusion, the problems of lack of basic data of carbon emission, insufficient system and carbon emission measuring and analyzing capability, lack of decomposition basis of carbon emission 'double-control' targets and the like of the regional power system at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a regional power system carbon emission measuring and calculating method, a regional power system carbon emission measuring and calculating device and a regional power system carbon emission measuring and calculating medium.
According to an aspect of the present invention, there is provided a regional power system carbon emission measurement method including:
acquiring relevant data of carbon emission measurement and analysis in key industries and areas, screening available data, complementing missing data and detecting abnormal values from the relevant data by adopting a LOF and KNN data preprocessing method, and determining effective relevant data;
determining regional carbon emission influence factors according to the effective related data, and calculating contribution degrees of the regional carbon emission influence factors by using an exponential decomposition method;
according to the effective related data, determining the association relation between regional carbon emission influencing factors and main economic activities;
according to the contribution degree of regional carbon emission influencing factors and the association relation between the regional carbon emission influencing factors and main economic activities, constructing a regional power system carbon emission measuring and calculating analysis model;
the electricity consumption data of each industry and each region in the current month is input into a regional power system carbon emission measuring, calculating and analyzing model to obtain the production process data and the energy activity level data of each industry and each region in the current month, and then the carbon discharge capacity of each industry and each region in the current month is calculated by adopting factors provided by IPCC.
Optionally, screening available data, complementing missing data and detecting abnormal values from the power data by adopting a data preprocessing method of LOF and KNN, and determining effective power data, including:
performing anomaly detection on the power data based on an LOF algorithm, and determining all detected anomaly values of the power data;
and carrying out data complementation on the detected abnormal value based on KNN, and determining effective power data.
Optionally, the regional carbon emission influencing factors include:
technical factors, including: fuel parameters, unit parameters, and system parameters;
economic factors, including: influence of macro economic development situation on primary/secondary energy supply and demand relation; influence of multi-field market mechanism design, price level and micro-trading strategy on market competitiveness of different manufacturers;
environmental factors, including: climate, weather, and power system environment exteriors.
Optionally, the contribution calculation formula is:
wherein F represents an object to be studied and decomposed, such as an index of carbon emission intensity, carbon emission, energy consumption, energy intensity, etc., X 1i X 2i …X ni N factors affecting F are expressed, i is an index of different industry categories, different energy varieties or different regions.
Optionally, determining the association relationship between the regional carbon emission influencing factors and the main economic activity according to the effective related data comprises:
Generating a comparison sequence of the carbon emission of the power system according to the effective related data;
generating a reference data sequence of the operation business of the power system according to the effective related data;
determining the degree of association between the carbon emission of the power system and the operation business of the power system according to the comparison sequence and the reference book sequence;
and determining a logic relationship according to the association degree.
According to another aspect of the present invention, there is provided a regional power system carbon emission measurement apparatus comprising:
the acquisition module is used for acquiring relevant data of carbon emission measurement and analysis in key industries and areas, screening available data from the relevant data by adopting a LOF and KNN data preprocessing method, complementing missing data and detecting abnormal values, and determining effective relevant data;
the first calculation module is used for determining regional carbon emission influence factors according to the effective related data and calculating the contribution degree of the regional carbon emission influence factors by using an exponential decomposition method;
the determining module is used for determining the association relation between the regional carbon emission influence factors and the main economic activities according to the effective related data;
the construction module is used for constructing a regional power system carbon emission measurement and analysis model according to the contribution degree of regional carbon emission influence factors and the association relation between the regional carbon emission influence factors and main economic activities;
The second calculation module is used for inputting the current electricity consumption data of each industry and each region into a regional power system carbon emission measuring and calculating analysis model to obtain the production process and energy activity level data of each industry and each region in the current month, and calculating the carbon discharge of each industry and each region in the current month by adopting factors provided by IPCC.
According to a further aspect of the present invention there is provided a computer readable storage medium storing a computer program for performing the method according to any one of the above aspects of the present invention.
According to still another aspect of the present invention, there is provided an electronic device including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any of the above aspects of the present invention.
According to the method, the influence factors of the regional power system carbon emission and the reasons for the influence factors are analyzed, the important factors such as emission reduction expectation, policy expectation and economic development are comprehensively considered, the key boundary conditions are built by combining the association relation between the power system carbon emission and the main economic activity indexes, the electric-carbon emission measuring and calculating method based on historical electric quantity data, energy consumption data and product yield data is provided, the regional power system carbon emission measurement and calculation is realized, the influence factor contribution degree of the regional power system carbon emission is obtained by adopting a factor decomposition analysis method, and the influence factors with larger influence on the regional power system carbon emission in each link are excavated.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flow chart of a method for measuring and calculating regional power system carbon emission according to an exemplary embodiment of the present invention;
FIG. 2 is a schematic diagram of a cleaning process using LOF and KNN based carbon emission related data in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for calculating primary economic activity index type and relationship with carbon emissions of an electrical power system for a region provided by an exemplary embodiment of the present invention;
fig. 4 is a schematic structural view of a regional power system carbon emission measurement device according to an exemplary embodiment of the present invention;
fig. 5 is a structure of an electronic device provided in an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present invention are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present invention, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in an embodiment of the invention may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in the present invention is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations with electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with the terminal device, computer system, server, or other electronic device include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the foregoing, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Exemplary method
Fig. 1 is a flowchart of a method for measuring and calculating carbon emission of a regional power system according to an exemplary embodiment of the present invention. The embodiment can be applied to an electronic device, as shown in fig. 1, the regional power system carbon emission measurement method 100 includes the following steps:
step 101, acquiring relevant data of carbon emission measurement and analysis in key industries and areas, screening available data, complementing missing data and detecting abnormal values from the relevant data by adopting a LOF and KNN data preprocessing method, and determining effective relevant data;
102, determining regional carbon emission influence factors according to effective related data, and calculating contribution degrees of the regional carbon emission influence factors by using an exponential decomposition method;
step 103, determining the association relation between regional carbon emission influencing factors and main economic activities according to the effective related data;
step 104, constructing a regional power system carbon emission measurement and analysis model according to the contribution degree of regional carbon emission influence factors and the association relation between the regional carbon emission influence factors and main economic activities;
and 105, inputting the current electricity consumption data of each industry and each region into a regional power system carbon emission measuring and calculating analysis model to obtain the production process level and the energy activity level data of each industry and each region in the current month, and calculating the carbon discharge of each industry and each region in the current month by adopting factors provided by IPCC.
Specifically, the invention provides an electric-carbon emission measuring and calculating method based on historical electric quantity data, energy consumption data and product yield data by analyzing influence factors (such as energy structure, industrial structure, energy consumption and the like) of regional power system carbon emission and reasons for generation of the influence factors, comprehensively considering important factors such as emission reduction expectation, policy expectation, economic development and the like, constructing key boundary conditions by combining the association relation between the power system carbon emission and main economic activity indexes, realizing regional power system carbon emission measuring and calculating, acquiring influence factor contribution degree of regional power system carbon emission by adopting a factor decomposition analysis method, and excavating influence factors with larger influence on the regional power system carbon emission of each link.
The invention can more practically and effectively master the carbon emission condition and trend of the area based on the systematic view angle and the analysis of the influence factors of the regional power system carbon emission measurement and calculation, thereby providing reliable technical support for the low-carbon emission reduction co-development. The research on the measurement and calculation of the carbon emission and the influencing factors has important theoretical and practical significance for promoting the green town development of China, realizing energy conservation and emission reduction, promoting the sustainable development of low-carbon economy and coping with international climate negotiations. The method can be applied to the power carbon emission measurement work under the background of double carbon, provides basis for the carbon emission control target of the power industry, effectively realizes the carbon reduction task of the power system, corresponds to the scenes of government release of power system carbon emission measurement and emission reduction effect analysis and the like, has foresight, and can also provide technical support for the work of carbon emission measurement, management, policy formulation and the like of related departments.
Aiming at the defects of regional power system carbon emission measurement and analysis, the invention provides a regional power system carbon emission measurement and analysis method based on a systematic visual angle and an influence factor analysis method thereof.
In summary, the key boundary conditions based on the important factors of the systematic visual angle can be obtained through the steps, the systematic regional power system carbon emission analysis model is formed, the regional power system carbon emission intensity measurement and calculation and the energy-saving and carbon-reduction scheme evaluation are realized, and a feasible reference is provided for the power control method in the low-carbon background.
Study area carbon emission analysis related data acquisition and data pretreatment:
on the basis of guaranteeing the quality of acquired data, association relations and interaction mechanisms among data in different fields are required to be clarified, mathematical models of carbon emission and various data are constructed, and system boundaries and related processing methods are defined. For primary data of multi-source heterogeneous characteristics, adopting methods such as statistical analysis, source analysis, causal analysis and the like to extract high-efficiency secondary data resources and building a dynamic interactive simulation environment. The dynamic simulation environment supports a mathematical model and a multi-agent model, can reflect a small number of special behaviors and irrational behaviors, and is convenient for effectively and dynamically evaluating the evolution situation of the carbon emission of the power system under the influence of uncertainty factors of the complex internal and external environments.
Because the data of the carbon emission related power system can face the conditions of abnormal data transmission, insufficient data storage, system maintenance and the like in the actual operation process, the problems of abnormality, deletion and the like of the characteristic data of the carbon emission related power system can exist, and the accuracy of the analysis result of the urban energy power carbon emission is seriously influenced.
Because the carbon emission data can have the data missing condition in the monitoring and acquiring process, the invention adopts the KNN method to carry out data complementation, searches K data points which are closest to the data points to be complemented in space, and takes the average value of K adjacent data points as the data points to be complemented to acquire the complement value. For a data missing point o' of any operation environment of the power system, a calculation formula of the KNN-based data complement method is as follows:
wherein o is the deletion and completion of the abnormal data by using a local outlier detection (Local outlier factor, LOF) algorithm and a K-nearest neighbor (KNN) based data completion algorithm, respectively, based on the obtained related power data. LOF is a density-based outlier detection algorithm that characterizes the outlier degree of a target point by using the relative density of its neighbors in the vicinity of the target point p, and is specifically obtained by the following equation:
wherein L is k (p) represents the outlier degree of the regional power system operating point p based on k neighbors; n (N) k (p) represents a point in the k-distance neighborhood of the point p; l (L) rd，k (. Cndot.) is expressed as local reachable density; d (D) k (p, o) is the reachable distance of point p from point o, which is determined by the maximum between the Euclidean distance of point p from point o and the Euclidean distance of point p from its k neighbors.
The cleaning scheme based on the carbon emission-related characteristic data of LOF and KNN is shown in fig. 2.
Further, regional carbon emission influencing factors are determined:
because the regional carbon emission level is also influenced by external factors such as economic development, climate conditions and the like, extremely high exogenous uncertainty is shown, and besides each link of power generation of a power system and related technologies, other multiple fields are required to be acquired across systems. And (3) carrying out analysis on main factors of the regional power system carbon emission in combination with the regional power system carbon emission scene, and determining regional carbon emission influencing factors. The invention analyzes the carbon emission influence level from the factors of the internal and external technology, economy, environment and the like of the power system, at least comprises:
1) Technical factors. Fuel parameters (e.g., fuel heating value, carbon emission factor); unit parameters (such as unit thermal efficiency, unit load rate, carbon capture rate); system parameters (e.g., genset composition, start-up mode, load level) will all have an impact on the physical level.
2) Economic factors. The influence of macro economic development situation on the primary/secondary energy supply and demand relation, the influence of multi-field market mechanism design, price level and micro transaction strategy of energy market, environment market and the like on the market competitiveness of different manufacturers can directly or indirectly influence from the economic level.
3) Environmental factors. In addition to climate and weather conditions, various regulatory measures for the outside of the power system environment will introduce short-term or long-term, intensity or total carbon emission constraints for different levels of emission principals, influencing by regulating the outside of the power system environment.
The above factors do not affect the power system carbon emission level in isolation, but rather there are complex interactions. For example, tightening of carbon emission constraints will change supply and demand relationships and price levels of related markets, and influence production operation and investment strategies of enterprises after cost conduction, thereby influencing operation modes and power generation structures of power systems. Because the carbon emission dynamic process is also influenced by a plurality of factors, a series of processes such as supply, storage, use and the like need to be considered, and various data such as physics, economy, environment, related policies and the like are involved, wherein the data also comprises statistical data, relational data, game data and the like. Therefore, the construction of the carbon emission measuring and calculating model needs to be carried out on the basis of the acquired data, and the accuracy is limited by the quality of the data.
In addition, the above-described influencing factors may be selected by constructing a typical carbon emission scenario, wherein the construction process of the typical carbon emission scenario is as follows:
And aiming at the researched area, acquiring relevant power data of the key industry and the area carbon emission measurement analysis, and extracting the metering data characteristics. The required data includes, but is not limited to, current month electricity consumption data of various industries in various areas, new energy power generation data in current month areas, power exchange data between current month areas and the like.
And collecting main economic activity data of the region, selecting proper indexes from the main economic activity data, wherein the indexes can be related to the carbon emission of the regional power system, such as the carbon emission of GDP and unit GDP, the carbon emission of electricity consumption and unit electricity consumption, the carbon emission of electricity consumption and unit electricity generation and the like of each region in each industry and each industry, and effectively reflecting the urban economic development level and the carbon emission of the urban power system. And taking main economic activities of the region and relevant data characteristics of the power system into consideration, and constructing a typical scene of the carbon emission of the power system by adopting a fuzzy C-means clustering algorithm.
The influence of seasons, climates and other factors on the new energy output is considered, and the load variation has an obvious time sequence rule. The invention adopts the fuzzy C-means clustering algorithm to perform scene reduction on a large number of initial scenes, and constructs typical time sequence scenes of distributed power and load power so as to improve the practicability of scheme design and the accuracy of evaluation.
Fuzzy C-Means clustering (Fuzzy C-Means) is a clustering analysis method fused with Fuzzy theory, and the basic principle is that membership degree is utilized to quantitatively express the degree of membership of samples to categories, an uncertain relation of the samples to the categories is established, and then the samples are clustered and partitioned. The specific implementation method is that membership degree of each sample to each clustering center is obtained through optimizing an objective function, so that class of the sample is determined, and the aim of automatically classifying and reducing sample data is fulfilled. A typical time sequence scene construction flow based on the fuzzy C-means clustering algorithm is shown in FIG. 3.
The method comprises the following specific steps:
(1) Based on sample data of distributed power supply and load of power system, n initial scenes X= { X are formed 1 ,X 2 ,…,X n Determining an initial cluster seed number c=2, and initializing a cluster center scene c= { C 1 ,C 2 ,…,C c Initializing membership matrix u= { U } ij }。
Taking Euclidean distance and minimum between each sample point and each clustering center as optimization targets, namely:
wherein: n is the number of samples; c is the number of cluster seeds; x is X j For the j-th sample; c (C) i Is the ith cluster center; u (u) ij For sample X j For the cluster center C i Membership degree of (3); m is a fuzzy weighting coefficient.
Membership u ij The value range of (2) is [0,1 ]]The larger the value is, the scene X is represented j For the cluster center C i The higher the degree of membership; for a single scene X j The sum of membership degrees of each cluster center is 1; for each polymerClass center, which contains between 1 and the total number of scenes. Thus membership u ij The constraint conditions of (2) are:
(2) Updating the scene and membership matrix of each clustering center:
(3) And (3) updating the objective function value, judging whether the convergence condition is met, if so, entering the next step, and otherwise, repeating the step (2). The convergence condition is that the objective function value is not changed basically, namely:
|J (k+1) -J (k) |＜ε
wherein: k represents the number of iterations and epsilon is a small constant.
(4) And calculating a clustering effectiveness evaluation index S according to the iteration result, wherein the larger S represents the better clustering effect, and the numerical value of the clustering seed number c is increased by 1.
(5) Repeating the steps (2) - (4) until the number of the cluster seeds is reachedn is the initial scene number, and the result with the largest S is selected as the optimal clustering result.
(6) And obtaining an optimal membership matrix and a clustering center scene through the steps, carrying out clustering reduction on the new energy output and load power scenes of the region according to the membership matrix, and selecting each clustering center scene as a typical scene.
Further, referring to fig. 3, the regional power system carbon emission influence factor contribution degree is calculated:
And roughly obtaining factors influencing the carbon emission of the electric power through principal component analysis, quantitatively determining regional carbon emission influencing factors through an LMDI method and an SDA method, and calculating the contribution degree of the regional electric power system carbon emission influencing factors.
Among the decomposition methods of influence factors that affect carbon emission changes, structural decomposition methods and factor decomposition methods are two of the most widely used, and LMDI decomposition methods are one of the factor decomposition methods, and factor decomposition methods also include laseres decomposition methods, fisher decomposition methods, and the like. The structural decomposition method is based on a complete input-output table, and is difficult to be specific to a certain area or industry because the complete input-output table of carbon emission is relatively difficult to obtain. The invention recommends an exponential decomposition method, wherein Kaya identity is used as the basis and mathematical judgment of carbon emission or carbon emission intensity factor decomposition, and a structural decomposition analysis method (SDA) is adopted as the data basis by means of an input-output method to analyze and calculate the contribution rate of each influence factor. The SDA factor decomposition is implemented by way of example with a two-factor system.
S＝BY
S as a system variable can be decomposed into products of two factors B and Y, so that
ΔS＝S 1 -S 0 ,ΔB＝B 1 -B 0 ,ΔY＝Y 1 -Y 0
The variable and the influence factor have the variable amounts of delta S, delta B and delta Y in the current period and the previous period respectively, and subscripts 1 and 0 of the variable and the influence factor represent the current period and the previous period respectively, then:
ΔS＝B 1 Y 1 -B 0 Y 0 ＝(B 1 -B 0 )Y 0 +B 0 (Y 1 -Y 0 )+
(B 1 -B 0 )(Y 1 -Y 0 )＝ΔBY 0 +B 0 ΔY+ΔBΔY
Typically, ΔBY 0 The initial influence of the variation of the influence factor called B is B 0 ΔY is referred to as the initial influence of Y-influencing factor variation on S, and ΔBΔY is referred to as two factorsThe co-effect of the elements.
If the individual items that affect each other are not listed independently, combining some of them into the initial influencing factors will result in two combining modes:
ΔS＝(ΔBY 0 +ΔBΔY)+B 0 ΔY＝ΔBY 1 +B 0 ΔY
ΔS＝ΔBY 0 +(ΔBΔY+B 0 ΔY)＝ΔBY 0 +B 1 ΔY
the above equation shows that the SDA method has obvious data dependence, and the full-element-based analysis must have large complete data support, otherwise the model construction of the SDA method cannot be successfully completed.
The exponential decomposition (IDA) method breaks through the limitation that the SDA method is required to be based on input-output table analysis, and only time sequence data is needed to reasonably calculate the contribution rate of the influence factors, wherein the expression is as follows:
wherein F represents an object to be studied and decomposed, such as an index of carbon emission intensity, carbon emission, energy consumption, energy intensity, etc., X 1i X 2i …X ni N factors affecting F are expressed, i is an index of different industry categories, different energy varieties or different regions. Exponential decomposition is generally performed by taking time series data as an object, and examining influencing factors behind variable changes in different periods, such as values of a basal period and a t period, which are respectively expressed as follows:
In general, there are two ways of decomposition of the object under study F, one in multiplicative form and the other in additive form, as follows:
D total ＝F t /F 0 ＝D x1 ·D x2 …D xn ·RD
ΔF total ＝F t -F 0 ＝ΔF x1 +ΔF x2 +…ΔF xn +ΔRD
wherein D is total And DeltaF total The values of the target variable from the 0 th phase to the t th phase are represented by the respective values, and Δrd represents the residual after decomposition, and the residual is generally an unexplained part. Therefore, the IDA method is combined with various factor decomposition methods which are popular at present in the application process, and the delta RD is decomposed again through reasonable addition of weights, so that the influence of the factors which cannot be explained at present is eliminated to the greatest extent.
Further, the association degree relation between the carbon emission and the main economic index of the regional power system:
based on a systemization view, key boundary conditions are constructed by considering important factors such as emission reduction expectation, policy expectation, economic development and the like, the association relation between the regional power system carbon emission and main economic indexes is analyzed, and the systematic regional power system carbon emission analysis model construction is supported.
The method adopts TAPIO, OECD, LMDI index decomposition method and other methods to construct indexes such as unhooking index and the like, and reflects the association relation between the carbon emission of the power system in the area and the main economic activity index; and carrying out multidimensional division on the acquired power system data according to an economic structure, an industrial structure, an energy structure and energy intensity, and analyzing by using corresponding indexes to obtain a logic relationship between the carbon emission of the power system and the operation business of the power system. The region primary economic activity index type and relationship to the carbon emissions of the power system is shown in fig. 3.
Economic indicators are key data for deep understanding of economic performance in a country, and generally refer to macroscopic economic indicators that are published and presented on a large scale, and are generally used for analyzing current and future trends. Most economic indicators are official data compiled by governments or non-profit organizations of various countries. Economic indicators are roughly divided into three groups: 1) Leading index: for predicting future fluctuations and trends in economies. These numbers change prior to the economy and help to find opportunities, but are not 100% accurate and therefore risky to use. 2) Synchronization index: is the result of a particular economic activity, and is useful only in measuring a certain area or region, but is a real-time snapshot of the economy. 3) Hysteresis index: tracking economies, which are released after an economic event occurs, is not necessarily useful in finding transaction opportunities, but can be used to discern the health of the economy.
As the regression analysis method in the economic statistics is mainly only used for the problems of few factors and linearity, the invention recommends to adopt a gray correlation analysis method to analyze the relations of regional power system carbon emission, population, economic growth, industrial structure, GDP speed increase and the like, establish the gray correlation degree of corresponding time series data and reflect the dynamic correlation degree of regional power system carbon emission, population, economic growth, industrial structure, GDP speed increase and the like.
In the implementation, the gray correlation technology is adopted to measure the factor correlation between two systems, and the analysis steps are as follows:
(1) And forming comparison sequences of different operation scenes according to the power metering data of the regional power system. The matrix form formed by the n comparison sequences is as follows:
wherein m is the number of indexes, wherein X 0 ＝(x 01 ,x 02 ,…x 0m )。
(2) Determining a reference data column
The reference data sequence should be an ideal comparison standard, and the reference data sequence can be composed of the optimal value (or the worst value) of each index, or other reference values can be selected according to the evaluation purpose and marked as X i ＝(x i1 ,x i2 ,…x im )。
(3) Non-dimensionality processing for variable sequence
Because of the different physical meanings of the factors in the system, the dimension of the data is also different, the comparison is inconvenient, or the correct conclusion is difficult to obtain during the comparison. Thus inIn the gray correlation analysis, dimensionless data processing is generally performed. Common dimensionless methods are averaging, initialization andtransformation, etc. The gray correlation analysis method is commonly used for carrying out dimensionless treatment by an initial value method, and the specific formula is as follows:
(4) The dimensionless data sequence forms the following matrix:
the absolute difference sequence, the maximum difference and the minimum difference absolute difference sequence of each comparison sequence and the reference sequence are calculated one by one as follows: i x' 0k -x′ ik I, k=1, 2 … m, i=1, 2 … n, maximum difference is:the minimum difference is:
(5) Calculating a correlation coefficient:
where ρ is a resolution factor and is typically 0.5.
(6) Calculating the degree of association
Calculating the average value of the association coefficient of each index and the corresponding element of the reference sequence for each comparison sequence to reflect the association relation between each comparison sequence and the reference sequence, namely the association degree, and marking as:
further, constructing a regional power system carbon emission measurement and analysis model:
in order to realize the carbon emission measurement based on the systematic view angle, the obtained related data are required to be combined, the logic relationship between the carbon emission of the urban power system and the operation business of the power system is considered, the association relationship between the carbon emission of the regional power system and the main economic index and the key boundary conditions such as emission reduction expectation, policy expectation and economic development are considered, further, an analysis model for measuring and calculating the carbon emission of the regional power system is constructed, the input model and the output content are definitely determined, and the association relationship between the carbon emission of the power system and the main economic activity index of the city in different dimensions such as the region, industry and the like is supported.
The electricity-carbon analysis model adopts historical electric quantity data, energy consumption data and product yield data to train an electricity-yield analysis model and an electricity-energy consumption analysis model to describe the relation between electricity and the production process and the energy consumption. After training, the current month electricity consumption data of each industry and each region are input into a model to obtain the production process level and energy activity level data of each industry and region in the current month, and then factors provided by IPCC and the like are adopted to calculate the carbon discharge. In addition, when accounting for regional carbon emissions, it is necessary to add regional power-in and out-call generated transferred carbon emissions, which are multiplied by regional power-in and out-call transferred power by regional power-carbon emission factors to obtain transferred carbon emissions resulting from power-in and out-call.
In addition, the calculation example analysis based on the technical scheme of the invention is as follows:
1) The carbon emission increment decomposition result of each influencing factor
Conversion efficiency DeltaV from fossil energy u Energy structure DeltaV t Electric power structure DeltaV s Proportional effect of electricity generation DeltaV q Intensity of consumption DeltaV e Economic scale DeltaV r Population size DeltaV p Net loss effect DeltaV l The contribution rate of each influencing factor can be obtained by dividing the carbon emission variation caused by the aspects of the like by the total carbon emission variation. Typically, the contribution rate is greater than 0, indicating that the influencing factor has a pulling effect on the carbon emissions. If the contribution rate is smaller than0, it indicates that the influencing factor has a slowing effect on carbon emissions. The continuous decomposition method is selected, and each effect is decomposed and analyzed year by year in a unit of one year, and the carbon emission change amount, the contribution value and the contribution rate of each effect in the power industry during the period are shown in tables 1 and 2.
Table 12005-2015 shows the incremental decomposition results (ten thousand tons) of carbon emissions by various influencing factors
Table 22005-2015 shows the decomposition contribution rate of each influence factor
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The emission factor comes from energy input, and although China is greatly propelling new energy to generate electricity, coal input and use are still main energy sources for generating electricity. The power structure, the conversion efficiency and the power generation ratio are index evaluation of production operation of the thermal power plant, so that the energy conversion efficiency can be improved, and the carbon emission in power production can be reduced. The economics, population size, etc. are all the factors that govern the analysis of the change in carbon emissions from electricity production. The network loss is the analysis of the carbon emission of the power loss from the power transmission process, and the network loss can be reduced by establishing an extra-high voltage circuit. Along with the continuous and rapid development of the economy in China, the construction of large enterprises and the improvement of the living standard of residents promote the development of power enterprises, and the carbon emission of power production is increased.
From the aspect of economic benefit, the system can help the electric power system develop more targeted emission reduction work, promote the reasonable utilization of various energy sources at the power generation side, optimize the energy source structure according to the expectation of carbon emission reduction, avoid the reduction of the economic benefit caused by blind emission reduction, and realize the coordinated propulsion of the emission reduction work and the economic development of the electric power system.
From the viewpoint of energy conservation and environmental protection, the operation mode of the power system can be improved and the carbon emission in the operation of the power system can be reduced by analyzing the main factors influencing the carbon emission of the power system and the correlation between each main factor and the operation of the power system.
The development direction of the carbon emission of the electric power system in the future is to establish a carbon emission statistics, monitoring and evaluation index system of the system, and an accurate and reliable carbon emission index statistics, monitoring and evaluation is realized by adopting a method suitable for analyzing the carbon emission track of the electric power system, so that the decomposition target of the carbon emission reduction of enterprises is influenced. Therefore, the distribution and the track of the carbon emission of the electric power system can be mastered more effectively and comprehensively, and the realization of the double-carbon target of the electric power system is greatly supported.
And according to the acquired carbon emission data, integrating and forming the regional carbon emission total, the regional carbon emission total and the regional carbon emission total in 2005-2020, and taking the regional carbon emission total and the regional carbon emission total as verification reference data. The sub-industry data comprise six major industries such as agriculture, forestry, animal husbandry and fishery, industry and building industry, energy industry, transportation industry, service industry, resident life and the like, and eight minor industries such as steel, nonferrous metals, chemical industry, building materials, papermaking and paper products, buildings, thermal power and the like; the industry data comprises six major industries such as agriculture, forestry, animal husbandry, fishery, industry and building industry, energy industry, transportation industry, service industry, resident life and the like, and eight minor industries such as steel, nonferrous metals, chemical industry, building materials, paper making and paper products, buildings, thermal power and the like.
2) Regional carbon emission results based on electro-carbon and energy-carbon models
The data of 2005-2015 is used as a training set, and the total carbon emission, the branch industrial carbon emission, the branch regional carbon emission and the branch industrial carbon emission of 2016-2020 are respectively measured and calculated through an electric-carbon analysis model and an electric-carbon model to be used as verification data. And (5) measuring and calculating the discharge amount of the 2016-year electric carbon model by using 2005-2015 data as a training set. And the average absolute percentage error rate is used as an evaluation index, the measurement and calculation results of the electric-carbon analysis model in each area and in the branch industry are respectively compared with the output results of the carbon emission accounting data of the carbon analysis model, the average absolute percentage error rate is used as an evaluation index, and the smaller the error rate is, the more accurate the measurement and calculation results of the electric-carbon analysis model are.
The average absolute percentage error rate of the total carbon emission in the regions of 2016-2020 in the electric-carbon analysis model and the carbon emission accounting data is calculated respectively, the accuracy of the comparison of the total carbon emission in the regions measured by the electric-carbon analysis model is demonstrated, and the specific verification result is shown in the table 3. In the overall aspect, the accuracy of the electric carbon model is highest when the carbon emission of the branch industry is predicted, the accuracy of the carbon emission of the predicted area and the carbon emission of the branch area are not greatly different, the error rate measured and calculated by the electric-carbon analysis model is within 10 percent and shows a fluctuation trend, and the average absolute percentage error rate of five years from 2016 to 2020 is 4.94 percent.
TABLE 3 regional carbon emission results for electric-carbon model and energy-carbon model (unit: ten thousand tons)
Thereby, the beneficial effects of this application are as follows:
1. the method comprises the steps of constructing a regional power system carbon emission typical scene by using a fuzzy C-means clustering algorithm, wherein different power system scenes in a region are divided into a plurality of typical categories, and each category represents a specific carbon emission scene. Through a fuzzy C-means clustering algorithm, the relations among different economic development levels, power system characteristics and related data characteristics can be considered, so that a representative carbon emission typical scene is constructed. The distribution and the track of the carbon emission of the electric power system are more comprehensively known, and guidance is provided for reducing the carbon emission level;
2. the invention analyzes the association degree of the regional power system carbon emission and the main economic index by using a gray association analysis method. Such analysis may help to clarify the relationship between the carbon emissions of the power system and the economic state of the art factors and quantify the degree of correlation between them. The method has important significance for formulating comprehensive emission reduction expectations, policy expectations, economic development and other important factors and promoting carbon emission optimization of the power system.
3. The invention provides a carbon emission measuring and calculating method based on 'electricity-carbon' analysis, which trains an 'electricity-yield analysis model' and an 'electricity-energy consumption analysis model' by utilizing historical electric quantity data, energy consumption data and product yield data. The models are helpful for predicting production process level and energy activity level data of various industries and various areas in the power system, and the carbon emission calculation is realized by adopting a factor method based on the production process level and the energy activity level data. .
Exemplary apparatus
Fig. 4 is a schematic structural diagram of a regional power system carbon emission measurement device according to an exemplary embodiment of the present invention. As shown in fig. 4, the apparatus 400 includes:
the acquiring module 410 is configured to acquire relevant data of carbon emission measurement and analysis in key industries and areas, screen available data from the relevant data by adopting a data preprocessing method of LOF and KNN, complement missing data and detect abnormal values, and determine effective relevant data;
the first calculation module 420 is configured to determine regional carbon emission influencing factors according to the valid related data, and calculate contribution degrees of the regional carbon emission influencing factors by using an exponential decomposition method;
a determining module 430, configured to determine, according to the valid related data, an association relationship between the regional carbon emission influencing factor and the main economic activity;
The construction module 440 is configured to construct a regional power system carbon emission measurement analysis model according to the contribution degree of the regional carbon emission influencing factors and the association relation between the regional carbon emission influencing factors and the main economic activities;
the second calculation module 450 is configured to input the current electricity consumption data of each industry and each region into a regional power system carbon emission measurement and analysis model to obtain production process level and energy activity level data of each industry and each region in the current month, and calculate carbon discharge of each industry and each region in the current month by using factors provided by IPCC.
Optionally, the acquiring module 410 includes:
the first determining submodule is used for carrying out anomaly detection on the power data based on an LOF algorithm and determining all detected anomaly values of the power data;
and the second determination submodule is used for carrying out data complementation on the detected abnormal value based on the KNN and determining effective power data.
Optionally, the regional carbon emission influencing factors include:
technical factors, including: fuel parameters, unit parameters, and system parameters;
economic factors, including: influence of macro economic development situation on primary/secondary energy supply and demand relation; influence of multi-field market mechanism design, price level and micro-trading strategy on market competitiveness of different manufacturers;
Environmental factors, including: climate, weather, and power system environment exteriors.
Optionally, the contribution calculation formula is:
wherein F represents an object to be studied and decomposed, such as an index of carbon emission intensity, carbon emission, energy consumption, energy intensity, etc., X 1i X 2i …X ni N factors affecting F are expressed, i is an index of different industry categories, different energy varieties or different regions.
Optionally, the determining module 430 includes:
the first generation sub-module is used for generating a comparison sequence of the carbon emission of the power system according to the effective related data;
the second generation sub-module is used for generating a reference data sequence of the operation business of the power system according to the effective related data;
the third determining submodule is used for determining the association degree between the carbon emission of the electric power system and the operation business of the electric power system according to the comparison sequence and the reference book sequence;
and the fourth determining submodule is used for determining a logic relation according to the association degree.
Exemplary electric PowerSub-device
Fig. 5 is a structure of an electronic device provided in an exemplary embodiment of the present invention. As shown in fig. 5, the electronic device 50 includes one or more processors 51 and memory 52.
The processor 51 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
Memory 52 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that may be executed by the processor 51 to implement the methods of the software programs of the various embodiments of the present invention described above and/or other desired functions. In one example, the electronic device may further include: an input device 53 and an output device 54, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device 53 may also include, for example, a keyboard, a mouse, and the like.
The output device 54 can output various information to the outside. The output device 54 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device relevant to the present invention are shown in fig. 5 for simplicity, components such as buses, input/output interfaces, etc. being omitted. In addition, the electronic device may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary methods" section of this specification.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the invention may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary method" section of the description above.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present invention. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the invention is not necessarily limited to practice with the above described specific details.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, systems, apparatuses, systems according to the present invention are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, systems, apparatuses, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It is also noted that in the systems, devices and methods of the present invention, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (8)
1. A regional power system carbon emission measurement method, comprising:
acquiring relevant data of carbon emission measurement and analysis in key industries and areas, screening available data, complementing missing data and detecting abnormal values from the relevant data by adopting a LOF and KNN data preprocessing method, and determining effective relevant data;
determining regional carbon emission influence factors according to the effective related data, and calculating the contribution degree of the regional carbon emission influence factors by using an exponential decomposition method;
according to the effective related data, determining the association relation between the regional carbon emission influencing factors and main economic activities;
according to the contribution degree of the regional carbon emission influencing factors and the association relation between the regional carbon emission influencing factors and the main economic activities, constructing a regional power system carbon emission measuring and calculating analysis model;
Inputting the current electricity consumption data of each industry and each region into the regional power system carbon emission measuring, calculating and analyzing model to obtain the production process level and the energy activity level data of each industry and each region in the current month, and calculating the carbon discharge capacity of each industry and each region in the current month by adopting factors provided by IPCC.
2. The method of claim 1, wherein screening available data, complementing missing data, and detecting outliers from the power data using a data preprocessing method of LOF and KNN, determining valid power data comprises:
performing anomaly detection on the power data based on an LOF algorithm, and determining all detected anomaly values of the power data;
and carrying out data complementation on the detected abnormal value based on KNN, and determining the effective power data.
3. The method of claim 1, wherein the regional carbon emission influencing factors comprise:
technical factors, including: fuel parameters, unit parameters, and system parameters;
economic factors, including: influence of macro economic development situation on primary/secondary energy supply and demand relation; influence of multi-field market mechanism design, price level and micro-trading strategy on market competitiveness of different manufacturers;
Environmental factors, including: climate, weather, and power system environment exteriors.
4. The method of claim 1, wherein the contribution calculation formula is:
wherein F represents an object to be studied and decomposed, such as an index of carbon emission intensity, carbon emission, energy consumption, energy intensity, etc., X 1i X 2i …X ni N factors affecting F are expressed, i is an index of different industry categories, different energy varieties or different regions.
5. The method of claim 1, wherein determining the association of regional carbon emission impact factors with primary economic activity based on the availability-related data comprises:
generating a comparison sequence of the carbon emission of the electric power system according to the effective related data;
generating a reference data sequence of the power system operation service according to the effective related data;
determining the degree of association between the carbon emission of the power system and the operation business of the power system according to the comparison sequence and the reference book sequence;
and determining the logic relationship according to the association degree.
6. A regional power system carbon emission measurement device, comprising:
The acquisition module is used for acquiring relevant data of carbon emission measurement and analysis in key industries and areas, screening available data, supplementing missing data and detecting abnormal values from the relevant data by adopting a LOF and KNN data preprocessing method, and determining effective relevant data;
the first calculation module is used for determining regional carbon emission influence factors according to the effective related data and calculating the contribution degree of the regional carbon emission influence factors by using an exponential decomposition method;
the determining module is used for determining the association relation between the regional carbon emission influence factors and the main economic activities according to the effective related data;
the construction module is used for constructing a regional power system carbon emission measurement and analysis model according to the contribution degree of the regional carbon emission influence factors and the association relation between the regional carbon emission influence factors and the main economic activities;
the second calculation module is used for inputting the current electricity consumption data of each industry and each region into the regional power system carbon emission measuring and calculating analysis model to obtain the production process level and the energy activity level data of each industry and each region in the current month, and calculating the carbon discharge of each industry and each region in the current month by adopting factors provided by IPCC.
7. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the method of any of the preceding claims 1-5.
8. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any of the preceding claims 1-5.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117808497A (en) * | 2024-03-01 | 2024-04-02 | 清华四川能源互联网研究院 | Electric power carbon emission abnormity detection module and method based on distance and direction characteristics |
| CN117808497B (en) * | 2024-03-01 | 2024-05-14 | 清华四川能源互联网研究院 | Electric power carbon emission abnormity detection module and method based on distance and direction characteristics |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117808497A (en) * | 2024-03-01 | 2024-04-02 | 清华四川能源互联网研究院 | Electric power carbon emission abnormity detection module and method based on distance and direction characteristics |
| CN117808497B (en) * | 2024-03-01 | 2024-05-14 | 清华四川能源互联网研究院 | Electric power carbon emission abnormity detection module and method based on distance and direction characteristics |
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