Carbon-energy integrated management method and system based on digital twinning

Abstract

The application discloses a carbon-energy integrated management method based on digital twinning, and relates to the technical field of carbon-energy management.The method comprises the following steps: obtaining clean energy data, thermal power plant data, purchased energy data, planned load data and production process data; obtaining a supply carbon emission propagation network by using a preset first carbon emission propagation prediction model; obtaining a demand carbon emission propagation network by using a preset second carbon emission propagation prediction model; calculating carbon emission factors according to the clean energy data, the thermal power plant data and the purchased energy data; generating preliminary control instructions according to the carbon emission factors, the supply carbon emission propagation network and the demand carbon emission propagation network; combining equipment execution instructions and load control instructions to obtain simulation results and determine carbon deviation results; and adjusting the equipment execution instructions and the load control instructions to obtain energy-carbon collaborative control instructions according to the carbon deviation results and a preset carbon integration mechanism.The application can effectively improve the accuracy of carbon-energy management.

Description

Technical Field

[0001] This application relates to the field of energy and carbon management technology, and in particular to an integrated energy and carbon management method and system based on digital twins. Background Technology

[0002] With the continuous growth of global energy demand and the increasingly severe problem of climate change, low-carbon energy management and carbon emission control have become important issues. Energy production and consumption are the main sources of greenhouse gas emissions; therefore, optimizing carbon emissions at all stages of energy production, transmission, and consumption is a key way to reduce carbon footprint.

[0003] Currently, existing energy management systems operate in isolation, with energy production monitoring systems, energy demand metering systems, and carbon accounting systems operating independently. For example, a power plant's DCS system records the operating parameters of generating equipment, but the carbon accounting system cannot access this data in real time and must manually enter it afterward, leading to delays and inaccuracies in carbon accounting. Wind farm SCADA systems can predict wind power output, but the grid's energy distribution system cannot directly access this predicted data and still relies on outdated reports transmitted manually. This lack of interoperability between management systems for different energy types makes it impossible to accurately predict carbon emissions from energy production, resulting in inaccurate carbon emission control and increased energy and carbon management costs.

[0004] There is currently no good solution to the above problems. Summary of the Invention

[0005] This application provides a digital twin-based integrated energy and carbon management method and system to improve the accuracy of energy and carbon management and reduce its cost.

[0006] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0007] Firstly, a digital twin-based integrated energy and carbon management method is provided, applied to an energy and carbon management system. The energy and carbon management system includes a supply side and a demand side, with the supply side including clean energy, thermal power, and purchased energy. The method includes:

[0008] The energy and carbon management system can acquire clean energy data, thermal power equipment data, and purchased energy data from the supply side, as well as planned load data and production process data from the demand side in real time.

[0009] Based on clean energy data, thermal power equipment data, and purchased energy data, the supply carbon emission propagation network is predicted using a pre-set first carbon emission propagation model.

[0010] Based on the planned load data and production process data from the demand side, a pre-set second carbon emission propagation prediction model is used to obtain the demand carbon emission propagation network;

[0011] Carbon emission factors are calculated based on clean energy data, thermal power equipment data, and purchased energy data, respectively.

[0012] Preliminary control instructions are generated based on carbon emission factors, supply carbon emission propagation networks, and demand carbon emission propagation networks. These preliminary control instructions include equipment execution instructions on the supply side and load control instructions on the demand side.

[0013] The simulation results are obtained by combining the equipment execution commands and load control commands in the digital twin model, and the carbon deviation is verified based on the simulation results to obtain the carbon deviation results.

[0014] Based on the carbon deviation results and the preset carbon integral mechanism, the equipment execution instructions and load control instructions of the target park are adjusted to obtain the energy-carbon coordinated control instructions.

[0015] In one possible implementation of the first aspect, the method further includes:

[0016] When the power generation of clean energy is greater than the first preset threshold and the real-time electricity consumption data is less than the second preset threshold, the power generation fuel type of thermoelectric energy is obtained.

[0017] Determine the carbon content per unit calorific value and the lower heating value of the fuel corresponding to the type of power generation fuel in the pre-set database;

[0018] The fuel input and electrical output of each power generation unit are obtained, and the average power generation efficiency of each power generation unit is calculated by combining the product of the fuel input and the lower heating value of the fuel with the electrical output.

[0019] The average carbon emissions per kilowatt-hour are calculated based on the average power generation efficiency and the carbon content per unit calorific value.

[0020] The equivalent carbon emission reduction for the target park is determined by multiplying the power generation of clean energy by the carbon emission value. The equivalent carbon emission reduction is used to determine the amount of carbon emission reduction achieved by replacing thermal power with clean energy.

[0021] In one possible implementation of the first aspect, the step of predicting the supply carbon emission propagation network using a preset first carbon emission propagation model based on clean energy data, thermal power equipment data, and purchased energy data includes the following steps:

[0022] The park's physical network map is constructed by integrating clean energy data, thermal power equipment data, and purchased energy data. This physical network map serves as a network map for the supply side.

[0023] For any node in the entity network graph of the park, calculate the instantaneous carbon emission intensity;

[0024] The carbon emission event status of each node is determined based on the instantaneous carbon emission intensity;

[0025] The carbon emission path is simulated by using a pre-set first carbon emission propagation model, combined with the carbon emission event status of each node and the network map of the park entities.

[0026] Calculate the carbon emission propagation rate, carbon emission recovery rate, and carbon emission propagation lag between each node based on the carbon emission pathway;

[0027] The Monte Carlo sampling algorithm is used to combine carbon emission propagation rate, carbon emission recovery rate and carbon emission propagation time lag to obtain the propagation probability matrix, the earliest arrival time of carbon propagation and the node risk heat value;

[0028] The carbon emission propagation network is obtained based on the propagation probability matrix, the earliest arrival time of carbon propagation, the node risk heat value, and the park entity network map.

[0029] In one possible implementation of the first aspect, the nodes of the park entity network graph include thermal power equipment nodes, and the calculation of instantaneous carbon emission intensity for any node of the park entity network graph includes the following steps:

[0030] When the node is a thermal power equipment node, the basic operating data of each thermal power equipment node is obtained. The basic operating data includes fuel consumption, unit carbon emission factor, actual power generation and actual heat supply.

[0031] Calculate the electrical side allocation coefficient and the thermal side allocation coefficient based on the basic operating data;

[0032] The total carbon emissions of thermal power equipment are calculated by multiplying the fuel consumption and the unit carbon emission factor.

[0033] The total carbon emissions from the thermal power equipment are multiplied by the electric side allocation factor and the thermal side allocation factor, respectively, to obtain the carbon emissions on the electric side and the carbon emissions on the thermal side.

[0034] The carbon emission intensity on the electric side and the carbon emission intensity on the thermal side are calculated using the ratios between the carbon emissions on the electric side and the actual power generation, and between the carbon emissions on the thermal side and the actual heat supply. These carbon emission intensities are used to characterize the instantaneous carbon emission intensity.

[0035] In one possible implementation of the first aspect, determining the carbon emission event state of each node based on instantaneous carbon emission intensity includes the following steps:

[0036] The carbon emission residual value within a preset time period is calculated by combining the instantaneous carbon emission intensity and the preset intensity benchmark value, and the carbon emission ramp-up rate is calculated based on the carbon emission residual value within the preset time period.

[0037] Nodes with carbon emission residual values ​​greater than or equal to a preset residual threshold and carbon emission ramp-up rates greater than or equal to a preset ramp-up rate threshold are designated as carbon emission event starting nodes, and the event occurrence location of each carbon emission event starting node is determined.

[0038] Calculate the node edge weights between each node based on the location of the event, and determine the set of propagation paths based on the node edge weights.

[0039] Determine the duration of the carbon emission event state for each node in each propagation path set;

[0040] High carbon emission sources are screened by combining duration and propagation path sets, and these high carbon emission sources are used to characterize the state of carbon emission events.

[0041] In one possible implementation of the first aspect, generating preliminary control instructions based on carbon emission factors, supply carbon emission propagation networks, and demand carbon emission propagation networks, wherein the preliminary control instructions include supply-side equipment execution instructions and demand-side load control instructions, comprises the following steps:

[0042] The energy carbon management system obtains information on the fuel type and average power generation efficiency of thermal power equipment, as well as the marginal carbon emission factor of purchased energy.

[0043] Determine the standard emission factor corresponding to the fuel type;

[0044] The effective carbon emission factor is calculated based on the standard emission factor and average power generation efficiency.

[0045] The supply node centrality among nodes in the supply carbon emission propagation network and the demand node centrality among nodes in the demand carbon emission propagation network are determined separately. The supply node centrality and demand node centrality are used to show the importance of nodes.

[0046] For any node in the supply carbon emission propagation network and the demand carbon emission propagation network, assign a centrality coefficient to each node based on the centrality of the supply node and the centrality of the demand node.

[0047] The effective carbon emission factor and marginal carbon emission factor for each node are determined respectively, and the node superposition weight for each node is calculated based on the effective carbon emission factor, marginal carbon emission factor and the centrality coefficient assigned to each node.

[0048] Preliminary control instructions are generated based on the weights of the nodes corresponding to each node.

[0049] In one possible implementation of the first aspect, determining the supply node centrality among nodes in the supply carbon emission propagation network and the demand node centrality among nodes in the demand carbon emission propagation network respectively includes the following steps:

[0050] For any given node, determine the number of transmission lines between each node and its adjacent node;

[0051] Demand node centrality and supply node centrality are obtained using a preset centrality calculation formula based on the total number of nodes and the number of transmission lines.

[0052] In one possible implementation of the first aspect, the equipment execution instructions include thermoelectric energy equipment and clean energy equipment. The step of adjusting the equipment execution instructions and load control instructions of the target park according to the carbon deviation results and a preset carbon integral mechanism to obtain energy-carbon coordinated control instructions includes the following steps:

[0053] Obtain the carbon credits of high-carbon emission equipment and clean energy equipment in the thermoelectric energy equipment. If the carbon credits are less than the preset carbon credit threshold, determine the carbon deviation allocation value corresponding to the high-carbon emission equipment based on the carbon deviation results.

[0054] Calculate the reduction in power generation from thermal power equipment based on the carbon deviation allocation value;

[0055] If the carbon credit of the clean energy equipment is greater than the preset carbon credit threshold, the clean energy power generation will be calculated based on the carbon credit of the clean energy equipment.

[0056] The reduction in power generation and the amount of clean energy power generation are respectively converted into power adjustment values ​​within a preset time period to obtain the reduced power generation of high-carbon emission equipment and the increased power generation of clean energy equipment.

[0057] The demand side of the park's load is predicted based on the power generation reduction and power generation increase on the supply side.

[0058] Identify the net adjustment gap on the demand side based on the park's load demand and load control instructions;

[0059] Energy and carbon coordinated control commands are obtained by adjusting load control commands through net regulation gaps.

[0060] Secondly, this application provides an integrated energy and carbon management system based on digital twins, comprising:

[0061] The memory is configured to store instructions; and

[0062] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned digital twin-based integrated energy and carbon management method.

[0063] Thirdly, this application provides a machine-readable storage medium storing instructions that cause a machine to execute the aforementioned digital twin-based integrated energy and carbon management method.

[0064] By establishing both supply-side and demand-side carbon emission propagation networks, the aforementioned technical solutions achieve bidirectional coupling between carbon sources and sinks. This effectively reduces the output of high-carbon-emission energy equipment and guides load-side peak shaving and shifting, resulting in lower-carbon and more rational overall energy dispatch. Preliminary control commands are generated based on a weighted average of carbon emission factors and network structure centrality, allowing control to consider not only power demand but also externalities and propagation risks of carbon emissions. Combining digital twin simulation and carbon deviation feedback effectively addresses the disconnect between prediction and execution in traditional strategies, enhancing the intelligence and accuracy of control decisions. Automatically generating control commands based on real-time equipment operating conditions, load plans, and carbon propagation trends, and compensating for carbon deviations using simulation results, improves the dynamic response capability of carbon reduction measures. Adjusting equipment execution commands and load control commands in the target park based on carbon deviation results and a pre-set carbon credit mechanism yields energy-carbon coordinated control commands. This allows for credit penalties on high-carbon equipment and guides equipment and user behavior towards low-carbon optimal strategies, effectively reducing carbon emissions.

[0065] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0066] Figure 1 A flowchart illustrating an energy and carbon integrated management method based on digital twins, provided for an embodiment of this application;

[0067] Figure 2 This is a schematic diagram of a digital twin-based integrated energy and carbon management method provided in an embodiment of this application. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0069] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0070] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0071] Figure 1 The illustration schematically shows a flowchart of an energy and carbon integrated management method based on digital twins according to an embodiment of this application. Figure 1 As shown in the embodiment of this application, an integrated energy and carbon management method based on digital twins is provided and applied to an energy and carbon management system. The energy and carbon management system includes a supply side and a demand side. The supply side includes clean energy, thermal power energy and purchased energy. The method may include the following steps.

[0072] S110. Real-time acquisition of clean energy data, thermal power equipment data, and purchased energy data from the supply side, as well as planned load data and production process data from the demand side, through the energy and carbon management system.

[0073] S120. Based on clean energy data, thermal power equipment data, and purchased energy data, the supply carbon emission propagation network is predicted using a pre-set first carbon emission propagation model.

[0074] S130. Based on the planned load data and production process data on the demand side, the pre-set second carbon emission propagation prediction model is used to obtain the demand carbon emission propagation network.

[0075] S140. Calculate the carbon emission factor based on clean energy data, thermal power equipment data, and purchased energy data respectively.

[0076] S150. Generate preliminary control instructions based on carbon emission factors, supply carbon emission propagation networks, and demand carbon emission propagation networks. The preliminary control instructions include equipment execution instructions on the supply side and load control instructions on the demand side.

[0077] S160. Simulation results are obtained by combining equipment execution commands and load control commands in a digital twin model, and carbon deviation is verified based on the simulation results to obtain carbon deviation results.

[0078] S170. Based on the carbon deviation results and the preset carbon integral mechanism, adjust the equipment execution instructions and load control instructions of the target park to obtain the energy-carbon coordinated control instructions.

[0079] The energy and carbon management system acquires real-time data on supply-side clean energy, combined heat and power (CHP) equipment, and purchased energy, as well as demand-side planned load and production process data. The system collects key data from both the supply and demand sides in real time. Clean energy data includes data related to solar, wind, hydro, and biomass energy. CHP equipment data refers to data acquired from CHP equipment, including fuel consumption, power generation, heating capacity, and equipment operating parameters. CHP equipment can be coal-fired or gas-fired. Purchased energy data refers to data on energy purchased from external sources, such as grid electricity or natural gas. In this embodiment, the supply side is the power generation side, such as power plants; the demand side is the electricity consumer, such as factories within the industrial park. Planned load data is the planned demand for electricity, heat, and other energy loads for the park or enterprises over a future period. For demand-side users such as industrial enterprises, production process data reflects the energy consumption characteristics and carbon emission correlations during the production process.

[0080] Based on clean energy data, thermal power equipment data, and purchased energy data, a pre-set first carbon emission propagation model is used to predict the supply-side carbon emission propagation network. In this embodiment, the pre-set first carbon emission propagation model is a pre-established model used to simulate the propagation of carbon emissions on the supply side. The first carbon emission propagation model is constructed based on a Long Short-Term Memory (LSTM) network. Energy data has time-series characteristics, and LSTM can better handle long-term dependencies in time series. Clean energy power generation data from enterprises or regions over the past few years is collected. This data can be obtained from the monitoring systems of clean energy power plants, including real-time data such as daily irradiance and power generation of solar power plants, and wind speed and power generation of wind farms. Carbon emission monitoring data can be obtained from carbon emission monitoring equipment installed by enterprises or regions. For example, a carbon dioxide concentration monitor can be installed on the chimney of thermal power equipment. Combined with fuel consumption and equipment operating parameters, the actual carbon emissions are calculated, and this data is preprocessed. Next, the Long Short-Term Memory (LSTM) network model is trained using clean energy power generation data and carbon emission monitoring data from the past few years. During training, preprocessed energy data (such as clean energy, thermal power equipment, and purchased energy data after time window division) is used as input x. tThe input is processed by an LSTM network. The LSTM network processes the input data through the aforementioned gating mechanism, learning the mapping relationship between energy data and carbon emission propagation parameters (such as carbon emissions and propagation path weights). Subsequently, a loss function is defined, such as the mean squared error (MSE) loss function. The gradient of the loss function with respect to the network weights is calculated using a backpropagation algorithm (such as time-based backpropagation BPTT), and an optimization algorithm (such as the Adam optimization algorithm) is used to gradually reduce the loss function, causing the network's predicted values ​​to gradually approach the actual values. Through these steps, a pre-defined first carbon emission propagation model is obtained. The supply-side carbon emission propagation network is represented as a graph, where nodes can be supply-side facilities such as power plants, and edges represent the propagation paths of energy and carbon. Each node has relevant node attributes, such as the node's energy production or conversion volume and carbon emissions. The supply-side carbon emission propagation network provides a clear view of the sources, propagation paths, and impacts on carbon emissions at different nodes. The system first collects data on clean energy, thermal power equipment, and purchased energy, then inputs this data into a pre-defined carbon emission propagation model for simulation prediction, thereby obtaining the supply-side carbon emission propagation network.

[0081] Based on the planned load data and production process data from the demand side, a pre-defined second carbon emission propagation prediction model is used to obtain the demand carbon emission propagation network. In this embodiment, the pre-built second carbon emission propagation prediction model is a pre-constructed model used to simulate and predict the carbon emission propagation caused by energy on the demand side. The demand carbon emission propagation network is presented in the form of a graph, where nodes can be different demand-side users, and edges represent the propagation relationship of energy and carbon on the demand side. Specifically, firstly, the energy consumption on the demand side is determined based on the planned load data. Then, combined with the carbon emission factor of each energy source, the direct carbon emissions on the demand side are calculated. For example, if a building's planned load data is 10,000 kWh and the grid electricity carbon emission factor is 0.6 kg CO2 / kWh, the direct carbon emissions from the building's daily electricity consumption are 10,000 × 0.6 = 6,000 kg CO2. Next, the pre-defined second carbon emission propagation prediction model is used in conjunction with production process data to analyze the flow and transformation of energy and carbon during the production process. For example, in automobile manufacturing enterprises, from raw material processing to assembly and painting, the second carbon emission propagation prediction model calculates the carbon emissions of each process based on its energy consumption and carbon emission factors, and simulates how carbon emissions propagate through the production process between different workshops and equipment, thus obtaining the demand carbon emission propagation network. In this embodiment, the demand carbon emission propagation network is obtained using a preset second carbon emission propagation prediction model based on the planned load data and production process data from the demand side. The specific steps include the following:

[0082] S1. Obtain planned load data from the demand side. The planned load data includes the target energy consumption level of each process step in the production process within a preset time period.

[0083] S2. Obtain production process data from the demand side. The production process data includes production line process links, equipment energy consumption relationships, and the coupling path between energy and materials between each production process.

[0084] S3. Based on planned load data and production process data, establish an energy and material flow network on the demand side, and mark the load intensity of each production process in the energy and material flow network;

[0085] S4. When the planned load exceeds the preset threshold, adjust the energy consumption allocation of some process links according to the preset flexible adjustment rules of the production process to obtain an adjustable load scheme.

[0086] S5. Input the adjusted production process data and planned load data into the preset second carbon emission propagation prediction model to predict the carbon emission intensity and carbon emission transmission relationship of each production process.

[0087] S6. Construct a demand-based carbon emission propagation network based on the relationship between carbon emission intensity and carbon emission transmission. The demand-based carbon emission propagation network is used to characterize the carbon emission impact paths and propagation patterns among nodes on the demand side.

[0088] First, each process step in the production flow represents a specific energy-consuming unit or part on the demand side. For industrial enterprises, energy-consuming units can be different production lines, workshops, or large equipment; a preset time period is a pre-defined time range used to uniformly measure energy consumption. The target energy consumption level refers to the amount of energy that each energy-consuming unit plans to consume within the preset time period. Obtaining this planned load data is to understand future energy consumption plans on the demand side, providing foundational data for subsequent energy management and carbon emission analysis.

[0089] Based on planned load data and production process flow data, an energy and material flow network is established on the demand side, and the load intensity of each production process is marked in the network. Planned load data refers to the energy expected to be consumed by each process or energy-consuming unit within a certain time period. Production process flow data refers to the sequential relationships and input-output relationships between different process stages. Nodes represent various stages or equipment in the production process, such as "heating furnace," "reactor," and "packaging machine." Edges represent the material transfer or energy coupling relationships between stages, such as steam flowing from the boiler to the reactor, or intermediate products flowing from the heating stage to the reaction stage. This results in a network diagram that reflects both the upstream and downstream relationships between production stages and the flow of energy / materials between different stages. Load intensity represents the energy consumption of a certain process node per unit time. Each node is accompanied by its energy consumption level, allowing for a clear view of which stage has the highest energy demand and which stage is the key energy consumption point.

[0090] When the planned load exceeds a preset threshold, an adjustable load scheme is obtained by adjusting the energy consumption allocation of some process steps according to the preset flexible adjustment rules of the production process flow. Planned load refers to the total energy consumption of the park or factory at a certain time in the future. The preset threshold represents the maximum acceptable level set by the system, such as the upper limit of grid capacity, energy consumption budget, or carbon emission target. When the predicted energy demand exceeds this threshold, measures need to be taken to avoid exceeding energy consumption or carbon emission standards. Flexible adjustment refers to adjusting the energy consumption of some process steps without affecting the overall production target or allowing for minor adjustments. In this embodiment, the preset rules may include peak shifting, peak shaving, substitution, and off-peak operation. Peak shifting refers to adjusting energy-intensive processes to operate during off-peak electricity prices or low-carbon emission periods; peak shaving refers to temporarily reducing the production load of certain non-critical processes; substitution refers to replacing high-carbon energy with low-carbon energy; off-peak operation refers to delaying or advancing the operation of some processes to avoid concentrated energy consumption during peak periods. After flexible adjustment, a new load allocation scheme will be formed. In this scheme, the energy consumption of each process step is redistributed. For example, the energy consumption of some processes is reduced or delayed; the utilization rate of some clean energy equipment is increased; and the overall load curve is smoothed to avoid exceeding the threshold.

[0091] The adjusted production process data and planned load data are input into a pre-set second carbon emission propagation prediction model to predict the carbon emission intensity and carbon emission transmission relationship for each production process. The adjusted production process data refers to the new process data after flexible adjustment, including the operating sequence, energy consumption allocation, and load reduction or transfer of each process. The planned load data represents the energy consumption level of each process node in a future period and serves as the input condition for the prediction model. The second carbon emission propagation prediction model is a pre-set model used to simulate the distribution and diffusion of carbon emissions in the demand-side network. The second carbon emission propagation prediction model outputs the carbon emission intensity and carbon emission transmission relationship for each production process. The carbon emission intensity is the carbon emission level corresponding to unit output or unit energy consumption, such as a reaction process: 0.8 kg CO2 / kWh electricity + 0.5 kg CO2 / t steam. The carbon emission transmission relationship represents the mutual influence path of carbon emissions between processes. For example, how much will an increase in energy consumption of process A lead to an increase in carbon emissions of process B, or by what proportion and with what delay will a change in the carbon intensity of a certain process be transmitted to downstream processes.

[0092] A demand-side carbon emission propagation network is constructed based on the relationship between carbon emission intensity and carbon emission transmission. This network characterizes the carbon emission impact paths and propagation patterns among nodes on the demand side. Nodes can be energy-consuming units, process steps in the production flow, equipment, etc., on the demand side. The carbon emission impact paths are constructed based on the carbon emission transmission relationships. In other words, the network based on carbon emission intensity and carbon emission transmission relationships forms a demand-side carbon emission propagation network, which is a network topology diagram with quantitative information. The propagation patterns refer to how carbon emissions spread and diffuse along energy and material flows within the demand-side carbon emission propagation network. For example, it may be found that carbon emissions often propagate from upstream to downstream process steps along the material processing sequence, or that high-energy-consuming equipment corresponds to process steps with high carbon emission intensity and a significant impact on other processes.

[0093] Carbon emission factors are calculated based on clean energy data, thermal power equipment data, and purchased energy data. In this embodiment, carbon emission factors include electricity emission factors and heat emission factors. Specifically, clean energy data, thermal power equipment data, and purchased energy data are the three types of input data sources for calculating carbon emission factors (classified by energy supply source), while electricity emission factors and heat emission factors are output results classified by energy service type (electricity / heat). In other words, carbon emission factors are calculated based on clean energy data, thermal power equipment data, and purchased energy data (the three types of data serve as input parameters from different sources). In this embodiment, carbon emission factors are classified into electricity emission factors and heat emission factors according to energy service type. The electricity emission factor refers to the carbon dioxide emissions corresponding to each unit of electricity consumed; the heat emission factor refers to the carbon dioxide emissions corresponding to each unit of heat provided. The calculation formula for the electricity emission factor is as follows:

[0094]

[0095] CHP (Combined Heat and Power) generation carbon emissions refer to the carbon emissions generated by the power generation of the combined heat and power equipment itself, measured in kilograms of carbon dioxide (kgCO2); purchased electricity carbon emissions refer to the carbon emissions generated from electricity purchased from the power grid or external energy market, measured in kilograms of carbon dioxide (kgCO2); electricity load emissions refer to the carbon emissions from the actual electricity load consumed by the park or node, measured in kilograms of carbon dioxide (kgCO2); and electric boiler power consumption emissions refer to the carbon emissions from the electricity consumed by the electric boiler in the process of converting electrical energy into heat energy, measured in kilograms of carbon dioxide (kgCO2).

[0096] The formula for calculating the thermal emission factor is as follows:

[0097]

[0098] CHP heating carbon refers to the carbon emissions generated from heating from combined heat and power (CHP) equipment, which can be carbon produced by the combustion of fuels such as natural gas and coal; electric boiler carbon refers to the carbon emissions generated by the electricity consumed by electric boilers for heating; and heat load emissions refer to the carbon emissions generated by the total heating demand of the system within a certain period of time.

[0099] Preliminary control instructions are generated based on carbon emission factors, the supply carbon emission propagation network, and the demand carbon emission propagation network. These instructions include equipment execution instructions on the supply side and load control instructions on the demand side. In this embodiment, the carbon emission factor refers to the carbon emissions per unit of energy or output. The supply carbon emission propagation network is a network diagram describing how carbon emissions propagate from the supply side to the demand side; the demand carbon emission propagation network is a network diagram describing how carbon emissions propagate on the demand side; equipment execution instructions are operational instructions issued to various types of equipment on the energy supply side; and load control instructions are control instructions issued to various types of users on the energy demand side. In other words, based on the carbon emission factors and the supply carbon emission propagation network, key carbon emission sources and propagation paths on the supply side are identified. For high-carbon-emission cogeneration equipment, based on its position in the propagation network, equipment execution instructions are formulated. For example, high-carbon-emission cogeneration units are required to reduce power generation during off-peak hours to reduce carbon emissions; for cogeneration equipment with multiple operating modes, they are instructed to prioritize the use of low-carbon fuels. Demand-side load control instructions are also based on carbon emission factors and demand carbon emission propagation networks. They analyze the carbon emission situation of demand-side users and formulate load control instructions for high-load users during periods of high power emission factors. For example, they guide transferable loads from periods of high power emission factors to periods of low power emission factors. During periods of tight power supply and high power emission factors, they issue reduction instructions for loads that can be reduced.

[0100] Simulation results are obtained by combining equipment execution commands and load control commands in a digital twin model. Carbon deviation is then verified based on these simulation results to obtain carbon deviation results. The supply-side equipment execution commands and demand-side load control commands are input into the digital twin model. The digital twin model runs simulation algorithms based on these input commands, combining the equipment execution commands and load control commands. First, the equipment execution commands and load control commands are converted into a standardized format recognizable by the digital twin model using an ETL tool (such as Apache NiFi). Next, basic data and model parameters are loaded, and simulation scenario boundaries are set to ensure consistency between the model and the physical system state. Loading basic data includes equipment parameters and real-time physical system status data; configuring simulation scenario parameters includes setting time windows according to commands, defining the simulation scope (e.g., including only the thermal power system + photovoltaic + chemical workshop), and constraints (e.g., boiler minimum load ≥ 30% of rated value). Finally, the integrity of parameters in the physical sub-models (e.g., boiler heat transfer model, photovoltaic output model), chemical sub-models (e.g., carbon generation model), and control logic sub-models (e.g., PID control algorithm) is checked. Next, the model is driven by standardized instructions, outputting dynamic simulation results of energy flow and carbon flow. The control logic sub-model receives standardized instructions to generate equipment action sequences and physical sub-model response action sequences, and uses the chemical sub-model to calculate carbon emissions based on the energy data of the physical sub-model. Through these sub-models, multi-dimensional simulation results are generated, providing data support for carbon deviation verification. For example, the simulation may demonstrate the reduction in fuel consumption and carbon emissions after a combined heat and power (CHP) unit power is reduced, as well as the impact on the power grid supply; or the reduction in daytime peak-hour power load and the increase in nighttime off-peak-hour power load after industrial load shifting, as well as the impact on the power grid load curve. The simulation results can include carbon emission data from both the supply and demand sides, as well as energy production and consumption data. Next, carbon deviation verification is performed based on the simulation results to obtain the carbon deviation result. Carbon deviation verification compares the carbon emission results obtained from the digital twin model simulation with the expected carbon emission target (or benchmark value) to calculate the deviation value. In other words, the benchmark carbon emission value is subtracted from the simulation result to calculate the deviation, thus obtaining the carbon deviation result.

[0101] Based on the carbon deviation results and the preset carbon credit mechanism, the equipment execution instructions and load control instructions of the target park are adjusted to obtain the energy-carbon coordinated control instructions. The preset carbon credit mechanism is an incentive and constraint mechanism used to measure the carbon emission reduction or emission increase behavior of each entity in the energy and carbon system and assign corresponding credits. The initial carbon credit allocation rules can refer to the tiered allocation logic of EU-ETS, using the general rules for greenhouse gas emission accounting of industrial enterprises as the benchmark, and allocating quotas according to the main energy type. The initial credits for clean energy entities = installed capacity × industry benchmark power generation × cleanliness factor, where the cleanliness factor is taken from the 2023 electricity carbon footprint factor. The initial credits for thermal power equipment entities (coal-fired and gas-fired boilers) = design annual energy consumption × unit energy consumption benchmark emission × emission reduction factor (0.85, referring to the average boiler efficiency). The initial credits for purchased energy entities = purchased amount × regional grid emission factor (e.g., 0.58 tCO2 / MWh). The industry benchmark is the average of the annual benchmark value (e.g., the emission intensity of the top 20% of enterprises in the region) and the enterprise's historical best value. The calculation formula is: Benchmark Emissions = Actual Energy Consumption × Benchmark Emission Factor (e.g., 0.1886 tCO2 / GJ for natural gas). The points system is based on emission reduction rewards. When actual emissions < benchmark emissions, the additional points = (benchmark emissions - actual emissions) × incentive coefficient (1.2 times). For example, a photovoltaic power station whose actual power generation exceeds the design value by 10% receives an additional 130.8 × 10% × 1.2 = 15.7 points. Excessive emissions are penalized when actual emissions > benchmark emissions. The deduction points = (actual emissions - benchmark emissions) × penalty coefficient (1.5 times), triggering a warning. If a thermal power plant exceeds emissions by 5%, 166,600 × 5% × 1.5 = 12,495 points are deducted. When the carbon deviation is positive, it indicates that the target has not been met, and if the thermal power equipment has few points remaining after deducting points for not fulfilling power reduction instructions, its power generation capacity limit needs to be further reduced. When demand-side users lose credits for failing to fulfill load control instructions, the intensity of control measures can be increased, such as stricter load transfer requirements or economic incentives to improve their responsiveness. If the carbon deviation is negative, indicating that the target has been exceeded and the main carbon credits are sufficient, the upper limit of the power generation capacity of clean energy equipment can be appropriately increased to improve energy utilization efficiency, or the control requirements for certain loads can be relaxed to reduce the impact on users' production and daily life, while ensuring a certain level of carbon emission reduction.

[0102] Figure 2 A schematic diagram of a digital twin-based integrated energy and carbon management method provided in this application embodiment is shown below. Figure 2 As shown, this includes the demand side and the supply side. The demand side includes thermal power, clean energy, and purchased energy. Thermal power, clean energy, and purchased energy provide electrical and thermal loads to the demand side, thereby generating demand-side carbon emissions.

[0103] By establishing both supply-side and demand-side carbon emission propagation networks, a two-way coupling from carbon sources to carbon sinks is achieved. This effectively reduces the output of high-carbon-emission energy equipment and guides load-side peak shaving and shifting, resulting in a lower-carbon and more rational overall energy dispatch. Preliminary control commands are generated based on a weighted average of carbon emission factors and network structure centrality, allowing control to consider not only power demand but also the externalities and propagation risks of carbon emissions. Combining digital twin simulation and carbon deviation feedback effectively solves the problem of disconnect between prediction and execution in traditional strategies, enhancing the intelligence and accuracy of control decisions. Control commands are automatically generated based on real-time equipment operating conditions, load plans, and carbon propagation trends, and carbon deviation compensation is performed using simulation results, improving the dynamic response capability of carbon reduction measures. Adjusting equipment execution commands and load control commands in the target park based on carbon deviation results and a preset carbon credit mechanism yields energy-carbon coordinated control commands. This allows for credit penalties on high-carbon equipment and guides equipment and user behavior towards low-carbon optimal strategies, effectively reducing carbon emissions.

[0104] In one embodiment of this invention, the method further includes:

[0105] S210. When the power generation of clean energy is greater than the first preset threshold and the real-time electricity consumption data is less than the second preset threshold, obtain the power generation fuel type of thermoelectric energy.

[0106] S220. Determine the carbon content per unit calorific value and the lower heating value of the fuel corresponding to the type of power generation fuel in the preset database.

[0107] S230. Obtain the fuel input and electrical output of each power generation device, and calculate the average power generation efficiency of each power generation device by combining the product of the fuel input and the lower heating value of the fuel and the electrical output.

[0108] S240. The average carbon emission value per kilowatt-hour is calculated based on the average power generation efficiency and the carbon content per unit calorific value.

[0109] S250. Multiply the power generation of clean energy by the carbon emission value to determine the equivalent carbon emission reduction of the target park. The equivalent carbon emission reduction is used to determine the amount of carbon emission reduction achieved by replacing thermal power with clean energy.

[0110] When the power generation from clean energy exceeds a first preset threshold and the real-time electricity consumption data is less than a second preset threshold, the fuel type for thermal power generation is determined. This means that when clean energy sources such as solar and wind power have high output and sufficient supply, and the current electricity demand in the park is not high, the overall load is light, and the energy pressure is not significant, the fuel type for thermal power generation can be determined through the energy management system. The first and second preset thresholds can be determined based on the actual situation.

[0111] The system determines the carbon content per unit calorific value and the lower heating value (LCV) of various power generation fuels from a pre-defined database. Power generation fuel types include coal, oil, natural gas, and biomass. Carbon content per unit calorific value refers to the mass of carbon in a unit of heat produced by the fuel. The LHC refers to the heat released when a unit mass of fuel is completely burned, with the water vapor in the combustion products existing in a gaseous state. The pre-defined database is a collection of information that has been pre-organized and stored for various power generation fuels. In other words, it retrieves the carbon content per unit calorific value and the LHC for various fuel types from a pre-defined knowledge base or database.

[0112] The average power generation efficiency of each generator is calculated by obtaining the fuel input and electrical output of each generator, and combining the product of the fuel input and the lower heating value of the fuel with the electrical output. Obtaining the fuel input and electrical output of each generator can be achieved through an energy management system. Fuel input refers to the amount of fuel input into the generator for combustion and power generation within a certain time period; electrical output refers to the amount of electrical energy produced and output to the grid within a certain time period; average power generation efficiency is the ratio of the electrical energy output to the input fuel energy. The formula for calculating average power generation efficiency is as follows:

[0113]

[0114] The average carbon emissions per kilowatt-hour (kWh) of electricity are calculated based on average power generation efficiency and carbon content per unit calorific value. Carbon content per unit calorific value refers to the mass of carbon in the fuel per unit of heat. The average carbon emissions per kWh refer to the amount of greenhouse gases such as carbon dioxide emitted when generating one kWh of electricity. The fuel input is converted into energy input using the lower heating value of the fuel. The total carbon content of the fuel is calculated based on the carbon content per unit calorific value, and then the total CO2 emissions are calculated according to the stoichiometric relationship between carbon and CO2. Finally, the average carbon emissions per kWh are obtained by dividing the total CO2 emissions by the total power generation. The specific calculation formula is shown below:

[0115]

[0116] Where EF represents the average carbon emissions per kilowatt-hour; c is the carbon content per unit calorific value; η is the average power generation efficiency; 44 / 12 is the stoichiometric coefficient for the conversion of carbon to carbon dioxide; 1000 represents the conversion of fuel energy units from MJ to GJ; 3.6 represents the conversion of electrical energy units to kW. h can be converted to the heat unit MJ.

[0117] Next, the equivalent carbon emission reduction for the target industrial park is determined by multiplying the clean energy generation by the carbon emission value. This equivalent carbon emission reduction is used to determine the carbon emission reduction achieved by replacing thermal power with clean energy. The equivalent carbon emission reduction is obtained by multiplying the clean energy generation by the carbon emission value, using the following formula:

[0118] Equivalent carbon emission reduction = Clean energy power generation × Carbon emission value

[0119] This indicates the reduction in carbon emissions equivalent to those generated by using clean energy instead of thermoelectric power generation.

[0120] By determining the equivalent carbon emission reduction of the target park, the amount of carbon emissions reduced by replacing thermal power with clean energy can be visually demonstrated. This not only accurately quantifies the carbon emission reduction effect, but also comprehensively reflects the environmental benefits of clean energy by introducing power generation efficiency and fuel differences. This allows for advance planning of equipment maintenance or adjustment of energy procurement strategies, thereby reducing the park's operating costs.

[0121] In one embodiment of this invention, the carbon emission propagation network is predicted using a preset first carbon emission propagation model based on clean energy data, thermal power equipment data, and purchased energy data, including the following steps:

[0122] S310. Integrate clean energy data, thermal power equipment data and purchased energy data to construct a physical network map of the park. The physical network map of the park is a network map of the supply side.

[0123] S320. For any node in the entity network graph of the park, calculate the instantaneous carbon emission intensity.

[0124] S330. Determine the carbon emission event status of each node based on the instantaneous carbon emission intensity;

[0125] S340. Simulate carbon emission paths by using a preset first carbon emission propagation model, combining the carbon emission event status of each node with the physical network graph of the park.

[0126] S350. Calculate the carbon emission propagation rate, carbon emission recovery rate, and carbon emission propagation time lag between each node based on the carbon emission path.

[0127] S360. Using the Monte Carlo sampling algorithm, combined with carbon emission propagation rate, carbon emission recovery rate and carbon emission propagation time lag, we obtain the propagation probability matrix, the earliest arrival time of carbon propagation and the node risk heat value.

[0128] S370. Based on the propagation probability matrix, the earliest arrival time of carbon propagation, the node risk heat value, and the park entity network map, the supply carbon emission propagation network is obtained.

[0129] This project integrates clean energy data, thermal power equipment data, and purchased energy data to construct a park entity network graph, which serves as a supply-side network graph. In this embodiment, the park entity network graph is a graph-based data structure used to represent entities and their relationships, graphically displaying the various stages and elements of energy production, transmission, and consumption within the park. This involves integrating clean energy data, thermal power equipment data, and purchased energy data, including unification in terms of time dimension, unit, and spatial structure. Unit unification refers to converting coal, gas, and oil consumption into standard energy units such as MJ or kWh. Through this integration, a digital graph of the supply-side energy network is established, including nodes, edges, and attributes. Nodes represent various supply equipment, such as photovoltaic units, wind farms, boilers, gas turbines, and purchased electricity interfaces; edges represent energy transmission relationships, such as power transmission lines and heat pipelines; attributes represent the operating parameters carried by each node and edge, such as power generation, carbon emission factor, efficiency, and fuel type.

[0130] For any node in the park's physical network graph, calculate the instantaneous carbon emission intensity; the instantaneous carbon emission intensity represents the carbon emission corresponding to a unit of output energy at a given moment. Nodes in the park's physical network graph can include thermal power equipment nodes and nodes with purchased energy. For thermal power equipment nodes, collect their fuel consumption, fuel type, lower heating value of the fuel, carbon content per unit of calorific value, actual power generation, and actual heat supply. For nodes with purchased energy, collect their purchased electricity or heat, as well as the marginal carbon emission factor of the external energy source. The carbon emissions of nodes with purchased energy can be calculated by multiplying the purchased energy by the external marginal carbon emission factor. The carbon emissions of thermal power equipment nodes can be calculated by multiplying fuel consumption, lower heating value of the fuel, and carbon content per unit of calorific value. Then, divide the node's carbon emissions by its energy output to obtain the instantaneous carbon emission intensity.

[0131] The carbon emission event status of each node is determined based on instantaneous carbon emission intensity. Instantaneous carbon emission intensity refers to the amount of carbon emissions generated per unit of energy output at a node at a specific moment, reflecting the node's carbon emission activity at that point in time. The carbon emission event status can include the location, propagation path, propagation speed, and impact range of the event. The carbon emission event status helps determine whether a node is currently in an abnormal, high-emission, or normal emission state. First, the instantaneous carbon emission intensity can be compared with a preset carbon emission intensity benchmark to determine whether the node is in a high-emission, abnormal, or normal emission state. Further determination of the carbon emission event status can be achieved using carbon emission residuals and carbon emission ramp-up rates. The carbon emission residual is obtained by subtracting the preset carbon emission intensity benchmark from the current intensity; the carbon emission ramp-up rate refers to the rate at which carbon emission intensity increases per unit time. By judging whether the residual threshold is exceeded and whether a rapid increase in intensity occurs, it is determined whether to mark the node as the starting point of a carbon emission event.

[0132] A pre-set first carbon emission propagation model is used to simulate carbon emission paths by combining the carbon emission event status of each node with the physical network graph of the park. In this embodiment, the pre-trained first carbon emission propagation model is used to simulate the carbon emission process. The carbon emission event status of each node is input into the pre-set carbon emission propagation model, while also incorporating the connection relationships and attributes of nodes in the physical network graph of the park. For direct emission nodes, the pre-set first carbon emission propagation model calculates the CO2 production of the node based on its status and distributes the carbon emissions to other relevant nodes along the energy transmission paths in the graph. For nodes that purchase external energy, the pre-set first carbon emission propagation model calculates the carbon emissions brought about by this energy source based on its carbon emission intensity and input amount, and distributes it to the power-consuming equipment nodes through the power transmission paths in the graph. Finally, the simulated carbon emission paths are output, showing the flow process of greenhouse gases such as CO2 from the generating nodes to other nodes in the park's energy network, including information such as the carbon emission amount and propagation direction on each path.

[0133] The carbon emission propagation rate, carbon emission recovery rate, and carbon emission propagation lag are calculated for each node based on the carbon emission path. The carbon emission propagation rate represents the ability or possibility for carbon emissions to "transfer" from one node to another. The carbon emission recovery rate represents the ability of a node to mitigate or neutralize the impact of carbon emissions. The carbon emission propagation lag refers to the time delay required for the impact of carbon emissions to be transmitted from one node to another. The formula for calculating the carbon emission propagation rate is shown below:

[0134]

[0135] Where Tij represents the propagation rate from node i to node j; Ni represents all neighboring nodes of node i; and Wij represents the weight of the connection edges between nodes. This represents the summation of the weights of all adjacent edges of node i. Since Wik is dimensionless, the summation result is also dimensionless (dimension 1).

[0136] The formula for calculating the carbon emission recovery rate is as follows:

[0137]

[0138] R represents the proportion of clean energy; F represents the node's flexible load adjustment capability; C represents carbon capture efficiency; the coefficient α+β+γ=1, weighted according to the scenario;

[0139] The clean energy share represents the proportion of clean energy used by a node in its total energy consumption. This can be obtained from the energy and carbon management system, showing the node's energy usage data for a specific period. A node's flexible load regulation capability describes its ability to adjust load, such as adjustable electrical load, peak-shaving capacity, and interruptible load capacity. High flexibility means the node can quickly respond to control commands during periods of abnormal carbon emissions. This can be assessed by extracting data from equipment or energy-consuming units' operating data and load characteristic parameters, combined with information such as production processes and energy consumption patterns. Carbon capture efficiency can be obtained by demonstrating the carbon collection efficiency of the capture system, based on the deployment of carbon capture devices at the node.

[0140] The formula for calculating the carbon emission propagation time lag is as follows:

[0141]

[0142] Among them, D ij V represents the physical distance between nodes; V represents the energy / heat transfer speed. This indicates the response time of the control system. Control system response time refers to the time required to respond in energy dispatching and carbon control systems. For example, if a thermal power plant in a park receives a load control command and the response time is 2 seconds, it means the equipment can complete the status adjustment within 2 seconds.

[0143] This study utilizes the Monte Carlo sampling algorithm, combining carbon emission propagation rate, carbon emission recovery rate, and carbon emission propagation lag, to derive the propagation probability matrix, earliest arrival time of carbon emission, and node risk heat value. The Monte Carlo sampling algorithm is a probabilistic statistical calculation method that uses extensive random sampling to simulate and solve problems in mathematics, physics, engineering, and other fields. The propagation probability matrix is ​​a matrix where each element represents the probability of carbon emission propagating from one node to another in the park's physical network graph. The earliest arrival time of carbon emission refers to the shortest time for carbon emission to propagate from the source node to the target node in the park's physical network graph. The node risk heat value is an indicator that comprehensively assesses the degree of carbon emission risk at nodes in the park's physical network graph. In other words, the carbon emission path is simulated multiple times, and the carbon emission propagation rate, carbon emission recovery rate, and carbon emission propagation lag are calculated for each simulation. Then, the Monte Carlo sampling algorithm is used to construct the propagation probability matrix, the earliest arrival time of carbon emission, and the node risk heat value through random sampling. Next, multiple rounds of simulated sampling are performed. In each round, based on the propagation rate, it is randomly determined whether the virus spreads from one node to the next. If it spreads, the propagation lag is considered, and the time is calculated. If a node has a recovery rate, the propagation may be interrupted. Subsequently, a propagation probability matrix is ​​obtained, which represents the probability of propagation occurring between each node. The earliest time each node is affected in all simulations is recorded, and the frequency of each node being affected is calculated to form a node risk heat value.

[0144] The supply carbon emission propagation network is derived based on the propagation probability matrix, the earliest arrival time of carbon propagation, node risk heat values, and the park's physical network graph. Node risk heat values ​​are labeled on the node graph of the park's physical network graph. Node information can also be edited, including node type, historical carbon emission data, current operating status, and a list of connections with other nodes. Next, the propagation probability is displayed, visualizing the edges representing node connections in the park's physical network graph based on values ​​from the propagation probability matrix. The earliest arrival time of carbon propagation is labeled for each edge. A time stamp format can be used, such as an earliest arrival time of 3 minutes, with the font size adjusted appropriately according to the time duration. The layout of nodes and edges in the supply carbon emission propagation network can be optimized based on the actual geographical layout of the park or energy logic relationships to better align with cognitive habits. For example, thermal power equipment nodes can be concentrated on one side, electrical equipment nodes distributed by region on the other side, and transmission line edges clearly connected, thus obtaining the supply carbon emission propagation network.

[0145] By forming a supply-side carbon emission propagation network, we can improve the accuracy and timeliness of carbon emission perception, enhance the traceability of carbon emission propagation paths and intensity, quantify the carbon reduction effect of clean energy, and provide strong decision support for carbon emission analysis and clean energy promotion in the park.

[0146] In one embodiment of this invention, the nodes of the park entity network graph include thermal power equipment nodes. For any node in the park entity network graph, calculating the instantaneous carbon emission intensity includes the following steps:

[0147] S410. When the node is a thermal power equipment node, obtain the basic operating data of each thermal power equipment node. The basic operating data includes fuel consumption, unit carbon emission factor, actual power generation and actual heat supply.

[0148] S420. Calculate the electrical side allocation coefficient and the thermal side allocation coefficient based on the basic operating data;

[0149] S430, calculate the total carbon emissions of thermal power equipment by multiplying the fuel consumption and the unit carbon emission factor;

[0150] S440. Multiply the total carbon emissions of the thermal power equipment by the electric side allocation coefficient and the thermal side allocation coefficient respectively to obtain the electric side carbon emissions and the thermal side carbon emissions.

[0151] S450. The carbon emission intensity on the electric side and the carbon emission intensity on the thermal side are calculated using the ratio between the carbon emissions on the electric side and the actual power generation, and the carbon emissions on the thermal side and the actual heat supply. The carbon emission intensity and the carbon emission intensity on the thermal side are used to characterize the instantaneous carbon emission intensity.

[0152] When the node is a thermal power equipment node, the basic operating data of each thermal power equipment node is obtained. The basic operating data includes fuel consumption, carbon emission factor per unit, actual power generation, and actual heat supply. A thermal power equipment node refers to a node in the park that is used to simultaneously produce electricity and heat. Fuel consumption refers to the amount of fuel consumed by the thermal power equipment during a certain operating period. Carbon emission factor per unit represents the carbon emissions corresponding to a unit of fuel consumption. Actual power generation refers to the amount of electricity actually produced by the thermal power equipment during the operating period and transmitted to the power grid or the power system within the park. Actual heat supply refers to the heat provided by the equipment to users in the park through heating pipelines and other facilities during the operating period.

[0153] Based on basic operating data, the electricity-side allocation coefficient and the heat-side allocation coefficient are calculated. The electricity-side allocation coefficient (KE) represents the proportion of various indicators generated by the fuel consumption of the thermal power equipment in the power generation stage; the heat-side allocation coefficient (KQ) represents the proportion of various indicators generated by the fuel consumption of the thermal power equipment in the heating stage, and KE + KQ = 1, meaning that all indicators of fuel consumption are allocated to both the electricity and heat stages. Specifically, the fuel consumption B, the lower heating value q of the fuel, and the actual power generation E are obtained from the basic operating data. The total heat Q released by fuel combustion is calculated by multiplying the fuel consumption and the lower heating value of the fuel. Therefore, the formula for calculating the electricity-side allocation coefficient (KE) is as follows:

[0154]

[0155] Where E represents the actual power generation; KE represents the electricity-side allocation factor; Q represents the total heat released by fuel combustion; and 3.6 represents the unit of electrical energy (kW). h can be converted to the heat unit MJ.

[0156] The formula for calculating the heat-side allocation factor (KQ) is as follows:

[0157]

[0158] Where KQ represents the heat-side allocation coefficient; E represents the actual power generation; Q represents the total heat released by fuel combustion; and 3.6 represents the unit of electrical energy (kW). h can be converted to the heat unit MJ.

[0159] The total carbon emissions of a thermal power plant are calculated by multiplying fuel consumption and the carbon emission factor per unit. Fuel consumption refers to the actual amount of fuel consumed during operation; the carbon emission factor per unit refers to the amount of CO emitted per unit of fuel burned. The total carbon emissions of the thermal power plant are obtained by multiplying fuel consumption and the carbon emission factor per unit.

[0160] Next, the total carbon emissions from the thermal power equipment are multiplied by the electricity-side allocation coefficient and the heat-side allocation coefficient, respectively, to obtain the electricity-side carbon emissions and heat-side carbon emissions. Total carbon emissions represent the total carbon emissions generated after the thermal power equipment burns fuel. The electricity-side allocation coefficient is a proportional coefficient used to allocate total carbon emissions to the electricity side; the heat-side allocation coefficient is a proportional coefficient used to allocate total carbon emissions to the heat side. Electricity-side allocation coefficient + heat-side allocation coefficient = 1. That is, electricity-side carbon emissions = total carbon emissions from the thermal power equipment × electricity-side allocation coefficient, and heat-side carbon emissions = total carbon emissions from the thermal power equipment × heat-side allocation coefficient. The electricity-side carbon emissions and heat-side carbon emissions are calculated using the above two formulas.

[0161] The carbon emission intensity on the electric side and the carbon emission intensity on the thermal side are calculated using the ratios between the carbon emissions on the electric side and the actual power generation, and between the carbon emissions on the thermal side and the actual heat supply. These carbon emission intensities are used to characterize the instantaneous carbon emission intensity. In this embodiment, to accurately measure the carbon emission efficiency of the thermoelectric equipment during the output of electrical and thermal energy, the following steps are used to calculate the carbon emission intensity on the electric side and the carbon emission intensity on the thermal side, respectively, and these are used to characterize the instantaneous carbon emission intensity of the thermoelectric equipment at a specific moment. That is, the carbon emission intensity on the electric side equals the carbon emissions on the electric side divided by the actual power generation; the carbon emission intensity on the thermal side equals the carbon emissions on the thermal side divided by the actual heat supply. The carbon emission intensity on the electric side and the carbon emission intensity on the thermal side are used as important indicators to describe the carbon emission efficiency of the equipment at the current moment, and the instantaneous carbon emission intensity is quantitatively characterized in combination with the current operating mode of the equipment. This intensity can be used to identify potential high-emission nodes and further assist in the identification of carbon emission event states and the simulation of propagation paths.

[0162] By calculating the instantaneous carbon emission intensity of thermal power equipment nodes, carbon emission contributions can be accurately broken down, clarifying the carbon emission ratios of power generation and heating, providing an important reference for park energy planning. At the same time, using carbon emission intensity to intuitively reflect equipment efficiency can identify inefficient equipment for optimization, provide basic data for carbon emission source tracking and real-time carbon emission monitoring, and build a carbon emission propagation network, providing a scientific basis for the formulation of carbon emission reduction measures and energy management in the park.

[0163] In one embodiment of this example, determining the carbon emission event status of each node based on the instantaneous carbon emission intensity includes the following steps:

[0164] S510. Calculate the carbon emission residual value within a preset time period by combining the instantaneous carbon emission intensity and the preset intensity benchmark value, and calculate the carbon emission ramp-up rate based on the carbon emission residual value within the preset time period.

[0165] S520. Nodes with carbon emission residual values ​​greater than or equal to a preset residual threshold and carbon emission ramp-up rates greater than or equal to a preset ramp-up rate threshold are designated as carbon emission event starting nodes, and the event occurrence location of each carbon emission event starting node is determined.

[0166] S530. Calculate the node edge weight values ​​between each node based on the location of the event, and determine the propagation path set based on the node edge weight values.

[0167] S540. Determine the duration of the carbon emission event state of each node in each propagation path set;

[0168] S550: High carbon emission sources are screened by combining duration and propagation path sets. High carbon emission sources are used to characterize the state of carbon emission events.

[0169] The carbon emission residual value within a preset time period is calculated by combining the instantaneous carbon emission intensity and a preset intensity benchmark value. The carbon emission ramp-up rate is then calculated based on this residual value. Instantaneous carbon emission intensity refers to the carbon emissions per unit of energy output at a specific node at a given moment; it is a real-time expression of carbon emission intensity. The preset intensity benchmark value refers to a pre-set standard reference value for carbon emission intensity. The carbon emission residual value represents the difference between the instantaneous carbon emission intensity and the benchmark value, used to quantify the degree of deviation at that node at that moment. The carbon emission ramp-up rate measures the growth trend of the carbon emission residual value per unit time, reflecting whether carbon emissions are accelerating. The carbon emission ramp-up rate can be calculated by dividing the residual change value within the preset time period by the length of that time period.

[0170] Nodes with carbon emission residual values ​​greater than or equal to a preset residual threshold and carbon emission ramp-up rate greater than or equal to a preset ramp-up rate threshold are designated as the starting points of carbon emission events. The location of each carbon emission event starting point is determined. A carbon emission event starting point node refers to a node where carbon emission intensity increases abnormally and at a significant rate within a certain time period; it can be considered an emission source. The preset residual threshold and preset ramp-up rate threshold are predefined benchmark values ​​by the system administrator or model, used to determine whether emission behavior constitutes an "event." The event location can correspond to the actual device number, system number, or GIS geographic coordinates. In other words, when the carbon emission residual value of a node is greater than or equal to the system's preset residual threshold (i.e., its emission level is significantly higher than normal), and the carbon emission ramp-up rate is greater than or equal to the preset ramp-up rate threshold (i.e., the abnormal value shows a rapid upward trend), then that node is identified as the starting point of a carbon emission event, indicating that this node is highly likely to be the initial source of a sudden carbon emission propagation behavior. Furthermore, the system will record the location information of the aforementioned starting node in the park's physical network graph as the location where the carbon emission event occurred, and use it as a basic reference point for subsequent carbon propagation path simulation and control scheduling.

[0171] The edge weights between each node are calculated based on the location of the event, and the set of propagation paths is determined accordingly. The event location refers to the location of the node identified as the starting point of the carbon emission event, which can be a thermal power plant, energy exchange station, etc. The edge weight is a numerical parameter in the network graph used to describe the probability or intensity of carbon emission propagation between two nodes. The set of propagation paths is the set of possible paths for carbon emission propagation in the network, determined by the edge weights. For any two nodes i and j with a direct connection, the edge weight w is calculated as follows: ij ,

[0172]

[0173] Among them, w ijThe values ​​represent the node edge weights; Mij represents the energy flow intensity; Nij represents the historical carbon propagation frequency; α1, β1, and γ1 represent the weighting coefficients. α1, β1, and γ1 can be set according to actual conditions. Energy flow intensity represents the amount of energy transferred through the connection between node i and node j per unit time; historical carbon propagation frequency represents the number or frequency of carbon emission propagation events that have occurred on the path from node i to j in past operating cycles.

[0174] The duration of the carbon emission event state for each node in the propagation path set is determined. Starting from the origin of the carbon emission event, all possible paths are inferred based on propagation probability, historical propagation records, and network topology. Each path is a sequence of multiple nodes. The duration of the event state refers to the length of time a node is "affected by the carbon emission event" in a certain propagation path. That is, for each node affected by the abnormality on each possible route of the carbon emission event spreading from the origin node, the time from the occurrence of the carbon emission anomaly to its recovery to normal is calculated. Taking the origin of the event as the source, the propagation process of carbon emissions in the path set is simulated; the time when each node enters the event-affected state is recorded; the duration is calculated and used as one of the risk indicators of the node in the propagation path.

[0175] Subsequently, high-carbon emission sources are screened by combining duration and propagation path set. High-carbon emission sources are used to characterize the carbon emission event status. A high-carbon emission source refers to a key source node with strong propagation ability, long carbon emission duration, and high propagation rate across multiple paths. The carbon emission event status reflects whether the current system is in an abnormal carbon emission state, such as a device generating excessive carbon emissions due to decreased efficiency. By analyzing the event duration of each node in the path, we identify which nodes are in a high-emission state for extended periods, and assess the "propagation breadth" of each node by combining factors such as the number of paths and path length. If a node not only has strong emissions but also a wide impact range, it is more likely to be identified as a "high-carbon emission source." Carbon emission event status may include high-carbon emission sources, low-intensity carbon emission sources, and medium-intensity carbon emission sources; this embodiment considers high-carbon emission sources.

[0176] By calculating carbon emission residuals and ramp-up rates, the starting points of carbon emission events can be identified, and the propagation paths of carbon emissions can be simulated. This can effectively prevent the spread of carbon emissions and provide forward-looking information for park managers. Identifying high-carbon emission sources helps parks to accurately reduce carbon emissions and optimize energy, build a dynamic and intelligent carbon emission propagation analysis and prediction mechanism, and improve the efficiency of carbon emission management.

[0177] In one embodiment of this example, generating preliminary control instructions based on carbon emission factors, the supply carbon emission propagation network, and the demand carbon emission propagation network, the preliminary control instructions include equipment execution instructions on the supply side and load control instructions on the demand side, comprising the following steps:

[0178] S610. Obtain the fuel type and average power generation efficiency of thermal power equipment, as well as the marginal carbon emission factor of purchased energy, through the energy carbon management system.

[0179] S620. Determine the standard emission factor corresponding to the fuel type;

[0180] S630. Calculate the effective carbon emission factor based on the standard emission factor and average power generation efficiency.

[0181] S640. Determine the supply node centrality among nodes in the supply carbon emission propagation network and the demand node centrality among nodes in the demand carbon emission propagation network, respectively. The supply node centrality and demand node centrality are used to show the importance of nodes.

[0182] S650. For any node in the supply carbon emission propagation network and the demand carbon emission propagation network, assign a centrality coefficient to each node according to the centrality of the supply node and the centrality of the demand node.

[0183] S660. Determine the effective carbon emission factor and marginal carbon emission factor for each node respectively, and calculate the node superposition weight for each node based on the effective carbon emission factor, marginal carbon emission factor and the centrality coefficient assigned to each node.

[0184] S670. Generate preliminary control instructions based on the node superposition weights corresponding to each node.

[0185] The carbon management system obtains information on the fuel type and average power generation efficiency of cogeneration equipment, as well as the marginal carbon emission factor of purchased energy. The fuel type of cogeneration equipment refers to the type of energy used in combined heat and power (CHP) or thermal power generation equipment; for example, different fuel types such as coal, natural gas, biomass fuel, and oil fuel correspond to different carbon emission levels. Average power generation efficiency refers to the ratio of electricity output to input energy. The marginal carbon emission factor of purchased energy refers to the carbon emissions per kilowatt-hour of electricity purchased from the grid or a third party; this depends on the current energy structure of the grid, for example, higher coal consumption results in a higher carbon factor, while higher hydropower consumption results in a lower carbon factor.

[0186] Determining the standard emission factor corresponding to a fuel type can be done by retrieving the carbon content per unit calorific value and the standard CO2 emission factor for that fuel type from a pre-defined standard emission factor database. The standard emission factor refers to the amount of carbon dioxide emitted per unit of fuel during complete combustion. Different types of fuels contain different carbon densities, and therefore produce different amounts of carbon dioxide after combustion. Thus, the standard emission factor is a numerical indicator uniformly determined based on factors such as fuel type, calorific value, and carbon content.

[0187] The effective carbon emission factor is calculated based on the standard emission factor and average power generation efficiency. The standard emission factor refers to the carbon dioxide emissions produced when a unit of fuel with a certain calorific value is completely burned. Average power generation efficiency refers to the overall efficiency of power generation equipment in converting the calorific value of input fuel into electrical energy over a specific period. The effective carbon emission factor refers to the actual carbon emissions produced per unit of electrical energy after considering equipment efficiency losses, reflecting true carbon intensity. The formula for calculating the effective carbon emission factor is shown below:

[0188] Effective carbon emission factor = Standard emission factor × Average power generation efficiency

[0189] During power generation, carbon emissions from fuel are not entirely directly related to the actual electricity produced, but are influenced by power generation efficiency. Average power generation efficiency determines the proportion of fuel energy actually used for power generation, while the standard emission factor determines the amount of carbon emissions corresponding to that portion of fuel actually involved in power generation. By mathematically correlating the two, the effective carbon emission factor (i.e., effective carbon emission factor) corresponding to each unit of electricity output can be obtained.

[0190] The supply node centrality among nodes in the supply carbon emission propagation network and the demand node centrality among nodes in the demand carbon emission propagation network are determined separately. Supply node centrality and demand node centrality are used to indicate the importance of nodes. Specifically, topological analysis is performed on all nodes in both the supply and demand carbon emission propagation networks to calculate the degree centrality of each node. In this embodiment, the determination of supply and demand node centrality is based on degree centrality, which measures the importance or influence of nodes in the network. A node with higher degree centrality indicates that it has a greater regulatory weight or emission radiation range in the carbon emission propagation process, and is considered a key node in carbon emission propagation. Node centrality is an indicator of how important a node is in the supply and demand carbon emission propagation networks. For example, a power plant or power generation equipment supplies electricity to many enterprise users; the more downstream nodes it connects to in the supply network, the higher its importance.

[0191] Next, for any node in either the supply or demand carbon emission propagation network, a centrality coefficient is assigned to each node based on its supply and demand centrality. The centrality coefficient is a numerical indicator used in network analysis to quantify the importance or influence of a single node within the entire network. In other words, after obtaining the centrality of each node in both the supply and demand carbon emission propagation networks, network analysis tools, such as Python's NetworkX library, are used to determine the centrality value of each node within its corresponding network; this value is the centrality coefficient. A higher coefficient indicates a more central node within the network. For example, in the supply network, steel mill A has a betweenness centrality coefficient of 0.8, while steel mill B has 0.3, indicating that A is a more critical carbon emission hub.

[0192] The effective carbon emission factor and marginal carbon emission factor for each node are determined separately. The node superposition weight for each node is calculated based on these factors and the assigned centrality coefficient. First, the attributes of each node are determined. Based on these attributes, the node's type is identified. When a node is a thermal power equipment node, its superposition weight is calculated using its effective carbon emission factor and corresponding centrality coefficient. When a node represents clean energy, its superposition weight is calculated using its marginal carbon emission factor and corresponding centrality coefficient. Thermal power equipment burns fossil fuels (such as coal, natural gas, and oil), and its carbon emissions are determined by equipment efficiency and fuel characteristics. Therefore, the superposition weight for this type of node is primarily based on its effective carbon emission factor and centrality coefficient. The node superposition weight equals the effective carbon emission factor multiplied by the centrality coefficient. Thermal power equipment with high carbon emission intensity and an important position in the network receives a higher weight and is given a higher priority for regulation. Clean energy itself has near-zero carbon emissions, but its dispatch needs to consider the "external substitution effect," i.e., the emission reduction contribution when clean electricity replaces high-carbon electricity. Therefore, the weight of such nodes is mainly based on the marginal carbon emission factor and the centrality coefficient. The node superposition weight equals the node's marginal carbon emission factor multiplied by the centrality coefficient, indicating that the control value of clean energy nodes is greater during periods of high marginal emission intensity in the power grid. Thus, each node receives a superposition weight that considers not only the carbon factor but also the node's positional importance in the network structure. For thermal power equipment nodes, since thermal power equipment burns fossil fuels, its carbon emissions are determined by equipment efficiency and fuel characteristics. Therefore, the node superposition weight is calculated using the effective carbon emission factor (related to fuel type and power generation efficiency) and the centrality coefficient. For clean energy nodes, since clean energy itself has near-zero carbon emissions, but its dispatch needs to consider the external substitution effect (i.e., the emission reduction contribution when clean electricity replaces high-carbon electricity, which is related to the marginal carbon emission factor of purchased energy), the node superposition weight is calculated using the marginal carbon emission factor and the centrality coefficient. In addition, node weighting is a quantitative indicator that comprehensively reflects the importance of a node in the carbon emission propagation network and its own carbon emission-related characteristics. Specifically, the centrality coefficient reflects the importance of a node in the supply or demand carbon emission propagation network. Nodes with high centrality may play a more critical connecting or transmitting role in the network, and have a greater impact on the overall carbon emission propagation. The effective carbon emission factor is related to the fuel type and power generation efficiency of equipment (such as thermal power equipment), reflecting the carbon emission characteristics of the energy conversion process involved in the node. Different fuel types and power generation efficiencies will lead to different effective carbon emission factors, which reflect the carbon emission level of the node's own energy utilization process. The marginal carbon emission factor is the carbon emission situation of purchased energy, reflecting the marginal carbon emission impact brought about by acquiring an additional unit of energy.The node superposition weight, calculated by combining these three factors, can quantify the overall impact of a node. It combines a node's structural importance in the network (through its centrality coefficient) with the carbon emission characteristics of its energy use and purchased energy (through effective carbon emission factors and marginal carbon emission factors) to comprehensively measure the node's overall impact on the entire carbon emission system. When generating preliminary control instructions, the node superposition weight provides a basis for determining which nodes require priority control and the appropriate level of control. A node with a high weight likely has a greater impact on overall carbon emissions, whether due to its key position in the network, its own high carbon emission level, or the high marginal carbon emission of its purchased energy. Such nodes require more attention and targeted measures in control. For example, if a node is located at the core of the carbon emission propagation network (high centrality coefficient), and its fuel has a high carbon emission factor (large effective carbon emission factor) and its purchased energy also has a high marginal carbon emission factor, then its node superposition weight will be high. In carbon emission control, it is necessary to focus on optimizing the node's energy use, upgrading equipment, or adjusting energy procurement strategies to reduce overall carbon emissions.

[0193] Finally, preliminary control instructions are generated based on the node weights associated with each node. A higher node weight indicates a more significant impact on overall carbon emissions and network operation; therefore, the system prioritizes nodes with higher weights when formulating control instructions. These preliminary control instructions are divided into supply-side and demand-side instructions. On the supply side, instructions to reduce output or power generation are generated for high-carbon emission equipment with high weights; conversely, instructions to increase output or prioritize dispatch are generated for clean energy equipment with high weights. On the demand side, instructions to reduce load or shift peak loads are generated for high-load nodes with high weights; instructions to maintain the status quo or implement low-priority control may be generated for flexible load nodes with lower weights.

[0194] By generating preliminary control commands based on carbon emission factors, the supply carbon emission propagation network, and the demand carbon emission propagation network, the actual carbon emission levels of equipment can be more accurately reflected, thereby improving the dynamic accuracy of carbon accounting. Assigning node centrality coefficients and incorporating topology and operational impacts into the weighting calculation helps avoid judging node importance solely based on single-point emissions. Preliminary control commands generated based on node weights enable differentiated regulation of different types of nodes, prioritizing high-weight nodes and secondarily regulating low-weight nodes, thus improving the overall carbon reduction efficiency of the system.

[0195] In one embodiment of this example, determining the supply node centrality among nodes in the supply carbon emission propagation network and the demand node centrality among nodes in the demand carbon emission propagation network includes the following steps:

[0196] S710. For any given node, determine the number of transmission lines between each node and its adjacent node.

[0197] S720. Demand node centrality and supply node centrality are obtained using a preset centrality calculation formula based on the total number of nodes and the number of transmission lines.

[0198] For any given node, determine the number of transmission lines between each node and its adjacent nodes. In this embodiment, adjacent nodes refer to other nodes that have a direct power transmission connection with the given node. The number of transmission lines represents the actual number of connections between the given node and its adjacent nodes. When constructing the carbon emission propagation network of the park, each node is connected to other nodes via transmission lines. In this embodiment, transmission lines are used to characterize the amount of power supplied to adjacent nodes. For example, the amount of power supplied by a thermal power equipment node to downstream users is represented by the number of transmission lines. The more transmission lines a node has, the higher its connectivity in the network. In the carbon emission propagation model, the number of lines can be used as a weighted basis for "degree".

[0199] Demand node centrality and supply node centrality are obtained using a pre-defined centrality calculation formula based on the total number of nodes and the number of transmission lines. The total number of nodes refers to the total number of nodes in the entire network. The number of transmission lines refers to the number of direct connections between a node and its neighboring nodes, which is the node's "degree". A higher degree indicates a closer connection with other nodes and a more important location. The pre-defined centrality calculation formula is as follows:

[0200]

[0201] Among them, C D(V) D represents node centrality; eg(v) This represents the number of transmission lines; n represents the total number of nodes.

[0202] By first determining the number of transmission lines between any node and its neighboring nodes, and then using a pre-defined centrality calculation formula based on the total number of nodes and the number of transmission lines, the supply node centrality of nodes in the supply carbon emission propagation network and the demand node centrality of nodes in the demand carbon emission propagation network can be obtained respectively. This allows for a quantitative assessment of the importance of nodes on the supply and demand sides of the carbon emission propagation network, which helps to understand and manage the carbon emission propagation process more scientifically.

[0203] In one embodiment of this invention, the equipment execution commands include thermoelectric energy equipment and clean energy equipment. Adjusting the equipment execution commands and load control commands of the target park based on carbon deviation results and a preset carbon credit mechanism to obtain energy-carbon coordinated control commands includes the following steps:

[0204] S810. Obtain the carbon credits of high-carbon emission equipment and clean energy equipment in the thermoelectric energy equipment. If the carbon credits are less than the preset carbon credit threshold, determine the carbon deviation allocation value corresponding to the high-carbon emission equipment based on the carbon deviation results.

[0205] S820. Calculate the reduction in power generation of thermal power equipment based on the carbon deviation allocation value;

[0206] S830. If the carbon credit of the clean energy equipment is greater than the preset carbon credit threshold, the clean energy power generation shall be calculated based on the carbon credit of the clean energy equipment.

[0207] S840, respectively converts the reduction in power generation and the amount of clean energy power generation into power adjustment values ​​within a preset time period to obtain the reduced power generation of high-carbon emission equipment and the increased power generation of clean energy equipment;

[0208] S850: Based on the power generation reduction and power generation increase on the supply side, the demand side of the park load is predicted.

[0209] S860: Identify the net adjustment gap on the demand side based on the park's load demand and load control instructions;

[0210] S870: Adjust the load control command through the net regulation gap to obtain the energy-carbon coordinated control command.

[0211] The process involves acquiring the carbon credits of high-carbon-emission equipment and clean energy equipment within a combined heat and power (CHP) energy system. If the carbon credit is less than a preset threshold, the carbon deviation allocation value corresponding to the high-carbon-emission equipment is determined based on the carbon deviation results. CHP energy equipment refers to energy equipment or systems that produce both heat and electricity. For example, a combined heat and power (CHP) plant burns fuel to generate steam that drives a turbine to generate electricity, and simultaneously uses the waste heat generated during power generation to heat the surrounding area. Carbon credits are a quantitative indicator of an equipment's carbon emission performance; they can be understood as scoring the equipment's carbon emission "behavior." For high-carbon-emission equipment, carbon credits may be calculated based on factors such as its actual carbon emissions and carbon emissions per unit of energy output. For clean energy equipment, carbon credits may be calculated based on factors such as the reduction in carbon emissions resulting from replacing traditional high-carbon energy sources. The preset carbon credit threshold is a pre-set standard value; the carbon deviation result refers to the difference between the actual carbon emissions and the target carbon emissions. When a carbon deviation occurs, i.e., the carbon emission reduction target is not met or the carbon emissions exceed the standard, the portion of the carbon deviation that high-carbon emission equipment must bear is calculated according to certain rules.

[0212] The reduction in power generation from thermal power (CHP) equipment is calculated based on the carbon deviation allocation value. The carbon deviation allocation value refers to the portion of the carbon deviation that high-carbon-emission equipment must bear, calculated according to certain rules, when there is a deviation in the actual carbon emissions of a CHP system. The power generation of high-carbon-emission equipment is closely related to its carbon emissions; it is assumed that for every certain amount of electricity generated by such equipment, a fixed proportion of carbon emissions will occur. Knowing the carbon deviation allocation value, and based on the correspondence between equipment power generation and carbon emissions, the amount of power generation that the equipment needs to reduce to offset this carbon deviation allocation value can be deduced. For example, if X tons of carbon dioxide correspond to a proportional reduction of Y kilowatt-hours in power generation, then Y kilowatt-hours is the reduction in power generation from the CHP equipment.

[0213] If the carbon credit of a clean energy device exceeds a preset carbon credit threshold, the clean energy power generation is calculated based on the device's carbon credit. The preset carbon credit threshold is a pre-defined standard value used to determine whether the clean energy device's carbon emission reduction contribution meets expectations or requirements. First, it is determined whether the clean energy device's carbon credit exceeds the preset threshold. If it does, it indicates that the device performs well in carbon emission reduction, meeting or exceeding the expected carbon contribution standard. Then, its clean energy power generation is calculated based on the device's carbon credit. Because carbon credits are often related to power generation, the power generation can be inferred from the carbon credits. For example, if the carbon credit calculation rule is 10 carbon credits for every 1000 kWh of electricity generated, and a solar photovoltaic power station has 600 carbon credits, then by calculating 600 ÷ 10 × 1000 = 60000 kWh, its clean energy power generation can be determined to be 60000 kWh.

[0214] The reduction in power generation and the increase in clean energy power generation are respectively converted into power adjustment values ​​within a preset time period to obtain the reduced power generation of high-carbon emission equipment and the increased power generation of clean energy equipment. The reduction in power generation refers to the amount of power generation that high-carbon emission equipment needs to reduce, based on previous calculations (such as those based on carbon offset values). Clean energy power generation is the actual or expected power generation of clean energy equipment, calculated through methods such as carbon credits. The preset time period is a pre-defined time length used to uniformly measure power generation. The power reduction is for high-carbon emission equipment, converting the reduction in power generation into a decrease in power generation per unit time within the preset time period. For example, if the reduction in power generation is 10,000 kWh and the preset time period is 1 hour, then the power reduction is 10,000 ÷ 1 = 10,000 kW (i.e., 10,000 kWh less electricity is generated per hour, resulting in a power reduction of 10,000 kW). The increased power generation is for clean energy equipment, converting the clean energy power generation into an increase in power generation per unit time within the preset time period. For example, if clean energy generation is 80,000 kWh and the preset time period is 1 hour, the increased power output would be 80,000 ÷ 1 = 80,000 kW (i.e., generating an additional 80,000 kWh per hour, resulting in an increase of 80,000 kW in power). Both the reduction in power generation and the increase in clean energy generation are in terms of electricity volume, but power system operation and control focus more on power output. Therefore, a unit conversion is needed: dividing the electricity volume by the preset time period to obtain the power adjustment value. For example, let the reduction in power generation be E. 减 (degree), with a preset time period of T (hours), then the power reduction P of high-carbon emission equipment 降 =E 减 ÷T; Let the clean energy power generation be E. 清 (degree), with a preset time period of T (hours), then the increased power P of the clean energy equipment 增 =E 清 ÷T.

[0215] The demand-side load demand of industrial parks is predicted by forecasting the power generation reduction and increase on the supply side. Adjustments in power generation on the supply side affect the overall power supply of the power system. Assuming the power system is a balanced whole (ideally, supply equals demand), when high-carbon emission equipment reduces power generation and clean energy equipment increases power generation on the supply side, it is necessary to predict the impact of this supply change on the demand-side load demand of industrial parks. Predictive models can be built based on historical data and the operating patterns of the power system. For example, past data shows that when the power generation of clean energy equipment on the supply side increases by 10MW, the load demand of a certain type of industrial enterprise in the park (with a certain preference or adaptability to clean energy electricity) may increase by 5MW (because clean energy electricity may be cheaper or more stable, leading enterprises to expand production); simultaneously, when the power generation of high-carbon emission equipment decreases by 8MW, the load demand of another part of the park that relies on traditional electricity and is price-sensitive may decrease by 3MW due to power supply shortages or price increases. By comprehensively considering the historical relationship between these supply-side power adjustments (power reduction and increase) and changes in various types of load demand in industrial parks on the demand side (determined through big data analysis, mathematical modeling, etc.), the load demand of industrial parks on the demand side can be predicted.

[0216] Identify the net regulation gap on the demand side based on the park's load demand and load control instructions. Park load demand refers to the electricity demand of various electrical devices within the park over a certain period. Load control instructions are commands issued by the power system dispatching department or the park's energy management department to adjust the park's load. The net regulation gap is the difference between the park's actual load demand and the load demand adjusted according to the load control instructions. First, clarify the park's load demand (the normal load demand derived from the operation of electrical equipment within the park, production plans, etc.). Then, receive load control instructions (such as the aforementioned load reduction or increase instructions). Calculate the target load after adjustment according to the load control instructions; for example, if the park's original load demand is L... 原 If the control order requires a 10% reduction in load, then the target load L 目 =L 原 ×(1-10%). Net adjustment gap = |L 原 -L 目 | (The absolute value is taken because only the size of the gap is considered, regardless of whether it exceeds or falls short). For example, if the original load demand of the park is 100MW, and the control order requires it to be reduced to 90MW, the net adjustment gap is |100-90|=10MW.

[0217] The net adjustment gap is used to adjust load control instructions to obtain energy-carbon coordinated control instructions. The net adjustment gap refers to the difference between the actual load demand of the industrial park and the target load after adjustment according to the load control instructions. Load control instructions are orders issued by the power system dispatching department or the park's energy management department to adjust the park's load. Energy-carbon coordinated control instructions are a type of control instruction that comprehensively considers energy (electricity) supply and demand balance and carbon emission control. It not only focuses on adjusting the power load to meet the power system's operational needs but also emphasizes how to reduce carbon emissions through load adjustment. The net adjustment gap reflects the deviation in the actual implementation of load control instructions. By analyzing this gap, the original load control instructions can be optimized. For example, if the net adjustment gap is due to the load control instructions requiring excessively high additional power generation from clean energy equipment, which the relevant equipment in the park cannot actually meet, then when adjusting the instructions, it is necessary to reduce the required additional power generation from that clean energy equipment and, in conjunction with carbon emission targets, form a new energy-carbon coordinated control instruction. In another scenario, if the net regulation gap is due to some high-energy-consuming and high-carbon-emission enterprises in the park failing to respond to the load reduction order, then when adjusting the order, the load control intensity for these enterprises may be increased, and carbon emission reward and punishment measures may be implemented to ensure that the new order can both guarantee power supply and demand and effectively control carbon emissions.

[0218] By identifying net regulation gaps and adjusting load control commands to obtain energy-carbon coordinated control commands, it is possible to achieve refined management of thermal power equipment and clean energy equipment. While ensuring the balance of power supply and demand, it can effectively control carbon emissions, promote the coordinated optimization of energy and environment, and improve the operating efficiency and sustainable development capabilities of the power system. This provides a scientific, systematic and operable solution for energy management and energy-carbon coordinated control in the park.

[0219] This application also provides a machine-readable storage medium storing instructions that cause a machine to execute the above-described digital twin-based integrated energy and carbon management method.

[0220] This application also provides an electronic device, including:

[0221] The memory is configured to store instructions; and

[0222] The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the aforementioned digital twin-based integrated energy and carbon management method.

[0223] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0224] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0225] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0226] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0227] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0228] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0229] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0230] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0231] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A digital twin-based integrated energy and carbon management method, characterized in that, Applied to an energy and carbon management system, which includes a supply side and a demand side, wherein the supply side includes clean energy, thermal power energy, and purchased energy, the method includes: The energy and carbon management system can acquire clean energy data, thermal power equipment data, and purchased energy data from the supply side, as well as planned load data and production process data from the demand side in real time. Based on clean energy data, thermal power equipment data, and purchased energy data, the supply carbon emission propagation network is predicted using a pre-set first carbon emission propagation model. Based on the planned load data and production process data from the demand side, a pre-set second carbon emission propagation prediction model is used to obtain the demand carbon emission propagation network; Carbon emission factors are calculated based on clean energy data, thermal power equipment data, and purchased energy data, respectively. Preliminary control instructions are generated based on carbon emission factors, supply carbon emission propagation networks, and demand carbon emission propagation networks. These preliminary control instructions include equipment execution instructions on the supply side and load control instructions on the demand side. The simulation results are obtained by combining the equipment execution commands and load control commands in the digital twin model, and the carbon deviation is verified based on the simulation results to obtain the carbon deviation results. Based on the carbon deviation results and the preset carbon integral mechanism, the equipment execution instructions and load control instructions of the target park are adjusted to obtain the energy-carbon coordinated control instructions.

2. The method according to claim 1, characterized in that, The method further includes: When the power generation of clean energy is greater than the first preset threshold and the real-time electricity consumption data is less than the second preset threshold, the power generation fuel type of thermoelectric energy is obtained. Determine the carbon content per unit calorific value and the lower heating value of the fuel corresponding to the type of power generation fuel in the pre-set database; The fuel input and electrical output of each power generation unit are obtained, and the average power generation efficiency of each power generation unit is calculated by combining the product of the fuel input and the lower heating value of the fuel with the electrical output. The average carbon emissions per kilowatt-hour are calculated based on the average power generation efficiency and the carbon content per unit calorific value. The equivalent carbon emission reduction for the target park is determined by multiplying the power generation of clean energy by the carbon emission value. The equivalent carbon emission reduction is used to determine the amount of carbon emission reduction achieved by replacing thermal power with clean energy.

3. The method according to claim 1, characterized in that, The step of predicting the supply carbon emission propagation network using a preset first carbon emission propagation model based on clean energy data, thermal power equipment data, and purchased energy data includes the following steps: The park's physical network map is constructed by integrating clean energy data, thermal power equipment data, and purchased energy data. This physical network map serves as a network map for the supply side. For any node in the entity network graph of the park, calculate the instantaneous carbon emission intensity; The carbon emission event status of each node is determined based on the instantaneous carbon emission intensity; The carbon emission path is simulated by using a pre-set first carbon emission propagation model, combined with the carbon emission event status of each node and the network map of the park entities. Calculate the carbon emission propagation rate, carbon emission recovery rate, and carbon emission propagation lag between each node based on the carbon emission pathway; The Monte Carlo sampling algorithm is used to combine carbon emission propagation rate, carbon emission recovery rate and carbon emission propagation time lag to obtain the propagation probability matrix, the earliest arrival time of carbon propagation and the node risk heat value; The carbon emission propagation network is obtained based on the propagation probability matrix, the earliest arrival time of carbon propagation, the node risk heat value, and the park entity network map.

4. The method according to claim 3, characterized in that, The nodes of the park's physical network map include thermal power equipment nodes. Calculating the instantaneous carbon emission intensity for any node in the park's physical network map includes the following steps: When the node is a thermal power equipment node, the basic operating data of each thermal power equipment node is obtained. The basic operating data includes fuel consumption, unit carbon emission factor, actual power generation and actual heat supply. Calculate the electrical side allocation coefficient and the thermal side allocation coefficient based on the basic operating data; The total carbon emissions of thermal power equipment are calculated by multiplying the fuel consumption and the unit carbon emission factor. The total carbon emissions from the thermal power equipment are multiplied by the electric side allocation factor and the thermal side allocation factor, respectively, to obtain the carbon emissions on the electric side and the carbon emissions on the thermal side. The carbon emission intensity on the electric side and the carbon emission intensity on the thermal side are calculated using the ratios between the carbon emissions on the electric side and the actual power generation, and between the carbon emissions on the thermal side and the actual heat supply. These carbon emission intensities are used to characterize the instantaneous carbon emission intensity.

5. The method according to claim 3, characterized in that, The process of determining the carbon emission event status of each node based on instantaneous carbon emission intensity includes the following steps: The carbon emission residual value within a preset time period is calculated by combining the instantaneous carbon emission intensity and the preset intensity benchmark value, and the carbon emission ramp-up rate is calculated based on the carbon emission residual value within the preset time period. Nodes with carbon emission residual values ​​greater than or equal to a preset residual threshold and carbon emission ramp-up rates greater than or equal to a preset ramp-up rate threshold are designated as carbon emission event starting nodes, and the event occurrence location of each carbon emission event starting node is determined. Calculate the node edge weights between each node based on the location of the event, and determine the set of propagation paths based on the node edge weights. Determine the duration of the carbon emission event state corresponding to each node in each propagation path set; High carbon emission sources are screened by combining duration and propagation path sets, and these high carbon emission sources are used to characterize the state of carbon emission events.

6. The method according to claim 1, characterized in that, The process of generating preliminary control instructions based on carbon emission factors, supply carbon emission propagation networks, and demand carbon emission propagation networks, including supply-side equipment execution instructions and demand-side load control instructions, includes the following steps: The energy carbon management system obtains information on the fuel type and average power generation efficiency of thermal power equipment, as well as the marginal carbon emission factor of purchased energy. Determine the standard emission factor corresponding to the fuel type; The effective carbon emission factor is calculated based on the standard emission factor and average power generation efficiency. The supply node centrality among nodes in the supply carbon emission propagation network and the demand node centrality among nodes in the demand carbon emission propagation network are determined separately. The supply node centrality and demand node centrality are used to show the importance of nodes. For any node in the supply carbon emission propagation network and the demand carbon emission propagation network, assign a centrality coefficient to each node based on the centrality of the supply node and the centrality of the demand node. The effective carbon emission factor and marginal carbon emission factor corresponding to each node are determined respectively, and the node superposition weight corresponding to each node is calculated based on the effective carbon emission factor, marginal carbon emission factor and the centrality coefficient assigned to each node. Preliminary control instructions are generated based on the weights of the nodes corresponding to each node.

7. The method according to claim 6, characterized in that, Determining the supply node centrality among nodes in the supply carbon emission propagation network and the demand node centrality among nodes in the demand carbon emission propagation network includes the following steps: For any given node, determine the number of transmission lines between each node and its adjacent node; Demand node centrality and supply node centrality are obtained using a preset centrality calculation formula based on the total number of nodes and the number of transmission lines.

8. The method according to claim 1, characterized in that, The equipment execution commands include those for thermal power equipment and clean energy equipment. The process of adjusting the equipment execution commands and load control commands of the target park based on carbon deviation results and a preset carbon credit mechanism to obtain energy-carbon coordinated control commands includes the following steps: Obtain the carbon credits of high-carbon emission equipment and clean energy equipment in the thermoelectric energy equipment. If the carbon credits are less than the preset carbon credit threshold, determine the carbon deviation allocation value corresponding to the high-carbon emission equipment based on the carbon deviation results. Calculate the reduction in power generation from thermal power equipment based on the carbon deviation allocation value; If the carbon credit of the clean energy equipment is greater than the preset carbon credit threshold, the clean energy power generation will be calculated based on the carbon credit of the clean energy equipment. The reduction in power generation and the amount of clean energy power generation are respectively converted into power adjustment values ​​within a preset time period to obtain the reduced power generation of high-carbon emission equipment and the increased power generation of clean energy equipment. The demand side of the park's load is predicted based on the power generation reduction and power generation increase on the supply side. Identify the net adjustment gap on the demand side based on the park's load demand and load control instructions; Energy and carbon coordinated control commands are obtained by adjusting load control commands through net regulation gaps.

9. A digital twin-based integrated energy and carbon management system, characterized in that, include: The memory is configured to store instructions; as well as A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the energy and carbon integrated management method based on digital twins according to any one of claims 1 to 8.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the energy and carbon integrated management method based on digital twins according to any one of claims 1 to 8.

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