297 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 an energy equipment carbon emission scheduling optimization method in an integrated energy system, and the method comprises the steps: determining an energy equipment carbon emission factor, constructing a carbon emission cost function, employing Monte Carlo simulation to generate a carbon transaction price fluctuation scene, and carrying out the carbon transaction price fluctuation scene. The method comprises the following steps: calculating a carbon transaction risk index of equipment in each scene according to a cost function, determining start-stop time by economic benefit optimization, constructing a comprehensive energy system topology network structure, updating a carbon flow path set according to the start-stop time, calculating a comprehensive carbon emission coefficient, establishing an energy flow and carbon flow coupling model between a high emission path and a low emission path, and constructing a comprehensive energy system topology network structure. A scheduling strategy feasible space is obtained through a cooperation strategy, and an optimal cooperation scheduling operation strategy is obtained through optimization of a particle swarm algorithm by taking the minimum carbon emission as a target. The method can effectively reduce the carbon emission / optimize the start and stop of the equipment, improves the economical efficiency and environmental protection property of system operation, achieves the dual goals of energy conservation and emission reduction and benefit improvement, and has good interpretability at the same time.

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.

Provided are a method for tracking carbon flow of a power system, a device and a medium. The method includes: acquiring related data of carbon flow tracking, where the related data include an injected active power sum column matrix of generator sets of all substations to be tracked, an injected carbon flow rate sum column matrix of generator sets of all substations to be tracked, an output power distribution matrix of a branch formed between substations to be tracked, and a power flow proportion distribution matrix of the branch formed between substations to be tracked; acquiring a carbon potential matrix EN of each substation to be tracked; and acquiring a carbon flow rate of each substation to be tracked, a carbon flow rate of the branch, and a carbon flow rate of an output load on the basis of the carbon potential matrix of each substation to be tracked.

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 discloses a carbon flow calculation method based on an electricity-carbon interaction relationship, and belongs to the technical field of carbon emission supervision, and the method comprises the steps: S1, building an electricity-carbon topological structure based on a power distribution network topological structure, and setting a carbon emission factor according to the electricity-carbon topological structure; s2, real-time carbon emission data are collected based on the electric carbon topological structure, and nodes in the electric carbon topological structure are graded and sorted to obtain a ranked node set; s3, calculating the carbon emission according to the real-time carbon emission data, the carbon emission factor and the emission level node set, and determining the tidal current direction of the corresponding carbon emission based on the electric quantity in the real-time carbon emission data; s4, establishing a real-time electricity-carbon topological graph based on the carbon emission, the tidal current direction, the real-time carbon emission data and the electricity-carbon topological structure; the problem that carbon emission data tracing and supervision are difficult due to the fact that the mutual influence relation of different carbon emission sources is not considered in the prior art is solved, carbon flow data are refined, and accurate tracing and supervision of the carbon emission data are achieved.

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 flow calculation method and system especially for an electric power system, and belongs to the technical field of carbon emission of the electric power system. The method comprises the steps of multi-modal data acquisition and fusion, dynamic graph construction, carbon flow distribution optimization based on combination of an adaptive genetic algorithm and reinforcement learning, carbon flow density analysis based on a graph neural network, carbon flow trend prediction based on a deep enhanced memory network, carbon emission flow feedback adjustment and carbon emission flow distribution. Through multi-level feedback control and path optimization, dynamic balance and adaptive distribution of the carbon emission flow of the electric power system are realized, the response efficiency of carbon flow distribution in the electric power system is improved, the stability and rationality of the carbon flow load under different conditions are guaranteed, and the adaptive capacity of dynamic change of the electric power system is improved.

The invention relates to a carbon flow analysis and carbon accounting method and device for an integrated energy system, and the method comprises the steps: building an optimized operation scheduling model of the integrated energy system with the minimum operation and maintenance cost of the integrated energy system as a target, and solving the optimized operation scheduling model to determine a comprehensive optimal operation scheme of the integrated energy system; on the basis of the comprehensive optimal operation scheme of the comprehensive energy system, according to the energy generation, transmission and conversion relation, carbon emission accumulated and distributed corresponding to energy flow on each branch of the comprehensive energy system is calculated step by step from the source through a carbon emission accounting model, and the carbon potential of each node and each branch of the comprehensive energy system is quantified; based on the carbon potential of each node and each branch of the integrated energy system, the carbon emission corresponding to the actual energy consumption behaviors of different consumption units is calculated from the perspective of the demand side. The method can deeply analyze the influence of the carbon emission coupling relationship and the transfer mode on the carbon emission in the energy coupling and conversion process.

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 carbon flow path tracking method and device. The power network diagram is constructed through the real-time power data, then the carbon emission factor and the carbon flow path of each node are calculated, then the carbon emission of each node is determined, the weight of each transmission path is evaluated according to the carbon emission, and finally the line with the maximum carbon emission in the preset monitoring time period is found out. Therefore, the emission reduction strategy is implemented more effectively, the power resource configuration is optimized more effectively, and the problem that the carbon flow path cannot be tracked accurately in the prior art is solved.

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 discloses low-carbon flow-state solidified soil for a seasonal frozen region. The low-carbon flow-state solidified soil comprises soil, a curing agent and water, the dosage of the curing agent is 15-20% of the mass of the dry soil; the water content is 40-60% of the dry mass of the soil and the curing agent; the soil comprises sludge and building residue soil, and the sludge accounts for 20-40 parts by weight, and the building residue soil accounts for 60-80 parts by weight; the curing agent is prepared from the following components in parts by weight: 40 to 60 parts of phosphogypsum, 10 to 15 parts of mixing plant waste slurry, 20 to 40 parts of steel slag, 5 to 10 parts of quick lime, 0.5 to 1.2 parts of glycerol, 0.2 to 0.6 part of hydroxypropyl methyl cellulose and 1 to 1.5 parts of water reducing agent; according to the invention, various technical problems of insufficient anti-freezing performance, unstable fluidity, long setting time, insufficient early strength, large shrinkage deformation, easy generation of segregation and bleeding and the like of the existing fluid-state solidified soil are solved, and meanwhile, more low-carbon environment-friendly raw materials are introduced, so that multi-aspect improvement of the performance of the fluid-state solidified soil and maximization of environment-friendly benefits are realized; and a more reliable and efficient solution is provided for earthwork such as foundation pit backfilling.

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.

An electric power system source network load Pareto optimal carbon responsibility calculation method based on carbon emission flow belongs to the technical field of carbon emission, and comprises the following steps: analyzing a topological structure in an electric power network, establishing an electric power system load flow calculation model to solve a load flow distribution condition in the electric power system, obtaining load flow data, and substituting the load flow data into carbon flow calculation to obtain a load flow calculation model; and obtaining the carbon flow intensity, the carbon flow rate and the carbon potential of each node, and introducing a carbon date to solve the carbon responsibility distribution of each node. The method comprises the following steps: analyzing the carbon emission condition of a system, considering the geographic position of each node and other factors, introducing two sections of objective functions of minimum power generation cost and minimum carbon date, carrying out multi-objective function optimization solution, and constructing an evaluation function by adopting a Pareto optimal method to obtain an optimal solution. According to the method, a feasible scheme reference can be provided for carbon responsibility distribution of the power system under the low-carbon background.

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 discloses a carbon flow monitoring and accounting method based on the Internet of Things. The method specifically comprises the following steps: S1, collecting power flow information of a power system; s2, performing flow calculation on the power network to obtain an active power flow matrix F of the whole power network; s3, performing hierarchical iterative calculation on the power grid carbon flow density, and gradually obtaining carbon flow data of the whole power grid; s4, arranging the carbon flow data to obtain a carbon flow matrix C, and providing a final visual result; s5, collecting carbon emission data; s6, establishing a carbon flow neural network model; s7, training the carbon flow neural network model; and S8, checking the carbon emission of the whole system by using the carbon flow neural network model. According to the invention, an Internet of Things data acquisition technology and a data analysis visualization technology are mainly utilized, the flow path of the carbon flow in the whole power network is visually displayed for a power system manager, and low carbon and greenization of the whole system are helped to be realized.

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.

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.