Power system carbon emission dynamic adjustment method and system based on carbon flow modeling

Abstract

The invention discloses an electric power system carbon emission dynamic adjustment method based on carbon flow modeling, and belongs to the technical field of electric power dispatching, and the method comprises the following steps: S1, obtaining multivariate heterogeneous data associated with an electric power system, and carrying out the feature extraction of the multivariate heterogeneous data to obtain an index set representing the carbon emission; s2, constructing a power system carbon flow model according to the topological structure and the power flow direction of the power system in combination with the index set; s3, the power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to the real-time power operation data and the trend factor; and S4, determining an index adjustment factor according to the carbon emission intensity, wherein the index adjustment factor acts on the index set to obtain a current carbon emission adjustment strategy. According to the scheme, the regional carbon emission is measured according to the carbon emission intensity, the index adjustment factor of each node is obtained by combining the value degree, each node is guided to perform refined regulation according to the index adjustment factor, and the reliability of the power dispatching scheme is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a method and system for dynamically adjusting carbon emissions of a power system based on carbon flow modeling. Background Art

[0002] Traditional carbon emission calculation and analysis methods are mostly based on static models and offline data, which cannot accurately reflect the dynamic characteristics of real-time operation of power systems. Power system operation is highly complex and uncertain. Power generation output changes with factors such as energy supply and unit failures. Load demand fluctuates significantly due to the influence of season, time, economic activities, etc. The grid topology may also change due to equipment maintenance, fault recovery, etc. These dynamic changes have a direct and complex impact on carbon emissions, but existing methods are difficult to capture and quantify these impacts in real time, resulting in the inability to formulate effective emission reduction strategies in a timely manner.

[0003] Existing adjustment strategies lack systematicity and flexibility. Remedial measures are often taken after analyzing carbon emission data, rather than making active and dynamic adjustments based on real-time monitoring and prediction. For example, when it is found that carbon emissions are too high during a certain period of time, only relatively extensive adjustment measures can be taken, such as simply increasing or reducing the power generation output of a certain type of unit, without fully considering the overall operating constraints of the power system, the diversity of energy supply, and the impact of different adjustment measures on system stability and economy. This passive adjustment method is not only limited in effect, but may also cause other problems.

[0004] With the advancement of new power system construction, large-scale access to renewable energy, continuous improvement of power market mechanisms, and increased user participation, the operation of power systems has become more complex and changeable. Therefore, an innovative method for dynamic adjustment of carbon emissions in power systems is urgently needed to achieve low-carbon, efficient, and stable operation of power systems.

[0005] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to solve the problem of weak pertinence of power dispatching due to inaccurate carbon emission measurement. A method and system for dynamic adjustment of carbon emissions in a power system based on carbon flow modeling is proposed. An indicator set is constructed by acquiring characteristic factors that are strongly correlated with carbon emissions. Trend factors are determined by analyzing the economic development trends of carbon emission areas. A carbon flow model of the power system is constructed in combination with the indicator set to calculate the carbon emission intensity of the carbon emission area. The regional carbon emissions are calculated according to the carbon emission intensity and then combined with the value to obtain the indicator adjustment factor of each node. Each node is guided to perform fine-grained regulation according to the indicator adjustment factor, thereby significantly improving the reliability of the power dispatching scheme.

[0007] In a first aspect, a technical solution provided in an embodiment of the present invention is a method for dynamically adjusting carbon emissions of a power system based on carbon flow modeling, comprising the following steps: S1. Acquire multivariate heterogeneous data associated with the power system, and perform feature extraction on the multivariate heterogeneous data to obtain an indicator set representing carbon emissions; S2. Construct a carbon flow model of the power system based on the topological structure and power flow direction of the power system combined with an indicator set; S3. The power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to real-time power operation data and trend factors; S4. Determine the indicator adjustment factor according to the carbon emission intensity, and the indicator adjustment factor acts on the indicator set to obtain the current carbon emission adjustment strategy.

[0008] In this scheme, data is collected from various nodes and components of the power system (such as power plants, substations, transmission lines, user terminals, etc.), and features closely related to carbon emissions are extracted; by comprehensively collecting and analyzing the multivariate heterogeneous data of the power system, the carbon emission status of the power system can be more accurately reflected, providing a solid foundation for subsequent modeling and adjustment strategies; the extracted carbon emission indicators are combined with the topological structure and flow direction of the power system to construct a power system carbon flow model, which can simulate the flow and distribution of carbon emissions in the power system and reflect the carbon emission contribution of different nodes and components; through real-time data monitoring and trend factor analysis, the target carbon emission intensity can be adjusted in time to ensure that the carbon emission management strategy is consistent with the actual operation status and policy orientation of the power system; by calculating the indicator adjustment factor and acting on the indicator set, a highly targeted and feasible carbon emission adjustment strategy can be formulated to effectively reduce the carbon emissions of the power system. The dispatching strategy formulated in this scheme takes into account the actual situation and future development trend of the power system, ensuring the effectiveness and sustainability of the strategy. At the same time, by continuously optimizing and adjusting the strategy, the carbon neutrality goal of the power system can be gradually approached and ultimately achieved.

[0009] Preferably, the multivariate heterogeneous data includes power generation side data, power transmission side data, power distribution side data, power consumption side data and meteorological data.

[0010] In this solution, various types of data are collected, including but not limited to electricity consumption, fuel type and consumption, equipment operating efficiency, environmental parameters (such as temperature and humidity), policy factors (such as carbon tax and emission reduction targets), etc. These data constitute a multivariate heterogeneous data set.

[0011] Preferably, feature extraction is performed on the multivariate heterogeneous data to obtain an index set characterizing carbon emissions, including the following steps: obtaining characteristic factors of the multivariate heterogeneous data, and calculating the value between each characteristic factor and carbon emissions through a correlation algorithm; Characteristic factors with values ​​greater than the preset values ​​are regarded as effective characteristic factors, and the effective characteristic factors are aggregated to generate an indicator set.

[0012] Preferably, a carbon flow model of a power system is constructed according to the topological structure and flow direction of the power system in combination with an indicator set, including the following steps: S21, constructing node-branch directed boundaries in the power system to generate carbon emission areas, and determining the active power flow distribution of each directed boundary in the power system through a power flow calculation method; S22, determining the branch power flow distribution matrix and the unit injection distribution matrix according to the power flow calculation results; S23, combining the indicator set to obtain the carbon emission intensity of each power generation unit in the carbon emission area in real time to construct a carbon emission intensity vector; S24, calculating the node carbon potential vector through the node active flux matrix, the branch power flow distribution matrix, the unit injection distribution matrix, and the carbon emission intensity vector; S25, calculating the branch carbon flow rate distribution matrix according to the node carbon potential vector and the branch power flow distribution matrix; S26, calculating the load carbon flow rate vector according to the node carbon potential vector and the load distribution matrix; S27. The branch carbon flow rate distribution matrix and the load carbon flow rate vector construct the power system carbon flow model.

[0013] Preferably, the power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to the real-time power operation data and the trend factor; comprising the following steps: S31. Construct a carbon emission trend prediction model based on regional industrial planning and energy structure adjustment strategies; S32. Predict the predicted carbon emissions for a period after the time node through the carbon emission trend prediction model. If the carbon emissions in the period after the time node are greater than the carbon emissions in the period before the time node, the trend factor is determined to be positive; if the carbon emissions in the period after the time node are equal to the carbon emissions in the period before the time node, the trend factor is determined to be zero; if the carbon emissions in the period after the time node are less than the carbon emissions in the period before the time node, the trend factor is determined to be negative; adjust the output of the power system carbon flow model according to the trend factor to obtain the target carbon emission intensity.

[0014] As a preferred method, the calculation formula of the target carbon emission intensity is: Among them, C t is the predicted carbon emission intensity at time t, C t ' is the predicted carbon emission intensity at time t; E t represents the power generation at time t, F i represents the carbon emission factor of the i-th power generation type, P i represents the proportion of power generation of the i-th power generation type; N is the upper limit of the power generation type, λ t is the trend factor at time t; Q t+1 is the carbon emission of the carbon emission area at time t+1, Q t is the carbon emissions of the carbon emission area at time t.

[0015] Preferably, the indicator adjustment factor is determined according to the carbon emission intensity, and the indicator adjustment factor acts on the indicator set to obtain the current carbon emission adjustment strategy; the steps include: S41, determining the value of each characteristic factor and carbon emission intensity according to the correlation algorithm, and determining the weight coefficient of the characteristic factor corresponding to each value through the normalization algorithm; S42, determining the carbon carrying capacity of each characteristic factor according to the carbon emission amount corresponding to the carbon emission intensity combined with the weight coefficient; S43. The difference between the carbon carrying capacity and the actual carbon carrying capacity determines the indicator adjustment factor, and the indicator adjustment factor acts on the characteristic factor in the corresponding indicator set to obtain the carbon emission adjustment strategy.

[0016] In a second aspect, a technical solution provided in an embodiment of the present invention is a carbon emission dynamic adjustment system, comprising: Data acquisition module: acquires multivariate heterogeneous data associated with the power system, extracts features from the multivariate heterogeneous data, and obtains an indicator set representing carbon emissions; Model building module: constructs a power system carbon flow model based on the topological structure and flow direction of the power system combined with an indicator set; Data analysis module: the power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to real-time power operation data and trend factors; Strategy generation module: determines the indicator adjustment factor according to the carbon emission intensity, and the indicator adjustment factor acts on the indicator set to obtain the current carbon emission adjustment strategy.

[0017] In the third aspect, a technical solution provided in an embodiment of the present invention is an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the method for dynamic adjustment of carbon emissions of the power system based on carbon flow modeling are implemented.

[0018] In a fourth aspect, a technical solution provided in an embodiment of the present invention is a storage medium, in which computer executable instructions are stored. When the computer executable instructions are loaded and executed by a processor, the steps of the method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling are implemented.

[0019] The present invention has at least the following beneficial effects: (1) The present invention obtains multivariate heterogeneous data associated with the power system and extracts features to obtain an index set that characterizes carbon emissions. These index sets cover various factors closely related to carbon emissions in the power system, making the measurement of carbon emissions more comprehensive and accurate. At the same time, a carbon flow model is constructed in combination with the topological structure and flow direction of the power system, which can reflect the flow and distribution of carbon emissions in the power system in real time, further improving the accuracy and real-time performance of carbon emission measurement. (2) The present invention determines the target carbon emission intensity of the carbon emission area through real-time power operation data and trend factors, so that the power dispatching strategy can respond to the dynamic changes of the power system in a timely manner; at the same time, the indicator adjustment factor is determined according to the carbon emission intensity, and acts on the indicator set to obtain the current carbon emission adjustment strategy; this dynamic adjustment method can fully consider the actual situation and future development trend of the power system, formulate a highly targeted and flexible power dispatching strategy, and effectively reduce the carbon emissions of the power system; (3) Through the carbon emission dynamic adjustment method and system proposed in the present invention, the target carbon emission intensity of the carbon emission area is dynamically adjusted by using real-time power operation data and trend factors to ensure that the carbon emission management strategy is closely aligned with the actual operation status of the power system; at the same time, by calculating the indicator adjustment factor and accurately acting on the indicator set, a refined adjustment strategy can be formulated for the carbon emission characteristics of each node and component in the power system. This dynamic and real-time management method not only improves the accuracy and efficiency of carbon emission control, but also promotes the coordination and cooperation of various links in the power system, thereby minimizing carbon emissions while ensuring stable power supply, and providing strong technical support for the low-carbon transformation and sustainable development of the power system.

[0020] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0022] Figure 1 This is a flow chart of the method for dynamically adjusting carbon emissions of a power system based on carbon flow modeling of the present invention.

[0023] Figure 2 The figure is a flow chart for constructing the carbon flow model of the power system of the present invention.

[0024] Figure 3 A flow chart is generated for the current carbon emission adjustment strategy of the present invention.

[0025] Figure 4 It is a structural schematic diagram of the carbon emission dynamic adjustment system of the present invention. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0028] Example: Figure 1 As shown, a method for dynamic adjustment of carbon emissions of a power system based on carbon flow modeling includes the following steps: S1, obtaining multivariate heterogeneous data associated with the power system, and performing feature extraction on the multivariate heterogeneous data to obtain an indicator set characterizing carbon emissions.

[0029] As an optional solution of this embodiment, the multivariate heterogeneous data includes power generation side data, power transmission side data, power distribution side data, power consumption side data and meteorological data.

[0030] It is understandable that all-round data acquisition equipment is deployed at all levels of the power system (including power plants, substations, transmission lines, user terminals, and meteorological stations) to obtain data on the full time and space scale of the power system. These data are multi-source heterogeneous, including power generation data (such as power generation, fuel consumption, emission concentration, etc.), transmission data (such as voltage, current, power factor, flow direction, etc.), distribution data, power consumption data (such as power load, power consumption time, etc.) and meteorological data (such as wind speed, light intensity, temperature, etc.). These raw data are cleaned and improved in quality to remove noise data, outliers and erroneous data, fill in missing data, and ensure the continuity and integrity of the data. Then, the data is checked for consistency to ensure the consistency of data from different data sources in physical meaning and logical relationship.

[0031] As an optional solution of this embodiment, feature extraction is performed on multivariate heterogeneous data to obtain an indicator set representing carbon emissions, including the following steps: Obtain the characteristic factors of multivariate heterogeneous data, and calculate the value between each characteristic factor and carbon emissions through the correlation algorithm; Characteristic factors with values ​​greater than the preset values ​​are regarded as effective characteristic factors, and the effective characteristic factors are aggregated to generate an indicator set.

[0032] It is understandable that the first choice is to extract characteristic factors that may be related to carbon emissions from multivariate heterogeneous data. These characteristic factors may include fuel consumption rate, power generation efficiency, unit output, etc. on the power generation side; line loss rate, transmission power, etc. on the transmission side; distribution efficiency, distribution loss, etc. on the distribution side; power load, power consumption time pattern, etc. on the power consumption side; and meteorological data such as wind speed, light intensity, temperature, etc., all of which may directly or indirectly affect carbon emissions; then the correlation algorithm (such as gray correlation analysis, Pearson correlation coefficient, etc.) is used to calculate the value between each characteristic factor and carbon emissions. Taking gray correlation analysis as an example, the algorithm quantifies the correlation between the characteristic factor sequence and the carbon emission sequence by comparing the geometric similarity between them. In the specific calculation, the characteristic factor sequence and the carbon emission sequence need to be dimensionless first to eliminate the influence of dimension differences on the correlation calculation. Then, the correlation coefficient between the characteristic factor sequence and the carbon emission sequence is calculated. The larger the correlation coefficient, the higher the correlation between the characteristic factor and the carbon emissions, and the greater the impact on the carbon emissions. The specific algorithm principle can be understood by technicians in this field based on the field knowledge they have mastered, so I will not repeat it again. Furthermore, a value threshold is set according to actual needs to screen effective characteristic factors. This threshold can be set based on experience or determined by cross-validation and other methods. The value of each characteristic factor calculated is compared with the set threshold, and the characteristic factors with a value greater than the threshold are screened out as effective characteristic factors; all the screened effective characteristic factors are gathered together to form an indicator set that characterizes carbon emissions. This indicator set will be used to subsequently construct a carbon flow model for the power system and to formulate a dynamic adjustment strategy for carbon emissions.

[0033] S2. Construct a carbon flow model of the power system based on the topological structure and flow direction of the power system combined with an indicator set.

[0034] Furthermore, if Figure 2 As shown, S2 includes the following steps: S21. Construct the node-branch directed boundary in the power system to generate the carbon emission area, and determine the active power flow distribution of each directed boundary in the power system through the power flow calculation method; since the branch power flow distribution matrix is ​​an N-order square matrix, it is represented by PB=(PBij)N×N; if there is a branch connecting node i and node j (i, j=1,2,…,N), and the positive active power flow flowing from node i to node j through this branch is p, then PBij=p, PBji=0; if the active power flow p flowing through the branch is a reverse power flow, then PBij=0, PBji=p; in other cases, PBij=PBji=0. In particular, for all diagonal elements, PBii=0(i=1,1,…,N).

[0035] S22. Determine the branch power flow distribution matrix and the unit injection distribution matrix based on the power flow calculation results; since the unit injection distribution matrix is ​​a K×N order matrix, it is represented by PG=(PGkj)K×N; if the kth (k=1,2,…,K)th generator set is connected to node j, and the active power flow injected into node j from the kth node containing the generator is p, then PGkj=p, otherwise PGkj=0.

[0036] S23. Acquire the carbon emission intensity of each generator set in the carbon emission area in real time in combination with the indicator set to construct a carbon emission intensity vector; specifically, collect or estimate the carbon emission intensity of the generator set at each collection node to form a generator set carbon emission intensity vector EG.

[0037] S24. Calculate the node carbon potential vector through the node active flux matrix, branch power flow distribution matrix, unit injection distribution matrix, and carbon emission intensity vector. Since the node carbon potential vector is an N-dimensional column vector, it is represented by EN. EN = (PN-PB′) - 1 PG′×EG, where PN is the node active flux matrix, PB′ is the transpose of the branch power flow distribution matrix, PG′ is the transpose of the unit injection distribution matrix, and EG is the carbon emission intensity vector of the generator unit.

[0038] S25. Calculate the branch carbon flow rate distribution matrix according to the node carbon potential vector and the branch flow distribution matrix. Since the branch carbon flow rate distribution matrix is ​​an N-order square matrix, it is represented by RB. RB = PBdiag(EN), where diag(EN) is a diagonal matrix with the node carbon potential vector EN as the diagonal element.

[0039] S26. Calculate the load carbon flow rate vector according to the node carbon potential vector and the load distribution matrix; the load carbon flow rate vector is represented by RL; RL=PL×EN, where PL is the load distribution matrix.

[0040] S27. The branch carbon flow rate distribution matrix and the load carbon flow rate vector construct the power system carbon flow model.

[0041] It can be understood that through the above S21-S27 process, a power system carbon flow model based on the power system topology, power flow direction and indicator set can be constructed. This model can quantitatively describe the flow state of carbon emissions in the power system and provide strong support for the carbon emission management of the power system.

[0042] S3. The power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to real-time power operation data and trend factors.

[0043] Preferably, the power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to the real-time power operation data and the trend factor; comprising the following steps: S31. Construct a carbon emission trend prediction model based on regional industrial planning and energy structure adjustment strategies; S32. Predict the predicted carbon emissions for a period after the time node through the carbon emission trend prediction model. If the carbon emissions in the period after the time node are greater than the carbon emissions in the period before the time node, the trend factor is determined to be positive; if the carbon emissions in the period after the time node are equal to the carbon emissions in the period before the time node, the trend factor is determined to be zero; if the carbon emissions in the period after the time node are less than the carbon emissions in the period before the time node, the trend factor is determined to be negative; adjust the output of the power system carbon flow model according to the trend factor to obtain the target carbon emission intensity.

[0044] It is understandable that the carbon emission trend prediction model constructed is used to predict the carbon emissions in the future (such as one year). Assume that the prediction model shows that in the next year, due to the development of the clean energy industry and the reduction of fossil energy use, the carbon emissions in the region will be reduced by 5% compared with the past year. Based on this prediction result, the trend factor is judged to be negative. Therefore, the output of the power system carbon flow model can be adjusted to appropriately increase the target carbon emission intensity to encourage more economic activities and development.

[0045] By building a carbon emission trend prediction model and considering the impact of factors such as regional industrial planning and energy structure adjustment strategies on carbon emissions, the accuracy of future carbon emission trend predictions can be improved. By dynamically adjusting the output of the power system carbon flow model based on the prediction results and trend factors, the target carbon emission intensity of the carbon emission area can be flexibly set to respond to different carbon emission trends and economic development needs. Through the combined application of real-time power operation data and trend factors, the power system can be better guided towards a low-carbon and environmentally friendly direction, promoting the optimization of the energy structure and the use of clean energy.

[0046] As an optional solution of this embodiment, the calculation formula of the target carbon emission intensity is: Among them, C t is the predicted carbon emission intensity at time t, C t ' is the predicted carbon emission intensity at time t; E t represents the power generation at time t, F i represents the carbon emission factor of the i-th power generation type, P i represents the proportion of power generation of the i-th power generation type; N is the upper limit of the power generation type, λ t is the trend factor at time t; Q t+1 is the carbon emission of the carbon emission area at time t+1, Q t is the carbon emissions of the carbon emission area at time t.

[0047] S4. Determine an indicator adjustment factor according to the carbon emission intensity, and the indicator adjustment factor acts on the indicator set to obtain the current carbon emission adjustment strategy.

[0048] Specifically, Figure 3 As shown, the following steps are included: S41. Determine the value of each characteristic factor and carbon emission intensity according to the correlation algorithm, and determine the weight coefficient of the characteristic factor corresponding to each value through the normalization algorithm.

[0049] It is understandable that data on various characteristic factors related to carbon emissions, such as energy type, equipment efficiency, operating time, etc., are collected; correlation algorithms (such as gray correlation analysis, Pearson correlation coefficient, etc.) are used to analyze the correlation between each characteristic factor and carbon emission intensity to obtain the value of each characteristic factor. The value is converted into a weight coefficient using a normalization algorithm (such as linear normalization, Z-score standardization, etc.) to ensure that the sum of the weight coefficients of all characteristic factors is 1; for example: the characteristic factors include coal usage, natural gas usage, wind power generation, equipment efficiency, etc. Through correlation analysis, it is found that the correlation between coal usage and carbon emission intensity is the highest, and the correlation between wind power generation and carbon emission intensity is the lowest. According to the normalization algorithm, coal usage is assigned a higher weight coefficient, while wind power generation is assigned a lower weight coefficient. Through correlation analysis and weight coefficient determination, the characteristic factors that have the greatest impact on carbon emissions can be accurately identified, and targeted emission reduction strategies can be formulated accordingly to improve emission reduction effects.

[0050] S42. Determine the carbon carrying capacity of each characteristic factor according to the carbon emission amount corresponding to the carbon emission intensity and the weight coefficient.

[0051] It can be understood that the carbon carrying capacity of each characteristic factor under a specific carbon emission intensity is calculated by combining the carbon emission intensity and the weight coefficient. The carbon carrying capacity represents the maximum carbon emissions that each characteristic factor can carry while keeping the current carbon emission intensity unchanged. Based on the weight coefficient obtained in the previous step and the carbon emission intensity of the current power system, the carbon carrying capacity of each characteristic factor such as coal, natural gas, and wind power can be calculated. For example, if the weight coefficient of coal is 0.6 and the current carbon emission intensity is X tons / hour, the carbon carrying capacity of coal may be 0.6X tons / hour. According to the real-time carbon emission intensity and the actual carbon carrying capacity of the characteristic factor, the indicator adjustment factor and the carbon emission adjustment strategy are dynamically adjusted to ensure that the emission reduction measures match the actual situation and improve the flexibility and effectiveness of emission reduction.

[0052] S43. The difference between the carbon carrying capacity and the actual carbon carrying capacity determines the indicator adjustment factor, and the indicator adjustment factor acts on the characteristic factor in the corresponding indicator set to obtain the carbon emission adjustment strategy.

[0053] It can be understood that the carbon load of each characteristic factor is compared with its actual carbon load (i.e., the carbon emissions under the current operating state), and the difference is calculated; the index adjustment factor is determined according to the size and positive and negative of the difference. If the difference is positive, it means that the current carbon emissions are lower than the carbon load, and there is room for emission reduction; if the difference is negative, it means that the current carbon emissions exceed the carbon load, and emission reduction measures need to be taken. The index adjustment factor is applied to the corresponding characteristic factor, and the relevant parameters are adjusted or corresponding measures are taken in the index set to form a carbon emission adjustment strategy. Assuming that the actual carbon load of coal is lower than its carbon load, it indicates that there is room for emission reduction in coal use. At this time, the index adjustment factor may include measures such as improving coal utilization efficiency and reducing coal use. On the contrary, if the actual carbon load of wind energy is higher than its carbon load, it may be necessary to increase wind power generation or optimize the operating efficiency of wind power generation equipment. This embodiment takes into account the comprehensive influence of multiple characteristic factors, and through the calculation of carbon load and the determination of index adjustment factors, it realizes the comprehensive optimization of carbon emissions of the power system and promotes the low-carbon and sustainable development of the power system.

[0054] Embodiment 2: A technical solution also provided in the embodiment of the present invention is a carbon emission dynamic adjustment system, such as Figure 4 As shown, including: Data acquisition module 101: acquiring multivariate heterogeneous data associated with the power system, and extracting features from the multivariate heterogeneous data to obtain an index set representing carbon emissions; Model building module 102: constructing a power system carbon flow model according to the topological structure and power flow direction of the power system combined with an indicator set; Data analysis module 103: the power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to real-time power operation data and trend factors; Strategy generation module 104: determines an indicator adjustment factor according to the carbon emission intensity, and the indicator adjustment factor acts on the indicator set to obtain the current carbon emission adjustment strategy.

[0055] In this embodiment, data is collected from various nodes and components of the power system (such as power plants, substations, transmission lines, user terminals, etc.), and features closely related to carbon emissions are extracted; by comprehensively collecting and analyzing the multivariate heterogeneous data of the power system, the carbon emission status of the power system can be more accurately reflected, providing a solid foundation for subsequent modeling and adjustment strategies; the extracted carbon emission indicators are combined with the topological structure and flow direction of the power system to construct a power system carbon flow model, which can simulate the flow and distribution of carbon emissions in the power system and reflect the carbon emission contributions of different nodes and components; through real-time data monitoring and trend factor analysis, the target carbon emission intensity can be adjusted in time to ensure that the carbon emission management strategy is consistent with the actual operation status and policy orientation of the power system; by calculating the indicator adjustment factor and acting on the indicator set, a highly targeted and feasible carbon emission adjustment strategy can be formulated to effectively reduce the carbon emissions of the power system. The dispatching strategy formulated in this scheme takes into account the actual situation and future development trends of the power system, ensuring the effectiveness and sustainability of the strategy. At the same time, by continuously optimizing and adjusting the strategy, the carbon neutrality goal of the power system can be gradually approached and ultimately achieved.

[0056] Embodiment 3: A technical solution provided in the embodiment of the present invention is an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the method for dynamic adjustment of carbon emissions of the power system based on carbon flow modeling are implemented.

[0057] Embodiment 4: A technical solution provided in the embodiment of the present invention is a storage medium, in which computer executable instructions are stored. When the computer executable instructions are loaded and executed by a processor, the steps of the method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling are implemented.

[0058] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0059] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, structures or units, which can be electrical, mechanical or other forms.

[0060] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0061] In addition, each functional unit in the embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0062] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0063] The specific implementation described above is a preferred implementation of the method and system for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for dynamic adjustment of carbon emissions in power systems based on carbon flow modeling, characterized by: The steps include: S1. Acquire multivariate heterogeneous data associated with the power system, and perform feature extraction on the multivariate heterogeneous data to obtain an indicator set representing carbon emissions; S2. Construct a carbon flow model of the power system based on the topological structure and power flow direction of the power system combined with an indicator set; S3, the power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to the real-time power operation data and trend factors; S4. Determine an indicator adjustment factor according to the carbon emission intensity, and the indicator adjustment factor acts on the indicator set to obtain the current carbon emission adjustment strategy.

2. The method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling according to claim 1 is characterized in that: The multivariate heterogeneous data includes power generation side data, power transmission side data, power distribution side data, power consumption side data and meteorological data.

3. The method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling according to claim 2 is characterized in that: Feature extraction of multivariate heterogeneous data to obtain an indicator set representing carbon emissions includes the following steps: Obtain the characteristic factors of multivariate heterogeneous data, and calculate the value between each characteristic factor and carbon emissions through the correlation algorithm; Characteristic factors with values ​​greater than the preset values ​​are regarded as effective characteristic factors, and the effective characteristic factors are aggregated to generate an indicator set.

4. The method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling according to claim 1 is characterized in that: The carbon flow model of the power system is constructed according to the topological structure and power flow direction of the power system combined with the indicator set, including the following steps: S21, constructing node-branch directed boundaries in the power system to generate carbon emission areas, and determining the active power flow distribution of each directed boundary in the power system through a power flow calculation method; S22, determining the branch power flow distribution matrix and the unit injection distribution matrix according to the power flow calculation results; S23, combining the indicator set to obtain the carbon emission intensity of each power generation unit in the carbon emission area in real time to construct a carbon emission intensity vector; S24, calculating the node carbon potential vector through the node active flux matrix, the branch power flow distribution matrix, the unit injection distribution matrix, and the carbon emission intensity vector; S25, calculating the branch carbon flow rate distribution matrix according to the node carbon potential vector and the branch power flow distribution matrix; S26, calculating the load carbon flow rate vector according to the node carbon potential vector and the load distribution matrix; S27. The branch carbon flow rate distribution matrix and the load carbon flow rate vector construct the power system carbon flow model.

5. The method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling according to claim 1 is characterized in that: The power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to real-time power operation data and trend factors; the steps include: S31. Construct a carbon emission trend prediction model based on regional industrial planning and energy structure adjustment strategies; S32. Predict the predicted carbon emissions for a period after the time node through the carbon emission trend prediction model. If the carbon emissions in the period after the time node are greater than the carbon emissions in the period before the time node, the trend factor is determined to be positive; if the carbon emissions in the period after the time node are equal to the carbon emissions in the period before the time node, the trend factor is determined to be zero; if the carbon emissions in the period after the time node are less than the carbon emissions in the period before the time node, the trend factor is determined to be negative; The output of the power system carbon flow model is adjusted according to the trend factor to obtain the target carbon emission intensity.

6. The method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling according to claim 5 is characterized by: The calculation formula for the target carbon emission intensity is: C t '=C t (1+λ t ); Among them, C t is the predicted carbon emission intensity at time t, C t ' is the predicted carbon emission intensity at time t; E t represents the power generation at time t, F i represents the carbon emission factor of the i-th power generation type, P i represents the proportion of power generation of the i-th power generation type; N is the upper limit of the power generation type, λ t is the trend factor at time t; Q t+1 is the carbon emission of the carbon emission area at time t+1, Q t is the carbon emissions of the carbon emission area at time t.

7. The method for dynamic adjustment of carbon emissions of power systems based on carbon flow modeling according to claim 1, 3 or 5, characterized in that: Determine an index adjustment factor according to the carbon emission intensity, and apply the index adjustment factor to the index set to obtain the current carbon emission adjustment strategy; including the following steps: S41, determining the value of each characteristic factor and carbon emission intensity according to the correlation algorithm, and determining the weight coefficient of the characteristic factor corresponding to each value through the normalization algorithm; S42, determining the carbon carrying capacity of each characteristic factor according to the carbon emission amount corresponding to the carbon emission intensity combined with the weight coefficient; S43. The difference between the carbon carrying capacity and the actual carbon carrying capacity determines the indicator adjustment factor, and the indicator adjustment factor acts on the characteristic factor in the corresponding indicator set to obtain the carbon emission adjustment strategy.

8. A carbon emission dynamic adjustment system, applicable to the carbon emission dynamic adjustment method of the power system based on carbon flow modeling as described in any one of claims 1 to 7, characterized in that: Included are: Data acquisition module: acquires multivariate heterogeneous data associated with the power system, extracts features from the multivariate heterogeneous data, and obtains an indicator set representing carbon emissions; Model building module: constructs a carbon flow model of the power system based on the topological structure and flow direction of the power system combined with an indicator set; Data analysis module: The power system carbon flow model determines the target carbon emission intensity of the carbon emission area in response to real-time power operation data and trend factors; Strategy generation module: determines the indicator adjustment factor according to the carbon emission intensity, and the indicator adjustment factor acts on the indicator set to obtain the current carbon emission adjustment strategy.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the method for dynamic adjustment of carbon emissions of a power system based on carbon flow modeling as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores computer executable instructions, and when the computer executable instructions are loaded and executed by the processor, the steps of the method for dynamic adjustment of carbon emissions of a power system based on carbon flow modeling as described in any one of claims 1 to 7 are implemented.

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