543 results about "Emission intensity" patented technology

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention relates to an urban road carbon emission prediction and optimization control method and system, and belongs to the technical field of road traffic carbon emission monitoring, and the method comprises the steps: obtaining real-time monitoring data of a to-be-detected road, and carrying out the preprocessing; performing space-time alignment on the preprocessed real-time monitoring data, extracting dynamic traffic features and static road features, and generating a space-time feature matrix; inputting the spatial-temporal characteristic matrix into a pre-trained carbon emission prediction model to obtain a prediction result matrix of the to-be-detected road; on the basis of the prediction result matrix, identifying a road grid of which the carbon emission intensity prediction value exceeds a preset threshold value as a high emission area; acquiring real-time traffic state data of the high emission area; and based on the real-time traffic state data and the prediction result matrix of the high emission area, constructing a multi-objective optimization function and carrying out solving to obtain an optimization control strategy of the high emission area. According to the invention, traffic carbon emission optimization control and real-time traffic condition synchronization can be 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 provides a carbon emission accounting method and system based on an artificial intelligence technology, and belongs to the field of carbon emission. According to the technical scheme, the method comprises the following steps: constructing a sensor network of an equipment operation state, obtaining real-time operation data, and preprocessing the real-time operation data through an edge computing node; establishing a dynamic prediction model of emission factor boundary parameters based on the real-time operation data; according to the dynamic prediction model, constructing a carbon emission propagation map, analyzing the spatial and temporal distribution trend of carbon emission intensity, and identifying the position of an abnormal emission source; according to the abnormal emission source position identification result, carbon emission trend prediction is executed, and emission early warning response is triggered in combination with a preset threshold value. The method has the beneficial effects that dynamic prediction of carbon emission factors, identification of emission abnormal sources and trend early warning response are realized, the real-time modeling and space-time propagation analysis capabilities of the system on carbon emission behaviors are enhanced, and the intelligent level and response efficiency of emission management are effectively improved.

A power plant full life cycle carbon footprint monitoring method based on an intelligent carbon tube platform relates to the field of electric digital data processing, and comprises the following steps: acquiring unit load data and fuel consumption data, and detecting desulfurization outlet gas according to a multi-layer infrared detection structure to obtain gas concentration data; integrating the gas concentration data, the unit load data and the fuel consumption data to obtain a synchronous data set; and inputting the synchronous data set into a carbon emission dynamic measurement and calculation model to obtain a carbon emission trend prediction result of a preset future time period, inputting the synchronous data set into a multi-target optimization model, and carrying out optimization calculation by taking carbon emission intensity minimization, carbon quota utilization rate optimization and power generation cost minimization as targets to obtain an optimization scheme. And generating a carbon emission monitoring result based on the carbon emission trend and the optimization scheme, and performing unit start-stop and system load adjustment control according to the carbon emission monitoring result. By implementing the method, the monitoring precision of power plant carbon footprint monitoring can be improved.

The invention discloses a carbon emission real-time regulation and control method and system based on multi-source data fusion and AI decision, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes, in the method, real-time production data is collected through Internet of Things equipment, and the real-time carbon emission intensity is calculated in combination with a carbon accounting engine. And constructing an AI optimization layer comprising a process knowledge graph, a long-short-term memory network and a reinforcement learning agent, converting process constraints into mathematical boundary conditions, predicting energy demands, carrying out iterative optimization by taking carbon emission intensity minimization as a target, generating a dynamic scheduling strategy, and realizing real-time adjustment of operation parameters of production equipment. The method and the device are used for improving the accuracy of carbon emission monitoring data on production scheduling and reducing the risk of high-carbon-intensity production caused by improper time period selection of enterprises.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

The invention discloses a regional integrated energy system two-stage optimization method considering # imgabs0 # efficiency and stepped carbon transaction. The method comprises the following steps: constructing a regional integrated energy system model based on an energy concentrator model; establishing a multi-objective optimization model considering the efficiency of the equipment # imgabs1 #, and incorporating the multi-objective optimization model into a stepped carbon transaction mechanism; constructing an RIES two-stage optimization model which comprises an upper-layer capacity configuration model and a lower-layer scheduling model; solving the established RIES two-stage optimization model, solving a Pareto leading edge of equipment capacity configuration by an upper-layer capacity configuration model by adopting an NSGA-III algorithm, and generating a plurality of groups of candidate schemes; the lower-layer scheduling model obtains an optimal scheduling strategy corresponding to each candidate scheme through a solver; and objective weighting is carried out based on an entropy weight method to determine index weight coefficients such as economy, carbon emission intensity and # imgabs2 efficiency, a TOPSIS method is adopted to carry out full-dimension quantitative evaluation on a Pareto leading-edge solution set, and an optimal capacity configuration scheme and optimal scheduling data of the system are selected. According to the method, the system energy efficiency can be improved, the carbon emission intensity is reduced, and the equipment configuration redundancy is optimized.

The invention provides a construction carbon emission dynamic simulation and optimization decision-making method based on BIM and digital twinning, relates to the technical field of building construction carbon emission management and control, and solves the problem of limitation of an existing management and control process in the aspects of accounting precision, response speed, emission reduction effect and economical efficiency. The method comprises the following steps: firstly, establishing a multi-dimensional BIM model, constructing a digital twinborn body linked with a physical construction site in real time based on the model, and mapping real-time energy consumption and material data; and then monitoring and calculating the real-time carbon emission intensity of the whole construction process in combination with a dynamic carbon emission factor library, further performing multi-target optimization solution by adopting a mixed integer programming algorithm based on the intensity value and an optimization decision, outputting a low-carbon construction optimization scheme, and guiding field execution. Through BIM, digital twinning and dynamic optimization full-chain technology integration, real-time monitoring, accurate accounting, intelligent optimization and closed-loop management and control of building construction carbon emission are smoothly realized.

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 present invention provides: a steel sheet which contains Ni, Cu and Sn, and has excellent chemical conversion treatability; and a component which includes said steel sheet.　A steel sheet and a component including the same according to the present invention are characterized by having a chemical composition containing, in mass %, 0.010-1.000% of Ni, 0.010-1.000% of Cu, and 0.003-1.000% of Sn, and by the emission intensities of Ni, Cu, and Sn in the surface layer of the steel sheet as measured by high-frequency glow discharge optical emission spectrometry satisfying the following relationships.　Ni: Surface layer maximum intensity / Bulk intensity ≤ 1.5 Cu: Surface layer maximum intensity / Bulk intensity ≤ 1.3 Sn: Surface layer maximum intensity / Bulk intensity ≤ 15

The invention discloses a zero-carbon park inter-industry information fusion operation and maintenance system, particularly relates to the technical field of carbon emission dynamic management and control, and is used for solving the problem of carbon accounting distortion caused by state deviation of an existing static carbon emission model and a physical system. The method comprises the following steps: establishing an initial carbon emission model containing energy efficiency reference parameters in a design stage, adopting multi-working-condition dynamic calibration in a construction stage to obtain an operation boundary characteristic spectrum and synchronously acquiring a thermodynamic trace, and identifying topological fragile nodes based on Lyapunov exponential stability analysis and optical flow method thermal gradient analysis; quantizing a carbon emission cascade conduction effect by combining an equipment energy topology network, generating a dynamic carbon emission model correction instruction to update an energy efficiency parameter, and finally executing double-instruction collaborative management and control in an operation and maintenance stage; a complete-cycle carbon data flow closed loop is realized, and through multi-source information fusion and conduction chain modeling, the effect of continuously reducing the carbon emission intensity of the park is achieved.

The invention discloses a regional carbon emission prediction method and system based on carbon emission monitoring and electronic equipment, and relates to the technical field of carbon emission prediction, and the method comprises the steps: collecting regional discrete point emission data through a multi-modal sensing network, and generating a regional-level continuous flow field; performing feature decoupling and quantization on different emission source types to generate a source stream feature vector matrix; reconstructing a continuous space-time emission field based on the source stream feature vector matrix, identifying a conduction path, and dynamically binding with field features along the path to generate a dynamic field data set; wherein the field characteristics comprise emission intensity, diffusion rate and regional coupling coefficient; constructing a field domain succession rule model based on the dynamic field data set, identifying a dominant evolution path based on an evolution baseline library of the model, injecting external disturbance along a conduction path of the dominant evolution path, and generating a multi-branch evolution scene tree; wherein the external disturbance comprises policy regulation and control intensity and a meteorological anomaly index. According to the invention, accurate prediction of regional carbon emission is realized.

The invention relates to a power distribution network partition and transformer substation power-carbon collaborative planning method considering multi-terminal uncertainty. The method comprises the following steps: extracting a representative wind-light output curve and a load curve; constructing an electrical coupling degree index, a load carbon flux index and a load balance degree index, calculating a comprehensive division index, and performing dynamic partitioning; and planning the transformer substation capacity which meets the main transformer N-1 verification, considers the influence of the load coincidence rate and the load prediction coefficient, and can meet the maximum load demand in the network element. The node carbon flux clustering index is combined with the traditional electrical coupling degree and load balance in the partition index, the limitation that traditional partition only depends on the electrical topological relation is broken through, the real-time carbon emission intensity of the load nodes is accurately quantified, and balanced partition of the power distribution network elements is achieved; carbon loss can be obviously reduced, and joint optimization of low carbon emission and high safety in the planning process of the transformer substation is realized; a typical scene expected value curve is used as input, and the influence of uncertainty on power distribution network planning is remarkably reduced.

The invention relates to the technical field of electric power system planning, in particular to a carbon emission evaluation method, system and device for multi-type adjusting power supply and new energy collaborative planning and a storage medium. The method comprises the following steps: acquiring power system equipment parameters, constructing a hierarchical planning platform architecture, and establishing a parameterized model library through data collection and governance; based on the parameterized model library, various adjusting power supply operation characteristic parameters are extracted, a collaborative optimization objective function is constructed in combination with new energy output characteristics, and a preliminary collaborative planning scheme is generated; formulating an active support strategy according to a power supply configuration result, determining a scheduling plan and generating an energy management and control instruction sequence, constructing typical and extreme weather scenes for collaborative operation simulation, and obtaining power supply output and power flow distribution; the node carbon emission intensity and the total system carbon emission amount are calculated through tidal current tracking in combination with the carbon emission factors, and carbon emission space-time distribution is generated; and comprehensively evaluating the collaborative planning scheme through a multi-dimensional evaluation index, and generating a visual decision support report.

The invention belongs to the technical field of carbon emission determination, and discloses a block chain-based carbon emission reduction comprehensive determination method and system, and the method comprises the steps: building a hierarchical data collection architecture, introducing a carbon emission description language, carrying out the collection and structural unification of internal heterogeneous data through a protocol converter, and obtaining multi-source heterogeneous data; the method comprises the following steps: establishing an intelligent oracle machine network, dynamically accessing an external data source, and aggregating multi-source heterogeneous data by adopting a BFT consensus mechanism to form a basic credible carbon data set; constructing a double-chain hybrid block chain architecture comprising a main chain and a side chain, and automatically calculating the carbon emission intensity based on a basic credible carbon data set in combination with a carbon right pledge mechanism; marking the data of which the carbon emission intensity is greater than a preset carbon emission intensity threshold value as high carbon emission behavior data, and triggering an account freezing operation; the scientificity, effectiveness and transparency of carbon emission reduction management are improved, and powerful technical support is provided for achieving the purpose of carbon emission reduction.

The invention provides a method and a system for calculating carbon emission in a building operation stage, and relates to the field of carbon emission metering. The method comprises the following steps: acquiring model driving data in real time, wherein the model driving data comprises meteorological data in a building operation stage, heating system operation data, building use state data and power grid carbon emission intensity data; the model driving data are input into a third-order RC model to predict the dynamic energy consumption in the building operation stage and obtain energy consumption prediction result data, and the third-order RC model is a multi-region coupled model established according to the partition characteristics of the building structure and comprises indoor environment nodes, enclosure structure nodes, indoor air nodes and internal heat quality nodes; constructing a dynamic differential equation set according to a thermal network theory; and according to the energy consumption prediction result data and the dynamically updated carbon emission factor database, calculating to obtain corresponding carbon emission data. According to the technical scheme, accurate and real-time calculation of the carbon emission in the operation stage of the building can be achieved with low cost.

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 belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention discloses a carbon quantity prediction method and device for coal-fired unit carbon emission, and a medium, and relates to the technical field of energy data intelligent analysis, and the method comprises the steps: calculating a carbon emission intensity prediction value and a confidence interval boundary value based on a real-time coal quality fusion operation parameter set and a device health index, and generating a dynamic carbon emission prediction package; comparing the dynamic carbon emission prediction packet with CEMS real-time monitoring data, calculating a prediction error rate, extracting a multi-dimensional error feature according to the prediction error rate, and generating a multi-dimensional error feature vector; and based on the multi-dimensional error feature vector, dynamically adjusting an equipment health index correlation factor and a coal quality confidence weight parameter, generating a dynamic correction instruction set, and generating a carbon emission prediction report in combination with a dynamic carbon emission prediction packet. According to the method, dynamic noise reduction, enhancement and feature quantization of the coal flow multispectral image are realized, the high-fidelity coal quality feature vector is directly generated, the problem of coal quality data lag is solved, and the coal quality sudden change response capability is improved.

The present invention provides a steel sheet containing Ni, Cu, and Sn, which has excellent chemical conversion treatability, and a part including the steel sheet. The steel sheet and parts including the steel sheet of the present invention have a chemical composition containing, in mass%, Ni: 0.010 to 1.000%, Cu: 0.010 to 1.000%, and Sn: 0.003 to 1.000%, and are characterized in that the emission intensities of Ni, Cu, and Sn in the surface layer of the steel sheet, measured by high-frequency glow discharge optical emission spectroscopy, respectively satisfy the following relationships: Ni: Maximum surface strength / bulk strength ≦ 1.5 Cu: Maximum surface strength / bulk strength ≦ 1.3 Sn: Maximum surface strength / bulk strength ≦ 15

The invention relates to the technical field of power system online monitoring, and discloses an online continuous monitoring carbon emission intensity prediction method and device and computer equipment, and the method comprises the steps: obtaining the production data of a coal-fired unit in real time, and extracting a plurality of carbon emission intensity characteristic indexes from the production data; fitting a reference value of each carbon emission intensity characteristic index by adopting a clustering algorithm based on the characteristic indexes; constructing a carbon emission intensity calculation model based on the characteristic indexes, and calculating the contribution degree of each carbon emission intensity characteristic index based on the carbon emission intensity calculation model; constructing a carbon emission intensity regression model based on each carbon emission intensity characteristic index contribution degree, the reference value and the carbon emission intensity calculation model; and predicting the carbon emission intensity based on the reference value of each carbon emission intensity characteristic index and the carbon emission intensity regression model. The problem of low prediction accuracy of the carbon emission intensity of the coal-fired unit due to the fact that real-time operation data of the coal-fired unit are not considered in the prior art is solved.

The invention discloses a carbon emission flow analysis method for carbon emission evaluation in multiple application scenes, belongs to the technical field of power system carbon emission modeling and evaluation, constructs a connection relationship among nodes, branches and a power supply in a power system, and collects output data and type information of a power generation power supply and a power flow result in multiple operation conditions. The method comprises the following steps: calculating carbon emission of a power generation node, distributing the carbon emission to a load node according to a power flow path, identifying a carbon emission source of the load node by adopting a power tracking method, and calculating carbon emission intensity and a carbon potential index; a transmission structure of carbon emission in a power grid is defined, step-by-step distribution and flow direction tracking of the carbon emission are realized, and the defect that a carbon emission path is unobservable in the prior art is overcome; the identification precision of the carbon emission composition is improved, and a basis is provided for the priority division of energy-saving and emission-reducing measures; a carbon emission path mapping matrix and a transition probability matrix are constructed, dynamic identification of path changes is realized, and discovery of key paths and nodes is facilitated.

The invention discloses a boiler direct carbon emission intensity correction method and a related device, and relates to the technical field of boiler carbon emission, and the method comprises the following steps: obtaining boiler efficiency, boiler direct carbon emission and boiler output heat obtained by performing an energy efficiency test and a carbon emission test on a boiler under a test condition; calculating a correction coefficient based on carbon in the fuel and a correction coefficient based on sulfur in the fuel based on the analysis value of the element or the component of the test fuel and the analysis value of the element or the component of the reference fuel; based on the boiler efficiency, the boiler direct carbon emission, the boiler output heat, the correction coefficient based on the carbon in the fuel and the correction coefficient based on the sulfur in the fuel, the corrected boiler direct carbon emission intensity corrected to the reference condition is obtained through calculation, and correction of the boiler direct carbon emission intensity when the test condition is inconsistent with the reference condition can be achieved; therefore, scientific evaluation of the carbon emission level of the boiler is realized.

The invention relates to the field of power system carbon emission flow analysis, in particular to a closed-loop power grid-oriented electric energy tracking and power flow tracing coupled carbon emission flow analysis method, which is based on power flow distribution of an actual power grid and comprehensively utilizes an electric energy tracking method and a power flow tracing method. According to the invention, fine allocation and full-link physical traceability of nodes, branches and loads in a closed-loop power grid structure by carbon emission are realized. By establishing a power flow distribution matrix, a unit injection matrix, a load distribution matrix and a carbon emission intensity vector, the carbon potential of each node, the branch carbon flow rate and the load carbon flow rate are calculated in a recursive manner, an electric energy tracking method and power flow traceability are coupled, and node allocation is iteratively optimized, so that the actual flow path and spatial distribution characteristics of the carbon flow in a power grid are disclosed. The method can adapt to scenes such as complex multi-source access and a looped network structure, the scientificity and fairness of closed-loop power grid carbon emission management are improved, and theoretical and method support is provided for development of low-carbon scheduling, carbon accounting and low-carbon economy.

The invention discloses a building energy saving and carbon reduction level evaluation method and device and a nonvolatile storage medium. The method comprises the following steps: acquiring unit area energy consumption and carbon emission intensity of a target building within a preset time interval; based on the energy consumption per unit area and the carbon emission intensity, an energy-carbon cooperation index of the target building is determined, and the energy-carbon cooperation index represents a cooperation evaluation index of energy consumption and carbon emission; based on the energy-carbon cooperation index, the energy consumption per unit area and the carbon emission intensity, a target evaluation index of the target building is determined, and the target evaluation index represents the energy-saving and carbon-reducing level of the building; and based on the target evaluation index, determining a target evaluation result of the target building, the target evaluation result including a plurality of evaluation levels. According to the invention, the technical problem of low evaluation efficiency caused by concentration of current building energy carbon evaluation methods in a single dimension is solved.

The invention relates to the technical field of land utilization change evaluation, in particular to a land gradient utilization carbon emission effect evaluation method, evaluation model and system and a readable storage medium. The method comprises the following steps: calculating a carbon absorption amount of a to-be-evaluated region from a natural vegetation dimension, a crop dimension and a water area dimension; calculating the carbon emission of the to-be-evaluated area from the energy consumption dimension, the industrial production process dimension, the waste treatment dimension, the agricultural dimension, the respiration dimension and the water area carbon volatilization dimension; calculating the carbon emission intensity, the carbon emission pressure index change intensity and the carbon footprint of the to-be-evaluated area based on the carbon absorption amount and / or the carbon emission amount; and according to the carbon emission intensity, the carbon emission pressure index and the carbon footprint, determining a land gradient utilization carbon emission effect evaluation result of the to-be-evaluated region. The objective of the invention is to solve the problem of how to accurately evaluate the carbon emission effect of an area with obvious vertical zone and altitude gradient.

The invention discloses an electricity-carbon cooperative scheduling method, system, medium and device based on Koopman and CEF models, and belongs to the field of power system optimization, and the method comprises the steps: obtaining the original expression of carbon intensity constraint according to a CEF model; reconstructing the original expression according to a branch outflow power distribution matrix and a diagonal matrix to obtain a nonlinear mapping relation expression between node carbon emission intensity and node power injection, and performing dimension raising processing according to a low-rank kernel approximate improved Koopman operator and a node power injection data set to obtain a linear mapping relation expression; according to the linear mapping relation expression, constructing a power-carbon cooperative scheduling model, and based on the current operation data of the power system, solving the scheduling model to obtain a scheduling optimization strategy; and regulating and controlling the operation of the power system according to the strategy to complete the power-carbon cooperative scheduling. Therefore, by implementing the method, the problem that the timeliness is insufficient when the power system is combined with the carbon emission flow to carry out power-carbon cooperative scheduling in the prior art can be solved.