258 results about "Carbon flow" patented technology

The invention discloses a carbon emission monitoring and predicting method and device based on digital twinning and a medium, and relates to the technical field of park carbon emission monitoring. The method comprises the steps of collecting multi-source data of a target area in real time through a multi-source heterogeneous sensor; performing dynamic anomaly detection on the multi-source data, and triggering cross-data type relevance verification when data anomaly is detected, so as to select a repair strategy according to a verification result; dynamically binding the repaired data with the space coordinates of the BIM model, and constructing a three-dimensional digital twinborn body containing an equipment-level carbon flow path; performing rolling prediction on the carbon emission trend based on a preset hybrid prediction model, and executing a control instruction from carbon emission source positioning to equipment parameter adjustment step by step when a predicted value triggers a multi-stage early warning logic; and after the control instruction is executed, the control effect is verified through real-time feedback of the three-dimensional digital twinborn body, and root cause backtracking analysis is started when an expectation is not reached.

The invention discloses a carbon neutralization target-oriented electricity-carbon-green certificate multi-target scheduling optimization method and system, and relates to the technical field of carbon neutralization control, and the method comprises the following steps: obtaining first data of a target energy storage unit, constructing a constraint generation network, and dynamically updating an energy storage operation constraint domain based on a dynamic time warping algorithm; based on the updated operation constraint domain, establishing an electricity-carbon coupling model, extracting a nonlinear mapping relation between an electric energy output behavior and carbon emission intensity, and outputting a dynamic incidence matrix through a multi-dimensional time sequence predictor; constructing a carbon flow distribution topological graph according to the dynamic incidence matrix, and obtaining an optimal carbon flow path through a Dijkstra algorithm; based on the operation constraint domain and the optimal carbon flow path, establishing a multi-subject collaborative decision-making mechanism, solving a multi-target unit equilibrium solution by adopting a distributed alternating direction multiplier method, and generating a global scheduling instruction set; according to the method, a carbon flow topology and dynamic constraint domain cooperation mechanism is constructed, so that efficient solving of a multi-main-body carbon response optimization scheduling problem is realized.

The invention discloses an active power distribution network interactive carbon reduction decision-making agent construction method based on carbon flow distribution, and relates to the technical field of active power distribution network low-carbon scheduling. According to the method, a carbon flow distribution dynamic tracking model is constructed, node-level carbon emission intensity is quantified, an intelligent agent framework based on a Markov decision process is designed, a state space including real-time carbon flow, new energy output and load fluctuation and action spaces including unit adjustment, an energy storage strategy and the like are defined, and the real-time carbon flow, new energy output and load fluctuation are determined. And optimizing a carbon emission minimization target through a reward function, training an intelligent agent by adopting a reinforcement learning algorithm fusing physical constraints and historical data, and finally generating a multi-target collaborative optimization decision by combining offline pre-training and online fine tuning strategies, so as to synchronously optimize new energy consumption, line loss and economy. According to the invention, the carbon emission intensity of the system is effectively reduced, and a technical support is provided for constructing a novel low-carbon and high-elasticity power system.

The invention relates to a low-carbon park comprehensive energy system multi-agent collaborative carbon responsibility allocation method, which comprises the steps of collecting multi-source data of a park, and performing multi-energy flow optimization scheduling by taking the minimum total operation cost of a comprehensive energy system as a target; constructing a dynamic carbon flow distribution matrix by utilizing a bidirectional carbon flow dynamic correction model considering carbon flow transfer caused by an energy storage charging and discharging process; a load carbon responsibility decoupling distribution model is adopted to decouple the total load into a plurality of sub-load categories, and different carbon responsibility distribution coefficients are given to each category of sub-load basis; the method comprises the following steps: performing multi-agent collaborative optimization by taking each benefit party in a park as a game participant, taking a Nash bargaining as a collaborative optimization framework, taking maximization of utility gains of all participants as an objective function, and taking a Shapley value as a carbon responsibility distribution basis, and obtaining a carbon responsibility distribution scheme of each agent in combination with a user carbon responsibility distribution coefficient. Compared with the prior art, the method ensures fairness, efficiency and operability of a carbon responsibility allocation scheme.

The present invention relates to the technical field of carbon emission monitoring. Disclosed is a method for constructing a carbon assessment classification indicator system of a zero-carbon power supply station taking carbon flow analysis into consideration. The method comprises: acquiring an output result of a power network steady-state carbon flow model of a zero-carbon power supply station; on the basis of the output result and a goal of the zero-carbon power supply station, obtaining carbon assessment classification indicators of zero-carbon power supply; assigning weights to the carbon assessment classification indicators by means of a frequency statistics method; and integrating weighted carbon assessment classification indicators, so as to obtain a carbon assessment classification indicator system of the zero-carbon power supply station. In the present invention, multidimensional comprehensive evaluation is performed taking into account carbon flow analysis, thereby assessing the zero-carbon level of a power supply station more comprehensively and more accurately. Moreover, by means of carbon flow analysis, carbon flow and transformation processes within the power supply station can be precisely tracked, so as to assess the carbon emissions of the power supply station more accurately, and thus an assessment result is more comparable and operable.

The invention discloses an integrated energy system low-carbon scheduling method considering energy-carbon coupling, and the method is based on a carbon emission flow theory, is combined with the strong fitting capability of a neural network, proposes a carbon flow constraint learning method, converts a complex mapping relation between power flow and carbon flow into mixed integer linear constraint, and achieves the low-carbon scheduling of an integrated energy system. And effective embedding of the carbon flow constraint in the optimization model is realized. Meanwhile, in order to reduce the structural complexity of the neural network, a sparse training strategy is introduced, the model parameter scale is effectively compressed, a ReLU activation function is linearized through an improved large-M method, and a cut plane constraint is introduced to gradually tighten a feasible region, so that the solving efficiency of an optimization model is remarkably improved. And finally, embedding the carbon flow constraint model into the optimal scheduling problem of the integrated energy system, exciting the carbon emission reduction consciousness of the load side, and promoting the load side to perform low-carbon energy consumption adjustment by guiding the demand response behavior of the load side based on the carbon signal of the load side, thereby realizing low-carbon scheduling under energy-carbon coordination and reducing the overall carbon emission level of the system.

The invention discloses a space-time carbon emission prediction method, system and device and a medium. The method comprises the steps of obtaining load historical sequence data; carrying out load flow calculation by adopting a first load flow analysis algorithm to obtain a load flow calculation result; based on the load flow calculation result, carbon flow analysis is carried out through power transmission between the nodes, and the carbon emission coefficient of each node is obtained; constructing a time sequence diagram neural network model, and performing preliminary training on the time sequence diagram neural network model; performing hyper-parameter optimization on the preliminarily trained time sequence diagram neural network model by adopting a first hyper-parameter optimization technology to obtain an optimized time sequence diagram neural network model; and inputting load sequence data and unit power output data at the current moment into the optimized sequence diagram neural network model, and carrying out online prediction on a carbon emission coefficient. According to the method, the real-time prediction of the carbon emission coefficient is realized based on the characteristic learning of the historical operation and monitoring data of the power system.

The invention relates to a production workshop carbon flow twinborn mapping method, and belongs to the technical field of carbon emission optimization. The method comprises the steps of collecting production workshop data in real time to perform multi-modal data fusion; constructing a carbon flow dynamic accounting mechanism model, and calculating the real-time carbon emission intensity of the process; constructing a carbon flow intensity tensor model, extracting multi-granularity carbon flow features based on the carbon flow intensity tensor model, and realizing real-time digital twinborn deduction of carbon flow propagation by using an intelligent prediction algorithm; establishing a differential equation to describe double-flow real-time interaction of the carbon flow and the value flow, constructing a carbon value incidence matrix, and performing carbon flow value analysis; and on the basis of the carbon value incidence matrix, a space-time carbon chain-oriented cooperative adjustment strategy is dynamically generated through a multi-objective optimization algorithm, a double-layer topological optimization model is constructed, and a closed-loop feedback mechanism is introduced to drive the economic sustainability of the control strategy in an industrial environment. The workshop carbon footprint can be displayed in real time, the workshop carbon emission condition can be truly reflected, and the carbon emission data can be analyzed and optimized in time.

The invention relates to the field of low-carbon electric power technology and electric power system optimization scheduling, in particular to an electric power system non-iterative low-carbon scheduling method and system based on carbon emission flow calculation and power flow decomposition, and the method comprises the steps: obtaining various types of input data in an electric power system, constructing an electric power carbon emission flow model based on power flow direction decomposition, a node carbon emission factor real-time calculation method and a carbon flow balance model suitable for non-iterative optimization solution are obtained; and finally, establishing a low-carbon dispatching optimization model of the power system, carrying out calculation and solution, and finally obtaining output results such as a unit output plan, a system power flow result and a node carbon emission factor. By establishing an electric power carbon emission flow model, synchronous calculation of a power flow equation and a carbon flow equation is achieved, the problem that traditional carbon flow calculation depends on a power flow calculation result is solved, load node carbon emission constraint is embedded in an optimized dispatching model, and a new technical path is provided for an electric power system to achieve refined low-carbon dispatching.

The invention discloses a power distribution network carbon emission metering and responsibility distribution method and device and a medium, and relates to the technical field of smart power grids. The method comprises the following steps: collecting multi-source carbon emission data in real time, and preprocessing the multi-source carbon emission data to obtain standard carbon emission data; inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain a dynamic carbon factor of the thermal power generating unit, and combining the dynamic carbon factor of the thermal power generating unit with a carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; performing load flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch load flow distribution matrix, and combining the dynamic carbon intensity vector, the node active flux matrix and the branch load flow distribution matrix to obtain the total carbon potential of each node on the load side; and based on the total carbon potential and the real-time load distribution matrix, calculating the carbon flow rate distributed to each node on the load side so as to obtain the carbon emission responsibility of each node on the load side.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention discloses a park carbon emission management method, equipment and a medium, and relates to the technical field of carbon emission intelligent monitoring, accounting and management. The method comprises the following steps: acquiring multi-source data related to carbon emission of a park; according to the multi-source data, calibrating a carbon emission factor corresponding to the park to obtain a dynamic calibration factor; performing carbon flow simulation on the carbon emission of the park based on the twins of the park and the dynamic calibration factor to obtain a simulation result of at least one emission reduction scheme; according to a simulation result, generating a zero-carbon path plan corresponding to the park; and under the framework of zero carbon path planning, based on a reinforcement learning algorithm, generating a carbon emission strategy corresponding to the park, and executing the carbon emission strategy. Therefore, precise park carbon emission management can be realized.

The invention relates to the technical field of carbon footprint management, in particular to a carbon footprint accounting method considering a whole life cycle, which comprises the following steps: analyzing life cycle path assessment influence and compressing to generate a path serial number sequence, reconstructing, assessing and transmitting identification abnormity according to serial numbers and reconstructing, identifying turning matching factors to obtain an adaptive set, and calculating a carbon footprint accounting result according to the adaptive set. And positioning heat loss inversion critical contrast monitoring to obtain accounting verification information, and collecting, layering and summarizing the carbon amount according to verification conversion. According to the method, through comprehensive utilization of flow path information, measurement of emission intensity and activeness and path screening are achieved, analysis of node carbon transfer characteristics is achieved, the accuracy of carbon flow judgment is improved, matching of operation sections and factor conditions is combined, identification and truncation of heat loss characteristics in an energy link are adopted, the reasonability of an inversion range is enhanced, and the accuracy of carbon flow judgment is improved. Through comparison of an emission monitoring result and energy input, verification of accounting data is enhanced, cross-level conversion and collection are combined, and consistency and integrity of carbon emission mapping are guaranteed.

The invention discloses a carbon emission prediction and emission reduction strategy optimization method and system, and belongs to the field of power data processing, and the method comprises the steps: firstly building an accurate carbon flow incidence relation through the power grid topology and power flow data through employing an upstream apportionment principle, and generating carbon flow intensity time series data of spatial and temporal distribution; and fusing operation data of the power system, constructing a multi-dimensional time sequence data set, and extracting spatial-temporal characteristics of the carbon emission intensity by using deep learning. A multi-time-scale nonlinear mapping relation network is established based on the characteristics, and accurate carbon emission intensity prediction from the minute level to the day level is achieved. And finally, by taking a prediction result as a constraint, constructing an optimization model with minimum total carbon emission and minimum operation cost as targets, and generating an optimal power generation scheduling and load adjustment strategy. According to the method, accurate tracing and multi-time scale prediction of carbon emission are realized, and the accuracy and efficiency of low-carbon optimization operation decision making of the power grid are effectively improved.

The invention discloses a factory carbon emission monitoring method and system based on an electricity-carbon calculation model, relates to the technical field of carbon emission monitoring and energy management, and is used for solving the problem that the real-time performance and accuracy of carbon emission monitoring are reduced due to the fact that carbon emission results are mostly presented as coarse-grained indexes. An initial carbon flow distribution matrix is constructed by collecting carbon concentration and electric energy consumption data of distributed energy access points in a plant, a carbon flow model is generated based on a digital twinborn body, a carbon flow weight is dynamically corrected by combining a Bayesian inference algorithm, the total carbon emission amount of the plant is calculated, and a monitoring result is output. According to the method, the spatial precision and the real-time correction capability of carbon emission tracking can be improved, early warning of the carbon emission trend of the key node is realized, and data support and decision basis are provided for a low-carbon operation strategy of a factory.

The invention relates to a microgrid operation method and system based on carbon flow tracking and multi-target collaborative optimization, and the method comprises the following steps: S1, carrying out the high-precision, real-time and synchronous data collection and processing, and providing complete and reliable data input for carbon flow tracking and optimal scheduling; s2, according to the data acquired in the S1, realizing carbon flow dynamic tracking through a directed graph, quantifying the carbon emission intensity of each node in the microgrid, introducing a carbon label technology, tracking the carbon emission source of each kilowatt-hour, and providing a data basis for optimization; s3, by taking minimization of carbon emission and operation cost as targets, a long-term scheduling plan is generated for the equipment, and multi-target collaborative optimization is realized; and S4, further optimizing the energy storage system on the basis of realizing the multi-target collaborative optimization in the step S3 so as to improve the demand response speed. According to the invention, the carbon emission reduction rate and the demand response speed during the operation of the industrial micro-grid can be effectively improved.

The invention discloses an electric carbon coupling carbon flow calculation method, system and device and a medium. The method comprises the steps of obtaining a real-time power curve of a power transmission line and corresponding current, resistance and power factor multi-source operation data; segmenting the real-time power curve according to a time window, and extracting a power dynamic feature set in each time period based on a current change trend; calculating the energy loss of the power transmission line in each time period according to the multi-source operation data, and generating a carbon emission factor calibration result in combination with the difference between a reference carbon emission factor and the energy loss; determining a carbon flow attribution result of each power generation node in each time period based on the carbon emission factor calibration result and a power factor curve of the power generation node; and calculating a comprehensive carbon flow value of each power generation node, and summarizing to obtain a carbon flow calculation result of the power system. According to the method, the real-time performance, the accuracy and the physical consistency of carbon flow calculation are remarkably improved.

The invention discloses a low-carbon scheduling method, system, equipment and medium for an integrated energy system, and belongs to the technical field of low-carbon scheduling, and the method comprises the steps: obtaining power network data, thermal network data and natural gas network data of the integrated energy system, establishing a multi-energy-flow carbon flow dynamic model according to the power network data, the thermal network data and the natural gas network data; calculating the time-varying node carbon intensity of each node based on the multi-energy-flow carbon flow dynamic model; calculating carbon flow betweenness centrality according to the time-varying node carbon intensity and a network topology structure, and identifying a carbon flow propagation key node through the carbon flow betweenness centrality; and establishing a low-carbon optimal scheduling model based on the time-varying node carbon intensity, and determining a low-carbon scheduling scheme of the integrated energy system in combination with the low-carbon optimal scheduling model and the carbon flow propagation key node. The method has the beneficial effects that the carbon emission of the comprehensive energy system can be effectively reduced, and meanwhile, the increase amplitude of the operation cost is reduced.

The invention relates to the technical field of power system carbon metering, in particular to a source-side power carbon space-time coupling power carbon emission tracing method and device and a medium, and the method comprises the steps: constructing a multi-dimensional reference model through the fusion of power grid topology, generator fuel characteristics and real-time output data; decoupling electric energy transmission and carbon propagation paths based on an admittance matrix, and calculating the spatial distribution of apportioned carbon emission through load flow; carbon emission factors are corrected in real time to capture transient characteristics, and limitation of static factors is broken through; fusing the power flow distribution and the dynamic factor, and calculating direct / indirect carbon emission contribution of a load node by adopting a power flow proportion recursive mechanism; real-time data are connected to generate a dynamic carbon flow distribution map and a traceability decision report, a key carbon conduction path and a responsibility subject are accurately identified, a verifiable decision basis is provided for low-carbon transformation, carbon transaction and scheduling optimization, and the timeliness and operability of carbon management are improved.

The invention belongs to the technical field of gas compressors, and discloses an electric power carbon emission accurate measurement and optimization method based on artificial intelligence, and the method comprises the steps: data collection and processing: collecting historical power generation, load, tidal current and carbon emission data, and carrying out the cleaning and standardization; training a dynamic carbon emission factor model, training a carbon emission factor prediction model of each thermal power generating unit by using historical data, and predicting by using the carbon emission factor prediction model to obtain a dynamic carbon emission factor; and training a GNN carbon flow model, and training the GNN model to estimate node carbon intensity by using the power grid data and the dynamic carbon emission factors. According to the invention, two core problems of inaccurate measurement and difficult optimization of the carbon emission of the electric power system are solved. Accurate carbon perception is realized through a dynamic carbon emission factor and a GNN carbon flow model, and an optimal scheduling strategy considering economy, safety and low carbon is searched in a complex power grid environment through reinforcement learning.

The invention discloses a building industry multi-subject collaborative full life cycle carbon emission management system and method, and relates to the technical field of building industry carbon emission management. The system comprises a carbon perception and data collaboration module, a carbon simulation and deduction module, a carbon optimization and collaborative regulation and control module, a carbon accounting and auditing traceability module and a carbon decision and knowledge generation module. Multi-source heterogeneous data is converted into a carbon flow unit with time, space and responsibility subject attributes, data checking, locking and compliance sharing are achieved by means of a multi-subject data collaboration pool, carbon data barriers among all responsibility subjects are broken through, the problems of fragmentization of carbon data collection and lack of collaboration are solved, and the data collection efficiency is improved. And meanwhile, the carbon accounting and audit traceability module completes multi-dimensional accounting based on the carbon flow unit, and establishes a digital carbon chain pointing to an original data source and a responsibility subject, so that full-link traceability of carbon emission data is realized.

The invention discloses a power system carbon emission dynamic traceability and evaluation method, system and device based on life cycle and topological entropy fusion and a medium, and belongs to the technical field of power system low-carbon operation and carbon emission evaluation, and the method comprises the steps: collecting carbon emission data of different stages; establishing a stage set, calculating the emission of each stage, and solving a life cycle carbon entropy; abstracting a power system into a weighted directed graph, calculating a branch carbon flow rate based on the weighted directed graph, and calculating a graph theory carbon entropy based on node inflow; introducing a weight parameter, fusing the life cycle carbon entropy and the graph theory carbon entropy, and defining a fusion entropy; under a multi-period operation condition, calculating a time evolution sequence based on the fusion entropy, and defining a sensitivity coefficient; and generating a visual carbon flow map containing node carbon potential distribution, a branch carbon flow rate, a carbon entropy evolution curve and a responsibility allocation matrix. According to the method, dynamic conduction and node-level tracking of carbon emission along the electric power flow are realized, and the complexity and balance of carbon emission space distribution are quantified through an entropy theory.

The invention relates to an inspection and traceability method, device and equipment for an electric power carbon emission metering system, and the method comprises the steps: setting the power and carbon flow density of a power generation side unit, the power grid topology and the tidal current power of each metering point, calculating and issuing a standard electric carbon factor to a standard electric carbon meter according to the power, and controlling a power source to output according to preset power. Driving a standard electric carbon meter and an electric energy meter to continuously work, calculating a real-time electric carbon factor and an electric carbon quantity measurement value of each metering point on the basis of data such as a tidal current power measurement value of each metering point after the work is finished, comparing the real-time electric carbon factor and the electric carbon quantity measurement value with a standard value to obtain an electric carbon quantity and electric carbon factor measurement deviation, and judging whether a metering error exceeds a limit or not according to the electric carbon quantity and electric carbon factor measurement deviation; the inspection of the metering result is realized; if an over-limit abnormal metering point exists, further recording multi-time counting data, calculating relative standard uncertainty of the electrical carbon quantity and the electrical carbon factor, and determining a metering point to be calibrated in combination with abnormal metering point information so as to realize traceability; according to the method, the accuracy and reliability of electric power carbon emission metering are remarkably improved.

The invention provides an electric power system carbon potential tracking and predicting method based on physical and data dual drive. The method comprises the following steps: constructing a time sequence characteristic data set; constructing a source-side unit double-layer collaboration map, and generating a normalized adjacent matrix; constructing a physically guided source side graph neural network model, and outputting a predicted source side dynamic carbon emission factor and a node carbon injection amount sequence; constructing a high-dimensional input tensor through a space-time diagram attention mechanism; constructing a whole-network space-time diagram neural network model to realize dynamic tracking of carbon potential; the method comprises the following steps: introducing a Kirchhoff carbon flow conservation law as a physical regularization term in a space-time law deduction process, constructing a mixed loss function containing the physical regularization term, and executing an optimal sentinel mechanism in a back propagation process of whole-network space-time diagram neural network model training, and finally, outputting a dynamic carbon potential prediction result of the load side after physical verification. According to the invention, the precision and credibility of carbon potential prediction can be improved.

The invention discloses a power distribution network multi-type flexible resource low-carbon scheduling method based on carbon emission flow, and relates to the technical field of power system low-carbon scheduling, and the method comprises the steps: constructing a scheduling time period set of a discrete time domain, and building an equivalent lossless network model; power flow calculation is carried out, and a node-branch carbon flow mapping relation is established; according to the carbon emission intensity of the unit, the carbon flow rate and node carbon emission flow of each branch are calculated; performing differential approximate calculation on the node injection power to obtain a carbon marginal coefficient of each node; generating a time-varying carbon price signal of each node in combination with an external carbon price reference; constructing a weighted combination objective function based on the node time-varying carbon price signal; and solving a weighted combination objective function and constraint conditions by adopting a distributed multi-agent collaborative optimization strategy. According to the invention, collaborative scheduling of multiple types of flexible resources under a low-carbon target is realized, and the carbon emission reduction effect is improved.

The invention provides a carbon footprint tracing method and device suitable for a copper product, and relates to the technical field of carbon tracing. The method comprises the following steps: acquiring data of a copper product in a full life cycle, calling a preset emission factor library and an industry carbon intensity database, and calculating carbon emission of the copper product in the full life cycle by adopting a mixed life cycle evaluation method to obtain a carbon emission accounting result of each stage; based on the carbon emission accounting result of each stage, combining with a multi-region input-output model, constructing copper product supply chain carbon emission distribution throughout the country, and quantifying an inter-provincial implicit carbon flow path; using the structure decomposition analysis model to decompose the driving factors; and performing time sequence prediction and supply chain high-carbon node identification on the carbon emission accounting result and the trans-provincial carbon distribution data by using a deep learning model to obtain a carbon traceability result. The method is used in the carbon footprint tracing process of the copper product, and solves the technical problem that national scale copper product carbon flow tracking cannot be carried out in the prior art.

The invention discloses a carbon flow calculation method and system considering full life cycle carbon emission of a power supply side, and the method comprises the steps: carrying out the integrity check and format check of power data, obtaining the operation parameters of a power system, carrying out the power flow calculation of the power system, and obtaining the voltage amplitude and phase angle of each node, obtaining active power and network loss of each branch; determining carbon emissions of an equipment production stage, a material transportation stage, an engineering construction stage, a power station operation and maintenance stage and a decommissioning stage, and establishing a power supply side full life cycle carbon emission model based on the emissions of all the stages; and based on the voltage amplitude and the phase angle of each node, the active power and the network loss of each branch and a power supply side full-life-cycle carbon emission model, establishing a power system carbon flow model considering power supply side full-life-cycle carbon emission. More accurate metering and tracking of the carbon flow of the electric power system can be realized, and certain practical benefits are achieved.

The invention provides a product full production cycle carbon footprint tracing evaluation method and system, and relates to the technical field of carbon footprint tracing, and the method comprises the steps: obtaining production activity data of each link of a product full life cycle, constructing a directed weighted network model, and executing path traversal, extracting a traversal path of which the path carbon intensity meets a preset path carbon intensity condition as a first carbon flow path, obtaining the node net carbon emission of each node, and determining the apportionment amount of each node to the third-range indirect emission; and matching the production activity data of the link corresponding to each node with the emission parameters in the carbon emission database to obtain the link carbon emission intensity of each link, and determining the link carbon emission of each link based on the link carbon emission intensity and the node net carbon emission. According to the method and the device, a small-flow high-carbon emission intensity path can be identified, and the range three-emission accounting accuracy is improved to the level of tracing to a specific supply chain node through a double-layer mapping relation of link level microscopic accounting and node level macroscopic tracing.

The invention relates to the technical field of industrial carbon flow monitoring and early warning, and discloses an industrial multi-stage carbon flow real-time monitoring and leakage prediction method, which comprises the following steps of: arranging a plurality of sensors in a carbon emission pipeline at the joint of power equipment and the carbon emission pipeline; historical electricity consumption of the power equipment and carbon emission data collected by each sensor at the same time are collected; constructing a carbon emission prediction model, and training the carbon emission prediction model by taking the historical electricity consumption of the power equipment and the carbon emission data collected by each sensor as a training set; and obtaining the electricity consumption of each power device in a certain period of time, and inputting the electricity consumption into the trained carbon emission prediction model to obtain the predicted carbon emission collected by each sensor in the period of time. According to the method, the carbon emission prediction model is obtained through training based on the electricity consumption and all sensor data at the same moment, and electricity-carbon associated data are fully fused, so that the trained model can perform linkage analysis and prediction on the electric power data and the carbon emission data.

The invention discloses a coal-fired power plant carbon emission intelligent control method and system based on machine learning, and relates to the technical field of coal-fired power plant carbon emission, and the method comprises the four steps: building a three-level sensing network to collect carbon flow data, importing various parameters to construct a carbon flow digital twinborn body, building a real-time mapping link, and obtaining twinborn body boiler data; establishing a solid waste related mapping model, and calculating and outputting a coal blending combustion proportion through an optimal solution; predicting the carbon release amount within 5 minutes in combination with boiler data and a blending combustion ratio, constructing a carbon capture agent model, and judging whether elastic adjustment is triggered or not through an improved Sarsa algorithm game; and analyzing a quota consumption rule based on prediction data, predicting a carbon price, optimizing multiple targets and analyzing a Pareto optimal strategy. According to the method, full-chain collaborative management and control are realized, the carbon emission control precision and intelligent level are improved, environmental protection standard reaching, quota management and cost control are balanced, and low-carbon and efficient operation of a power plant is assisted.