Energy-carbon integrated management method and system based on digital twinning

Abstract

The invention discloses an energy-carbon integrated management method based on digital twinning, and relates to the technical field of energy-carbon management, and the method comprises the steps: obtaining clean energy data, thermoelectric equipment data, outsourcing energy data, planned load data and production process data; predicting by using a preset first carbon emission propagation model to obtain a supply carbon emission propagation network; obtaining a required carbon emission propagation network by using a preset second carbon emission propagation prediction model; respectively calculating carbon emission factors according to the clean energy data, the thermoelectric equipment data and the outsourcing energy data; generating a preliminary regulation and control instruction according to the carbon emission factor, the supply carbon emission propagation network and the demand carbon emission propagation network; performing simulation in combination with the equipment execution instruction and the load regulation and control instruction to obtain a simulation result, and determining a carbon deviation result; and adjusting the equipment execution instruction and the load regulation instruction according to the carbon deviation result and a preset carbon integral mechanism to obtain an energy-carbon coordinated regulation instruction. According to the invention, the accuracy of carbon management can be effectively improved.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of energy-carbon management, in particular to an energy-carbon integrated management method and system based on digital twinning. BACKGROUND

[0002] With the continuous growth of global energy demand and the increasingly serious 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, so optimizing carbon emissions at each link of energy production, transmission and consumption has become a key way to reduce carbon footprint.

[0003] At present, the existing energy management system is in a mode of isolated operation, and the energy production monitoring system, the energy demand metering system and the carbon accounting system are independent of each other. For example, the power plant DCS system records the operating parameters of the power generation equipment, but the carbon accounting system cannot obtain these data in real time and can only manually input them afterwards, resulting in delayed and inaccurate carbon accounting. The wind farm SCADA system can predict wind power output, but the energy distribution system of the power grid cannot directly call the prediction data and still relies on the outdated report manually transmitted. This makes the management systems of different energy types not interoperable, unable to accurately predict the carbon emission behavior of energy production, and leads to inaccurate carbon emission control and increased energy-carbon management cost.

[0004] At present, there is no good solution to the above problems. SUMMARY

[0005] The embodiment of the present application provides an energy-carbon integrated management method and system based on digital twinning, which is used to improve the accuracy of energy-carbon management and reduce the cost of energy-carbon management.

[0006] To achieve the above purpose, the embodiments of the present application adopt the following technical solutions: In a first aspect, an energy-carbon integrated management method based on digital twinning is provided, applied to an energy-carbon management system, the energy-carbon management system including a supply side and a demand side, the supply side including clean energy, thermal power energy and purchased energy, the method comprising: real-time acquisition of clean energy data, thermal power equipment data and purchased energy data of the supply side and planned load data and production process data of the demand side by the energy-carbon management system; prediction of a supply carbon emission propagation network according to the clean energy data, the thermal power equipment data and the purchased energy data by using a preset first carbon emission propagation model; obtaining of a demand carbon emission propagation network according to the planned load data and the production process data of the demand side by using a preset second carbon emission propagation prediction model; calculation of carbon emission factors according to the clean energy data, the thermal power equipment data and the purchased energy data, respectively; The preliminary regulation instruction includes a device execution instruction on the supply side and a load regulation instruction on the demand side; The simulation result is obtained by combining the device execution instruction and the load regulation instruction in the digital twin model, and a carbon deviation result is obtained by performing carbon deviation checking according to the simulation result; The carbon deviation result and a preset carbon integration mechanism are used to adjust the device execution instruction and the load regulation instruction of the target park to obtain the carbon collaborative regulation instruction.

[0007] In a possible implementation manner of the first aspect, the method further includes: In a case where the power generation of the clean energy is greater than a first preset threshold and the real-time power consumption data is less than a second preset threshold, a power generation fuel type of the thermal power energy is obtained; The unit calorific value carbon content corresponding to the power generation fuel type and the fuel low-heat value are determined in a preset database; The fuel input quantity and the electric energy output quantity of each power generation device are obtained, and the average power generation efficiency of each power generation device is calculated in combination with the product of the fuel input quantity and the fuel low-heat value and the electric energy output quantity; The average power generation efficiency and the unit calorific value carbon content are used to calculate the carbon emission value per degree of electricity; The power generation of the clean energy is multiplied by the carbon emission value to determine the equivalent carbon emission reduction quantity of the target park, and the equivalent carbon emission reduction quantity is used to determine the carbon emission reduction quantity reduced by the clean energy instead of the thermal power energy.

[0008] In a possible implementation manner of the first aspect, the supply carbon emission propagation network is predicted by using a preset first carbon emission propagation model according to the clean energy data, the thermal power device data and the purchased energy data, and includes the following steps: The clean energy data, the thermal power device data and the purchased energy data are integrated to construct a park entity network graph, and the park entity network graph is a network graph on the supply side; For any node of the park entity network graph, an instantaneous carbon emission intensity is calculated; The carbon emission event state of each node is determined according to the instantaneous carbon emission intensity; The carbon emission path is simulated by using the preset first carbon emission propagation model in combination with the carbon emission event state of each node and the park entity network graph; The carbon emission propagation rate, the carbon emission recovery rate and the carbon emission propagation time lag between each node are calculated according to the carbon emission path; The propagation probability matrix, the earliest carbon propagation arrival time and the node risk heat value are obtained by using a Monte Carlo sampling algorithm in combination with the carbon emission propagation rate, the carbon emission recovery rate and the carbon emission propagation time lag; The supply carbon emission propagation network is obtained according to the propagation probability matrix, the earliest carbon emission propagation time, the node risk heat value and the park entity network graph.

[0009] In a possible implementation manner of the first aspect, the node of the park entity network graph comprises a thermal power plant node, and the calculation of the instantaneous carbon emission intensity of any node of the park entity network graph comprises the following steps. In the case that the node is the thermal power plant node, the basic operation data of each thermal power plant node is obtained, and the basic operation data comprises fuel consumption, unit carbon emission factor, actual power generation and actual heat supply; The electric side allocation coefficient and the heat side allocation coefficient are calculated according to the basic operation data; The total carbon emission of the thermal power plant is calculated by multiplying the fuel consumption and the unit carbon emission factor; The electric side carbon emission and the heat side carbon emission are obtained by multiplying the total carbon emission of the thermal power plant with the electric side allocation coefficient and the heat side allocation coefficient respectively; The electric side carbon emission intensity and the heat side carbon emission intensity are calculated by using the ratio between the electric side carbon emission and the actual power generation and the ratio between the heat side carbon emission and the actual heat supply, and the electric side carbon emission intensity and the heat side carbon emission intensity are used to represent the instantaneous carbon emission intensity.

[0010] In a possible implementation manner of the first aspect, the determination of the carbon emission event state of each node according to the instantaneous carbon emission intensity comprises the following steps. The carbon emission residual value in a preset time period is calculated by combining the instantaneous carbon emission intensity and a preset intensity reference value, and the carbon emission climbing rate is calculated according to the carbon emission residual value in the preset time period; The node with the carbon emission residual value greater than or equal to a preset residual threshold value and the carbon emission climbing rate greater than or equal to a preset climbing rate threshold value is taken as a carbon emission event starting point node, and the event occurrence position of each carbon emission event starting point node is determined; The node edge weight value between each node is calculated according to the event occurrence position, and the propagation path set is determined according to the node edge weight value; The duration of the carbon emission event state of each node in each propagation path set is determined; The high carbon emission source is screened by combining the duration and the propagation path set, and the high carbon emission source is used to represent the carbon emission event state.

[0011] In a possible implementation manner of the first aspect, the generation of the preliminary control instruction according to the carbon emission factor, the supply carbon emission propagation network and the demand carbon emission propagation network comprises the following steps. The fuel type and the average power generation efficiency of the thermal power plant and the marginal carbon emission factor of the purchased energy are obtained by the carbon management system. 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.

[0012] 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: 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.

[0013] 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: 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. According to the load demand of the park and the load regulation instruction, a net regulation gap on the demand side is identified; The load regulation instruction is adjusted by the net regulation gap to obtain an energy-carbon collaborative regulation instruction.

[0014] In a second aspect, the present application provides an energy-carbon integrated management system based on digital twinning, comprising: a memory configured to store instructions; and a processor configured to call the instructions from the memory and enable the above-mentioned energy-carbon integrated management method based on digital twinning when executing the instructions.

[0015] In a third aspect, the present application provides a machine-readable storage medium having instructions stored thereon, the instructions being used to cause a machine to execute the above-mentioned energy-carbon integrated management method based on digital twinning.

[0016] Through the above technical solution, by establishing a supply carbon emission propagation network and a demand carbon emission propagation network, bidirectional coupling between carbon sources and carbon sinks is realized, which can effectively reduce the output of high-carbon emission energy equipment, and can also guide the load side to reasonably cut peak and off-peak, achieving lower carbon and more reasonable overall energy scheduling. The preliminary regulation instruction is generated based on the carbon emission factor and the network structure centrality weighting, which can make the regulation not only depend on power demand, but also take into account the externality factors and propagation risks of carbon emissions. Combined with digital twinning simulation and carbon deviation feedback, the problem of disconnection between prediction and execution in traditional strategies can be effectively solved, and the intelligence and accuracy of regulation decision-making can be enhanced. According to the real-time device operating condition, load plan and carbon propagation situation, the regulation instruction is automatically generated, and the carbon deviation is compensated combined with the simulation result, which can improve the dynamic response capability of carbon emission reduction measures. According to the carbon deviation result and the preset carbon credit mechanism, the energy-carbon collaborative regulation instruction is obtained by adjusting the device execution instruction and the load regulation instruction of the target park, which can punish high-carbon equipment and guide the behavior of equipment and users to change to low-carbon optimal strategy, effectively reducing carbon emissions.

[0017] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific embodiments part. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of an energy-carbon integrated management method based on digital twinning provided by the embodiments of the present application is shown; Figure 2 A structure diagram of an energy-carbon integrated management method based on digital twinning provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0019] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the specific implementation described herein is only used to explain and illustrate the embodiments of the present application, and is not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0020] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship, motion condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications will also change accordingly.

[0021] In addition, if the embodiments of the present application involve descriptions such as "first", "second", etc., the descriptions of "first", "second", etc. are only for description purposes, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application.

[0022] Figure 1 A flowchart of a carbon-energy integrated management method based on digital twinning according to an embodiment of the present application is schematically shown. As shown in Figure 1 The embodiments of the present application provide a carbon-energy integrated management method based on digital twinning, applied to a carbon-energy management system, the carbon-energy management system including a supply side and a demand side, the supply side including clean energy, thermal power energy and purchased energy, the method can include the following steps.

[0023] S110, acquiring clean energy data, thermal power equipment data and purchased energy data of the supply side and planned load data and production process data of the demand side in real time through the carbon-energy management system; S120, predicting a supply carbon emission propagation network according to the clean energy data, the thermal power equipment data and the purchased energy data by using a preset first carbon emission propagation model; S130, obtaining a demand carbon emission propagation network according to the planned load data and the production process data of the demand side by using a preset second carbon emission propagation prediction model; S140, calculate carbon emission factors according to clean energy data, thermal power plant data and purchased energy data respectively; S150, generate preliminary control instructions according to carbon emission factors, supply carbon emission propagation network and demand carbon emission propagation network, the preliminary control instructions including device execution instructions on the supply side and load control instructions on the demand side; S160, simulate in the digital twin model combining the device execution instructions and the load control instructions to obtain simulation results, and perform carbon deviation checking according to the simulation results to obtain carbon deviation results; S170, adjust the device execution instructions and the load control instructions of the target park according to the carbon deviation results and a preset carbon credit mechanism to obtain energy-carbon collaborative control instructions.

[0024] The clean energy data, thermal power plant data and purchased energy data on the supply side and the planned load data and production process data on the demand side are obtained in real time by the energy-carbon management system. The energy-carbon management system is a management system for real-time collection of key data on the supply side and the demand side. The clean energy data includes relevant data of clean energy such as solar energy, wind energy, water energy and biomass energy. The thermal power plant data is obtained for cogeneration equipment, including fuel consumption, power generation power, heat supply power, equipment operation parameters and other data. The cogeneration equipment can be a coal-fired cogeneration unit or a gas-fired cogeneration unit. The purchased energy data is obtained when the park purchases energy from the outside. The purchased energy can be purchased power from the power grid or purchased natural gas. In this embodiment, the supply side is the power generation side, such as a power plant, and the demand side is the power consumption side, such as a factory in an enterprise park. The planned load data is the planned data of the energy load demand of the park or enterprise in a future period of time. For industrial enterprises and other demand side users, the production process data reflects the energy consumption characteristics and carbon emission correlation in the production process.

[0025] According to the clean energy data, the thermal power plant data and the purchased energy data, a supply carbon emission propagation network is predicted by using a preset first carbon emission propagation model. In this embodiment, the preset first carbon emission propagation model is a model established in advance to simulate the propagation mode of carbon emission on the supply side. The first carbon emission propagation model is constructed based on a long short-term memory network (LSTM). The energy data has a time series characteristic, and the LSTM can better process the long-term dependence relationship in the time series. The clean energy power generation data of the enterprise or the region in the past years is collected. These data can be obtained from the monitoring system of the clean energy power station, including real-time data such as daily irradiance and power generation of a solar power station, wind speed and power generation of a wind farm. The carbon emission monitoring data can be obtained from the carbon emission monitoring equipment installed in the enterprise or the region. For example, a carbon dioxide concentration monitor is installed in the chimney of a thermal power plant. The actual carbon emission amount is calculated in combination with the fuel consumption and the equipment operation parameters, and the data is preprocessed. Next, the long short-term memory network (LSTM) model is trained by using the clean energy power generation data and the carbon emission monitoring data in the past years. During the training process, the preprocessed energy data (such as the clean energy, thermal power plant and purchased energy data after time window division) is taken as the input x t to the LSTM network. The LSTM network processes the input data through the above-mentioned gating mechanism, learns the mapping relationship between the energy data and the carbon emission propagation parameters (such as the carbon emission amount and the propagation path weight), and then defines a loss function such as a mean square error (MSE) loss function. The gradient of the loss function with respect to the network weight is calculated by using a back propagation algorithm such as a time-based back propagation through time (BPTT), and an optimization algorithm such as an Adam optimization algorithm is used to gradually reduce the loss function and gradually approach the actual value. The preset first carbon emission propagation model is obtained through the above-mentioned steps. The supply carbon emission propagation network is a network represented in the form of a graph. The nodes can be power stations and other supply side related facilities, and the edges represent the propagation paths of energy and carbon. Each node has relevant node attributes such as the node energy production or conversion amount, the carbon emission amount and the like. Through the supply carbon emission propagation network, the source of the supply side carbon emission, the propagation path and the carbon emission influence on different nodes can be directly observed. The system first collects the data of the clean energy, the thermal power plant and the purchased energy, and then inputs these data into a preset carbon emission propagation model for simulation and prediction, so as to obtain the supply side carbon emission propagation network.

[0026] According to the planned load data of the demand side and the production process data, a demand carbon emission propagation network is obtained by using a preset second carbon emission propagation prediction model. In this embodiment, the preset second carbon emission propagation prediction model is a pre-constructed model for simulating and predicting the carbon emission propagation caused by energy in the demand side. The demand carbon emission propagation network is presented in the form of a graph, in which the nodes can be different demand side users, and the edges represent the propagation relationship of energy and carbon in the demand side. Specifically, first, the energy consumption of the demand side is determined according to the planned load data, and then the direct carbon emission of the demand side is calculated by combining the carbon emission factor of each energy. For example, the planned load data of a building is 10000 degrees, the grid power carbon emission factor is 0.6 kg of carbon dioxide per degree, and the direct carbon emission of the building for one day is 10000*0.6=6000 kg of carbon dioxide. Next, the preset second carbon emission propagation prediction model is used to analyze the flow and conversion of energy and carbon in the production process by combining the production process data. For example, in an automobile manufacturing enterprise, from raw material processing to assembly, to painting and other links, the second carbon emission propagation prediction model calculates the carbon emission of each process according to the energy consumption and carbon emission factor of each process, and simulates how the carbon emission propagates among different workshops and equipment along with the production process, thereby obtaining the demand carbon emission propagation network. In this embodiment, the demand carbon emission propagation network is obtained by using the preset second carbon emission propagation prediction model according to the planned load data of the demand side and the production process data, which specifically includes the following steps: S1. Obtain the planned load data of the demand side, which includes the target energy consumption level of each production process link in a preset time period; S2. Obtain the production process data of the demand side, which includes the energy consumption relationship of the production line process link and the equipment, and the coupling path between energy and materials among each production process.

[0027] S3. Based on the planned load data and the production process data, an energy and material flow network of the demand side is established, and the load intensity of each production process is labeled in the energy and material flow network; S4. When the planned load exceeds a preset threshold, adjust the energy consumption distribution of part of the process links according to the preset flexible adjustment rule of the production process to obtain an adjustable load scheme; S5. Input the adjusted production process data and the 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; S6. Construct a demand carbon emission propagation network according to the carbon emission intensity and the carbon emission transmission relationship, which is used to represent the carbon emission influence path and propagation law among the nodes of the demand side.

[0028] Firstly, each process step of the production process is a unit or part of the demand side that consumes energy. For an industrial enterprise, the energy-consuming unit can be different production lines, workshops, etc., or large equipment, etc. The preset time period is a pre-defined time range for unified measurement of 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 these planned load data is to understand the future energy consumption plan of the demand side, and to provide basic data for subsequent energy management and carbon emission analysis.

[0029] Based on the planned load data and production process data, an energy material flow network of the demand side is established, and the load intensity of each production process is marked in the energy material flow network. The planned load data refers to the energy that each process or energy-consuming unit is expected to consume within a certain period of time. The production process data refers to the sequential relationship between different process steps and the input-output relationship. The nodes represent each step or device of the production process, such as "heating furnace", "reaction kettle", "packaging machine". The edges represent the material transfer or energy coupling relationship between the steps, such as steam transmission from the boiler to the reaction kettle, or intermediate product flow from the heating step to the reaction step. In this way, a network diagram is obtained, which can not only reflect the upstream and downstream relationship between the production steps, but also reflect the flow of energy / material between different steps. The load intensity represents the energy consumption of a certain process node per unit time, and each node is attached with an energy consumption level, so that it can be directly seen which step has a larger energy demand and which step is the key point of energy consumption.

[0030] When the planned load exceeds the preset threshold, the energy consumption distribution of part of the process steps is adjusted according to the preset flexible adjustment rule of the production process to obtain an adjustable load scheme. The planned load refers to the total energy consumption of the park or factory in a certain future time period. The preset threshold represents the maximum acceptable level set by the system, such as the upper limit of the power grid capacity, the energy consumption budget, or the carbon emission target. When the predicted energy consumption demand exceeds this threshold, measures need to be taken to avoid excessive energy consumption or carbon emissions. Flexible adjustment refers to adjusting the energy consumption of part of the process steps without affecting the overall production target or allowing a small amount of adjustment. In this embodiment, the preset rules can include peak shifting, peak cutting, substitution, and peak shifting operation. Peak shifting refers to adjusting energy-consuming processes to run during off-peak hours or low-carbon emission periods. Peak cutting refers to temporarily reducing the production load of some non-critical processes. Substitution refers to replacing high-carbon energy with low-carbon energy. Peak shifting operation refers to delaying or advancing the operation of part of the process to avoid concentrated energy consumption during peak hours. After flexible adjustment, a new load distribution scheme is formed. In this scheme, the energy consumption of each process step is redistributed, for example, the energy consumption of part of the process is reduced or delayed, the utilization rate of part of the clean energy equipment is improved, and the overall load curve is smoothed to avoid exceeding the threshold.

[0031] The adjusted production process data and the planned load data are input into a preset second carbon emission propagation prediction model to predict the carbon emission intensity and the carbon emission transmission relationship of each production process; the adjusted production process data refer to new process data after flexible adjustment, including the running order of each process, energy consumption allocation, load reduction or transfer condition; the planned load data represent the energy consumption level of each process node in a future period, serving as an input condition of 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 carbon emission intensity and the carbon emission transmission relationship of each production process are output by the second carbon emission propagation prediction model, and the carbon emission intensity refers to the carbon emission level corresponding to unit output or unit energy consumption, such as the 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 the carbon emission of process B will increase due to the energy consumption increase of process A or how much the carbon intensity change of a process will be transmitted to the downstream process in terms of proportion and time delay.

[0032] A demand carbon emission propagation network is constructed according to the carbon emission intensity and the carbon emission transmission relationship, and the demand carbon emission propagation network is used to represent the carbon emission influence path and the propagation law between nodes in the demand side. The nodes can be energy-using units in the demand side, process links in the production process, equipment, etc.; the carbon emission influence path is constructed based on the carbon emission transmission relationship; that is, the carbon emission intensity and the carbon emission transmission relationship are used to construct a demand carbon emission propagation network, that is, a network topology graph with quantitative information. The propagation law refers to how carbon emissions propagate and diffuse along the energy flow and material flow in the demand carbon emission propagation network. For example, it is found that carbon emissions often propagate from upstream process links to downstream process links along the material processing sequence, or the process link corresponding to a high-energy-consumption device has high carbon emission intensity and great influence on other links, etc.

[0033] Carbon emission factors are calculated according to clean energy data, thermal power equipment data and purchased energy data, which include power emission factors and heat emission factors in this embodiment. Specifically, the clean energy data, the thermal power equipment data and the purchased energy data are three types of input data sources (classified according to energy supply sources) for calculating the carbon emission factors, and the power emission factors and the heat emission factors are output results classified according to energy service types (electricity / heat), that is, the carbon emission factors are calculated according to the clean energy data, the thermal power equipment data and the purchased energy data (the three types of data are used as input parameters of different sources). In this embodiment, the carbon emission factors are classified into power emission factors and heat emission factors according to energy service types. The power emission factor refers to the carbon dioxide emission amount corresponding to unit electricity consumption, and the heat emission factor refers to the carbon dioxide emission amount corresponding to unit heat supply. The calculation formula of the power emission factor is as follows:

[0034] CHP generation carbon refers to carbon emissions caused by power generation of a combined heat and power equipment, in units of kilogram of carbon dioxide (kgCO2); purchased electricity carbon refers to carbon emissions caused by power purchased from a power grid or an external energy market, in units of kilogram of carbon dioxide (kgCO2); electric load emission refers to carbon emissions of actual power consumption of a park or a node, in units of kilogram of carbon dioxide (kgCO2); and electric boiler power consumption emission refers to carbon emissions of power consumption in a process in which an electric boiler converts power into heat, in units of kilogram of carbon dioxide (kgCO2).

[0035] A calculation formula of the thermal emission factor is as follows:

[0036] CHP heat supply carbon refers to carbon emissions caused by heat supply of a combined heat and power equipment, which can be carbon caused by combustion of fuels such as gas and coal; electric boiler power consumption carbon refers to carbon emissions of power consumed by an electric boiler for heat supply; and heat load emission refers to carbon emissions caused by total heat supply demand of a system in a time period.

[0037] A preliminary control instruction is generated according to a carbon emission factor, a supply carbon emission propagation network and a demand carbon emission propagation network, and the preliminary control instruction includes a device execution instruction of a supply side and a load control instruction of a demand side. In this embodiment, the carbon emission factor refers to carbon emissions corresponding to unit energy or unit output; the supply carbon emission propagation network is a network diagram describing how carbon emissions propagate from a supply end to a demand side; the demand carbon emission propagation network is a network diagram describing how carbon emissions of a demand side propagate; the device execution instruction is an operation instruction issued to various devices of the energy supply end; and the load control instruction is a control instruction issued to various users of the energy demand end. That is, based on the carbon emission factor and the supply carbon emission propagation network, a key carbon emission source and a propagation path of the supply side are identified. For a thermal power equipment with a high carbon emission factor, a device execution instruction is formulated in combination with a position of the thermal power equipment in the propagation network, for example, a combined heat and power unit with high carbon emissions is required to reduce power generation during a load valley period to reduce carbon emissions; for a thermal power equipment with multiple operating modes, the thermal power equipment is instructed to preferentially use low-carbon fuels. The demand side load control instruction is also based on the carbon emission factor and the demand carbon emission propagation network, and carbon emission conditions of the demand side users are analyzed. For a high-load user in a high power emission factor period, a load control instruction is formulated, for example, a transferable load is guided to be transferred from a high power emission factor period to a low power emission factor period; in a period in which power supply is tight and the power emission factor is high, a reducible load is instructed to be reduced.

[0038] The simulation result is obtained by simulating the device execution instruction and the load regulation instruction in the digital twin model, and the carbon deviation result is obtained by performing carbon deviation checking according to the simulation result. The device execution instruction on the supply side and the load regulation instruction on the demand side are input into the digital twin model. The digital twin model runs the simulation algorithm according to the input instructions, first, the device execution instruction and the load regulation instruction are converted into a standardized format recognizable by the digital twin model through an ETL tool (such as ApacheNiFi). Next, load the basic data and model parameters, set the simulation scene boundary, and ensure that the model is consistent with the physical system state. Basic data loading includes device parameters and real-time state data of the physical system; simulation scene parameter configuration includes setting according to instruction time window, defining simulation range (such as only including thermal power system + photovoltaic + chemical plant), constraint conditions (such as boiler minimum load ≥ 30% rated value) and the like. Then, check the parameter integrity of the physical sub-model (such as boiler heat transfer model, photovoltaic output model), chemical sub-model (such as carbon generation model), and control logic sub-model (such as PID control algorithm). Next, based on the standardized instruction driven model, the dynamic simulation results of energy flow and carbon flow are output. The control logic sub-model receives the device action sequence generated by the standardized instruction and the physical sub-model response action sequence, and calculates the carbon emissions based on the energy data of the physical sub-model using the chemical sub-model, and generates multi-dimensional simulation results through the above sub-models to provide data support for carbon deviation checking. For example, after simulating the power reduction of a combined heat and power unit, the reduction of fuel consumption, the reduction of carbon emissions, and the influence on the power supply of the power grid, after simulating the load transfer of an industrial enterprise, the reduction of power load during the daytime peak period, the increase of power load during the night low valley period, and the influence on the load curve of the power grid. The simulation result can include carbon emission data, energy production and consumption data on the supply side and demand side. Next, according to the simulation result, the carbon deviation result is obtained by performing carbon deviation checking, and the carbon deviation checking is to compare the carbon emission result obtained by the digital twin model simulation with the expected carbon emission target (or benchmark value) to calculate the deviation value. That is, the benchmark carbon emission value is subtracted from the simulation result to calculate the deviation, and the carbon deviation result is obtained.

[0039] The equipment execution instruction and the load regulation instruction of the target park are adjusted according to the carbon deviation result and a preset carbon integration mechanism to obtain the energy-carbon collaborative regulation instruction. The preset carbon integration mechanism is an incentive and restraint mechanism for measuring the carbon emission reduction or emission increase behavior of each subject in the energy and carbon system, and giving corresponding points. The initial allocation rule of carbon points can refer to the step allocation logic of EU-ETS, and the initial points of the clean energy subject are allocated according to the subject energy type based on the general rules for greenhouse gas emission accounting of industrial enterprises. The initial points of the clean energy subject = installed capacity × industry benchmark power generation × clean coefficient, wherein the clean coefficient is taken from the power carbon footprint factor in 2023. The initial points of the thermal power equipment subject (coal-fired boiler, gas-fired boiler) = design annual energy consumption × unit energy consumption benchmark emission × emission reduction coefficient (0.85, referring to the average efficiency of the boiler). The initial points of the external purchase energy subject = external purchase quantity × regional power grid emission factor (such as 0.58 tCO2 / MWh). The industry baseline is the average value of the annual benchmark value (such as the emission intensity of the top 20% enterprises in the region) and the historical best value of the enterprise, and the calculation formula is benchmark emission = actual energy consumption × benchmark emission factor (such as natural gas 0.1886 tCO2 / GJ). The point increase and decrease rule is emission reduction reward, when the actual emission < benchmark emission, the new points = (benchmark emission - actual emission) × incentive coefficient (1.2 times). For example, if the actual power generation of a certain photovoltaic power station exceeds the design value by 10%, it will obtain additional 130.8 × 10% × 1.2 = 15.7 points. The over-emission deduction is when the actual emission > benchmark emission, the deduction points = (actual emission - benchmark emission) × penalty coefficient (1.5 times), and the pre-warning is triggered. If the thermal power enterprise over-emits by 5%, it will deduct 166600 × 5% × 1.5 = 12495 points. When the carbon deviation is positive, it means that the target has not been reached, and the thermal power equipment needs to further reduce its power generation upper limit due to the deduction of points for not completing the power reduction instruction. When the demand side users need to deduct points for not completing the load regulation instruction, the regulation intensity on them can be increased, such as more stringent load transfer requirements or economic incentives to improve their response enthusiasm. If the carbon deviation is negative, it means that the target has been exceeded and the subject carbon points are sufficient, and the power generation upper limit of the clean energy equipment can be appropriately increased to improve energy utilization efficiency, or the regulation requirements on certain loads can be relaxed to reduce the impact on user production and life.

[0040] Figure 2 A structure schematic diagram of an energy-carbon integrated management method based on digital twinning provided for an embodiment of the present application, as shown in Figure 2 The demand side includes thermal power energy, clean energy and external purchase energy. The thermal power energy, clean energy and external purchase energy provide electric load and thermal load to the demand side, and then generate demand side carbon emission in the demand side.

[0041] By establishing supply carbon emission propagation network and demand carbon emission propagation network, the two-way coupling between carbon source and carbon sink is realized, which can effectively reduce the output of high-carbon emission energy equipment, and can also guide the load side to reasonably cut peak and valley, and realize lower carbon and more reasonable overall energy scheduling. The preliminary control instruction is generated based on the carbon emission factor and the network structure centrality weighting, which can make the control not only according to the power demand, but also take into account the external factors and propagation risk of carbon emission. Combined with digital twin simulation and carbon deviation feedback, the problem of disconnection between prediction and execution in traditional strategy can be effectively solved, and the intelligence and accuracy of control decision can be enhanced. According to the real-time device operating condition, load plan and carbon propagation situation, the control instruction is automatically generated, and the carbon deviation compensation is carried out combined with the simulation result, which can improve the dynamic response ability of carbon emission reduction measures. According to the carbon deviation result and the preset carbon integration mechanism, the device execution instruction and the load control instruction of the target park are adjusted to obtain the carbon coordinated control instruction, which can punish the high-carbon equipment and guide the behavior of the equipment and the user to the low-carbon optimal strategy, and effectively reduce the carbon emission.

[0042] In one of the embodiments of the present embodiment, the method further comprises: S210, in the case that the power generation of clean energy is greater than a first preset threshold and the real-time electricity consumption data is less than a second preset threshold, obtaining the power generation fuel type of thermal power energy; S220, determining the unit calorific value carbon content and the low-grade fuel heat value corresponding to the power generation fuel type in the preset database; S230, obtaining the fuel input quantity and the electric energy output quantity of each power generation device, and calculating the average power generation efficiency of each power generation device by combining the product of the fuel input quantity and the low-grade fuel heat value and the electric energy output quantity; S240, calculating the average carbon emission value per kilowatt-hour according to the average power generation efficiency and the unit calorific value carbon content; S250, multiplying the power generation of clean energy by the carbon emission value to determine the equivalent carbon emission reduction amount of the target park, and the equivalent carbon emission reduction amount is used to determine the carbon emission reduction amount reduced by replacing thermal power energy with clean energy.

[0043] In the case that the power generation of clean energy is greater than a first preset threshold and the real-time electricity consumption data is less than a second preset threshold, the power generation fuel type of thermal power energy is obtained, that is, the output of clean energy such as solar energy and wind power is high, and the power demand of the current park is not high, the overall load is light, and the energy demand pressure is not high. When the energy demand pressure is not high, the power generation fuel type of thermal power energy can be obtained through the energy management system. The first preset threshold and the second preset threshold can be determined according to the actual situation.

[0044] Determine the unit heat value carbon content and the low heat value of the power generation fuel type in the preset database, the power generation fuel type including coal, oil, natural gas, biomass energy and the like; the unit heat value carbon content refers to the mass of carbon elements contained in unit heat of fuel; the low heat value of fuel refers to the heat released when unit mass of fuel is completely combusted and the water vapor in the combustion products exists in gaseous form; the preset database is an information set in which various power generation fuel related data are pre-arranged and stored. That is, the unit heat value carbon content and the low heat value of various fuel types are found out from a knowledge base or database which has been set.

[0045] Obtain the fuel input and the electric energy output of each power generation device, and calculate the average power generation efficiency of each power generation device by multiplying the fuel input and the low heat value of fuel and the electric energy output. The fuel input refers to the amount of fuel input into the power generation device for power generation in a certain time period; the electric energy output refers to the amount of electric energy produced and output to the power grid by the power generation device in a certain time period; the average power generation efficiency is the ratio of the electric energy output to the fuel energy input; the calculation formula of the average power generation efficiency is as follows:

[0046] Calculate the average carbon emission value per degree of electricity according to the average power generation efficiency and the unit heat value carbon content. The unit heat value carbon content refers to the mass of carbon elements contained in unit heat of fuel. The average carbon emission value per degree of electricity refers to the amount of greenhouse gases such as carbon dioxide generated per degree of electricity. The fuel input is converted into energy input by the low heat value of fuel. The total carbon content of fuel is calculated according to the unit heat value carbon content, and the total CO2 emission is calculated according to the stoichiometric relationship of carbon conversion into CO2. Finally, the average carbon emission value per degree of electricity is obtained by dividing the total CO2 emission by the total power generation. The specific calculation formula is as follows:

[0047] Wherein, EF represents the average carbon emission value per degree of electricity; c is the unit heat value carbon content; η is the average power generation efficiency. 44 / 12 is the stoichiometric coefficient of carbon conversion into carbon dioxide; 1000 represents the conversion from MJ to GJ of the unit of fuel energy; 3.6 represents the conversion from kW h to heat unit MJ.

[0048] Next, the power generation capacity of clean energy is multiplied by the carbon emission value to determine the equivalent carbon emission reduction amount of the target park, and the equivalent carbon emission reduction amount is used to determine the carbon emission reduction amount reduced by clean energy instead of thermal power energy. The equivalent carbon emission reduction amount is obtained by multiplying the power generation capacity of clean energy by the carbon emission value, and the formula is: Equivalent carbon emission reduction = Clean energy power generation × Carbon emission value This indicates the reduction in carbon emissions equivalent to those generated by using clean energy instead of thermoelectric power generation.

[0049] 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.

[0050] 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: 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. S320. For any node in the entity network graph of the park, calculate the instantaneous carbon emission intensity. S330. Determine the carbon emission event status of each node based on the instantaneous carbon emission intensity; 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. 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. 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. 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.

[0051] The clean energy data, the thermal power equipment data and the purchased energy data are integrated to construct a park entity network graph, and the park entity network graph is a network graph on the supply side. In the embodiment, the park entity network graph is a data structure based on graph theory, which is used to represent entities and the relationship therebetween, and displays various links and elements of energy production, transmission and consumption in the park in a graphical manner. That is, the clean energy data, the thermal power equipment data and the purchased energy data are integrated, including time dimension unification, unit unification and space structure unification. The unit unification refers to converting the consumption of coal, gas and oil into a standard energy unit such as MJ or kWh. Through the above integration, a digital graph of the supply side energy network is established, including nodes, edges and attributes. The nodes represent various supply devices such as photovoltaic units, wind farms, boilers, gas units and purchased electricity interfaces. The edges represent the energy transmission relationship such as power transmission lines and heat pipelines. The attributes represent the operating parameters carried by each node and edge such as power generation, carbon emission factor, efficiency and fuel type.

[0052] For any node of the park entity network graph, the instantaneous carbon emission intensity is calculated. The instantaneous carbon emission intensity represents the carbon emission corresponding to the unit output energy of the node at a certain moment. The nodes of the park entity network graph can include thermal power equipment nodes and purchased energy nodes. For the thermal power equipment nodes, the fuel consumption, fuel type, low calorific value of fuel, carbon content per unit calorific value, actual power generation and actual heat supply are collected. For the purchased energy nodes, the purchased electricity or heat and the marginal carbon emission factor of external energy are collected. The carbon emission of the purchased energy node can be calculated by the product of the purchased energy and the marginal carbon emission factor of the external energy. The carbon emission of the thermal power equipment node can be calculated by the product of the fuel consumption, the low calorific value of fuel and the carbon content per unit calorific value. Then, the carbon emission of the node is divided by the energy output to obtain the instantaneous carbon emission intensity.

[0053] The carbon emission event state of each node is determined according to the instantaneous carbon emission intensity, which refers to the carbon emission amount generated by a unit of energy output of a certain node at a certain moment, and reflects the carbon emission activity of the node at a certain time point. The carbon emission event state can include the occurrence position, propagation path, propagation speed and influence range of the carbon emission event, and through the carbon emission event state, it can be determined whether the node is currently in an abnormal emission, high emission or normal emission state. First, it can be determined whether the current node is in a high emission, abnormal emission or normal emission state by comparing the instantaneous carbon emission intensity with a preset carbon emission intensity benchmark value. Then, the carbon emission event state is further determined, which can be achieved by using carbon emission residual and carbon emission climbing rate. The carbon emission residual can be obtained by subtracting the preset carbon emission intensity benchmark value from the current intensity; and the carbon emission climbing rate refers to the growth rate of the carbon emission intensity in a unit of time. By judging whether the residual threshold is exceeded and whether the intensity rapidly rises, it can be determined whether to mark the node as a carbon emission event starting node.

[0054] The carbon emission path is simulated by combining the carbon emission event state of each node and the park entity network graph using a preset first carbon emission propagation model. In this embodiment, the preset first carbon emission propagation model is a pre-trained model used to simulate the process of carbon emission. The carbon emission event state of each node is input into the preset carbon emission propagation model, while the connection relationship and attributes of the nodes in the park entity network graph are combined. For direct emission nodes, the preset first carbon emission propagation model calculates the CO2 generation amount of the node according to its state, and distributes the carbon emission to other related nodes along the energy transmission path in the graph. For purchased energy nodes, the preset first carbon emission propagation model simulates the carbon emission caused by the input amount according to its carbon emission intensity, and distributes the carbon emission to the power-consuming equipment nodes through the power transmission path in the graph. Finally, the simulated carbon emission path is output to show the flow process of CO2 and other greenhouse gases in the park energy network from the generation node to other nodes, including the carbon emission amount, propagation direction and other information on each path.

[0055] The carbon emission propagation rate, carbon emission recovery rate and carbon emission propagation time lag between each node are calculated according to the carbon emission path. The carbon emission propagation rate represents the ability or possibility of carbon emission from one node to another node; the carbon emission recovery rate represents the ability of a node to self-dissolve or neutralize the influence after being affected by carbon emission; and the carbon emission propagation time lag refers to the time delay required for carbon emission influence to be transmitted from one node to another node. The calculation formula of the carbon emission propagation rate is as follows:

[0056] Wherein, Tij represents the propagation rate from node i to node j; Ni represents all adjacent nodes of node i; Wij represents the connection edge weight between nodes; The sum of all adjacent edge weights of node i is represented by Wik, and since Wik is dimensionless, the sum is also dimensionless (dimension 1).

[0057] The calculation formula of carbon emission recovery rate is as follows:

[0058] R represents the proportion of clean energy; F represents the flexible load regulation capacity of the node; C represents the carbon capture efficiency; the coefficients α+β+γ=1, weighted according to the scene; The proportion of clean energy represents the proportion of clean energy used by the node in its total energy consumption, which can be obtained from the energy carbon management system. The flexible load regulation capacity of the node is described as whether the node has the ability to adjust the load, such as adjustable electrical load, peak-shifting electricity capacity, interruptible load, etc. High flexibility means that the node can quickly respond to control instructions when carbon emissions are abnormal. It can be extracted from the running data of equipment or energy-using units, combined with production process, energy-using mode, etc. information to evaluate. Carbon capture efficiency can be obtained by deploying carbon capture devices in the node, which represents the carbon capture efficiency of the capture system.

[0059] The calculation formula of carbon emission propagation time lag is as follows:

[0060] Wherein, D ij represents the physical distance between nodes; V represents the energy / heat transmission speed; represents the control system response time. The control system response time represents the response time in the energy scheduling and carbon control system, for example, a park thermal power equipment receives a load regulation, if the response time is 2 seconds, it means that the device can complete the state adjustment within 2 seconds.

[0061] The propagation probability matrix, the earliest carbon propagation arrival time and the node risk heat value are obtained by using the Monte Carlo sampling algorithm combined with the carbon emission propagation rate, the carbon emission recovery rate and the carbon emission propagation time lag. The Monte Carlo sampling algorithm is a calculation method based on probability statistics, which simulates and solves problems in mathematics, physics, engineering and other fields through a large number of random sampling. The propagation probability matrix is a matrix, and the elements in the matrix represent the probability of carbon emission from one node to another node in the park entity network graph. The earliest carbon propagation arrival time refers to the shortest time for carbon emission to propagate from the source node to the target node in the park entity network graph. The node risk heat value is an index for comprehensively evaluating the risk degree of carbon emission of nodes in the park entity network graph. That is, the carbon emission path is simulated multiple times, and the carbon emission propagation rate, the carbon emission recovery rate and the carbon emission propagation time lag are calculated each time, and then the Monte Carlo sampling algorithm is used for random sampling to construct the propagation probability matrix, the earliest carbon propagation arrival time and the node risk heat value. Next, multiple rounds of simulation sampling are performed. In each round, it is randomly judged whether to diffuse from one node to the next according to the propagation rate. If diffusion occurs, the time is calculated by considering the propagation time lag. If the node has a recovery rate, the propagation may be interrupted. Then, the propagation probability matrix, that is, the probability of propagation between each node, is obtained. The earliest time when each node is affected in all simulations is recorded, and the frequency of each node being affected is calculated to form the node risk heat value.

[0062] According to the propagation probability matrix, the earliest carbon propagation arrival time, the node risk heat value and the park entity network graph, a supply carbon emission propagation network is obtained. On the node graph of the park entity network graph, the node risk heat value is marked. The node information can also be edited, including the node type, historical carbon emission data, current operating state and connection relationship list with other nodes, etc. Next, the propagation probability is displayed. The edges representing the connection relationship of nodes in the park entity network graph are visualized according to the values in the propagation probability matrix. For each edge, the earliest carbon propagation arrival time is marked. Time tags can be used, such as the earliest arrival time of 3 minutes, and the font size is appropriately adjusted according to the time length. The layout of the nodes and edges of the supply carbon emission propagation network can also be optimized according to the actual geographical layout or energy logical relationship of the park, so that it is more in line with the cognitive habit. For example, the thermal power equipment nodes are concentrated on one side, the electric equipment nodes are distributed according to the region on the other side, and the power transmission line edges are clearly connected, so as to obtain the supply carbon emission propagation network.

[0063] By forming the supply carbon emission propagation network, the accuracy and timeliness of carbon emission perception can be improved, the traceable carbon emission propagation path and intensity can be improved, the clean energy carbon reduction effect can be quantified, and strong decision support can be provided for park carbon emission analysis and clean energy popularization.

[0064] In one of the implementations of the embodiment, the nodes of the park entity network graph include a thermal power plant node, and the calculation of the instantaneous carbon emission intensity for any node of the park entity network graph includes the following steps: S410, in the case of a node being a thermal power plant node, obtaining basic operation data of each thermal power plant node, the basic operation data including fuel consumption, unit carbon emission factor, actual power generation, and actual heat supply; S420, calculating an electricity-side allocation coefficient and a heat-side allocation coefficient according to the basic operation data; S430, calculating total carbon emission of the thermal power plant by multiplying the fuel consumption and the unit carbon emission factor; S440, multiplying the total carbon emission of the thermal power plant by the electricity-side allocation coefficient and the heat-side allocation coefficient respectively to obtain electricity-side carbon emission and heat-side carbon emission; S450, calculating electricity-side carbon emission intensity and heat-side carbon emission intensity using the ratio between the electricity-side carbon emission and the actual power generation and the ratio between the heat-side carbon emission and the actual heat supply, the carbon emission intensity and the heat-side carbon emission intensity being used to represent the instantaneous carbon emission intensity.

[0065] In the case of a node being a thermal power plant node, basic operation data of each thermal power plant node is obtained, the basic operation data including fuel consumption, unit carbon emission factor, actual power generation, and actual heat supply; the thermal power plant node represents a node of a park for simultaneously producing electric energy and heat energy; the fuel consumption refers to the amount of fuel consumed by the thermal power plant in a certain operation period; the unit carbon emission factor represents the carbon emission corresponding to unit fuel consumption; the actual power generation refers to the amount of electric energy actually produced and delivered to the power grid or the internal power system of the park by the thermal power plant in the operation period; and the actual heat supply refers to the amount of heat provided by the device to the park users through heat supply pipelines and other facilities in the operation period.

[0066] The electricity-side allocation coefficient (KE) and the heat-side allocation coefficient (KQ) are calculated according to the basic operation data, the electricity-side allocation coefficient (KE) representing the allocation proportion of each index generated by the fuel consumption of the thermal power plant in the power generation link, and the heat-side allocation coefficient (KQ) representing the allocation proportion of each index generated by the fuel consumption of the thermal power plant in the heat supply link, and KE+KQ=1, that is, all the indexes of fuel consumption are allocated to the electricity and heat links. Specifically, the fuel consumption B, the fuel low-heat value q, and the actual power generation E are obtained through the basic operation data, and the total heat Q released by the fuel combustion is calculated by multiplying the fuel consumption and the fuel low-heat value. Therefore, the formula for calculating the electricity-side allocation coefficient (KE) is as follows:

[0067] wherein E represents actual power generation; KE represents electricity side allocation coefficient; Q represents total heat released by fuel combustion; 3.6 represents the conversion of electric energy unit kW h is converted into heat unit MJ.

[0068] The formula for calculating the heat side allocation coefficient (KQ) is as follows:

[0069] wherein KQ represents heat side allocation coefficient; E represents actual power generation; Q represents total heat released by fuel combustion; 3.6 represents the conversion of electric energy unit kW h is converted into heat unit MJ.

[0070] The total carbon emission of the thermal power equipment is calculated by multiplying the fuel consumption and the unit carbon emission factor; the fuel consumption refers to the actual fuel consumption of the equipment during operation; the unit carbon emission factor refers to the amount of CO emitted per unit of fuel combustion. The total carbon emission of the thermal power equipment is obtained by multiplying the fuel consumption and the unit carbon emission factor.

[0071] Next, the total carbon emission of the thermal power equipment is multiplied by the electricity side allocation coefficient and the heat side allocation coefficient respectively to obtain the electricity side carbon emission and the heat side carbon emission; the total carbon emission represents the total amount of carbon emission generated after the thermal power equipment burns fuel; the electricity side allocation coefficient is a proportional coefficient for allocating the total carbon emission to the power end; the heat side allocation coefficient is a proportional coefficient for allocating the total carbon emission to the heat end, and the electricity side allocation coefficient + the heat side allocation coefficient = 1. That is, the electricity side carbon emission = the total carbon emission of the thermal power equipment x the electricity side allocation coefficient, and the heat side carbon emission = the total carbon emission of the thermal power equipment x the heat side allocation coefficient. The electricity side carbon emission and the heat side carbon emission are calculated through the above two formulas.

[0072] The electricity side carbon emission intensity and the heat side carbon emission intensity are calculated by using the ratio between the electricity side carbon emission and the actual power generation, and the ratio between the heat side carbon emission and the actual heat supply. The carbon emission intensity and the heat side carbon emission intensity are used to represent the instantaneous carbon emission intensity. In this embodiment, in order to accurately measure the carbon emission efficiency of the thermal power equipment in the process of outputting electric energy and heat energy, the following steps are adopted to calculate the electricity side carbon emission intensity and the heat side carbon emission intensity respectively, and to represent the instantaneous carbon emission intensity of the thermal power equipment at a specific moment. That is, the electricity side carbon emission intensity is equal to the electricity side carbon emission divided by the actual power generation; the heat side carbon emission intensity is equal to the heat side carbon emission divided by the actual heat supply. The electricity side carbon emission intensity and the heat side carbon emission intensity are used as important indicators for describing the carbon emission efficiency of the equipment at the current moment, and are combined with the current operation mode of the equipment to quantitatively represent the instantaneous carbon emission intensity. The intensity can be used to identify potential high emission nodes, and further assist in identifying carbon emission event states and simulating propagation paths.

[0073] By calculating the instantaneous carbon emission intensity of the thermoelectric equipment nodes, the carbon emission contribution can be accurately disassembled, the carbon emission proportion of power generation and heat supply can be determined, and important reference can be provided for park energy planning; at the same time, the carbon emission intensity can be used to intuitively reflect the efficiency of the equipment, so that inefficient equipment can be found and optimized, and the carbon emission propagation network can be constructed to provide a scientific basis for carbon emission reduction measures and energy management of the park.

[0074] In one of the embodiments of the present embodiment, determining the carbon emission event state of each node according to the instantaneous carbon emission intensity comprises the following steps: S510, calculating the carbon emission residual value in the preset time period in combination with the instantaneous carbon emission intensity and the preset intensity reference value, and calculating the carbon emission climbing rate according to the carbon emission residual value in the preset time period; S520, taking the node with the carbon emission residual value greater than or equal to the preset residual threshold value and the carbon emission climbing rate greater than or equal to the preset climbing rate threshold value as the carbon emission event starting node, and determining the event occurrence position of each carbon emission event starting node; S530, calculating the node edge weight value between each node according to the event occurrence position, and determining the propagation path set according to the node edge weight value; S540, determining the duration of the carbon emission event state of each node in each propagation path set; S550, screening high carbon emission sources in combination with the duration and the propagation path set, and the high carbon emission source is used to represent the carbon emission event state.

[0075] The carbon emission residual value in the preset time period is calculated in combination with the instantaneous carbon emission intensity and the preset intensity reference value, and the carbon emission climbing rate is calculated according to the carbon emission residual value in the preset time period. The instantaneous carbon emission intensity refers to the carbon emission amount corresponding to the unit output energy of a node at a certain time, which is a real-time expression form of the carbon emission intensity. The preset intensity reference value refers to the standard carbon emission intensity reference value set by the system in advance. The carbon emission residual value represents the difference between the instantaneous carbon emission intensity and the reference value, which is used to quantify the deviation degree of the node at that moment. The carbon emission climbing rate is used to measure the growth trend of the carbon emission residual value in unit time, which reflects whether the carbon emission is in an accelerating rising state. The carbon emission climbing rate can be calculated by dividing the residual change value in the preset time period by the length of the time period.

[0076] The node with a carbon emission residual value greater than or equal to a preset residual threshold value and a carbon emission climbing rate greater than or equal to a preset climbing rate threshold value is taken as a carbon emission event starting node, and the event occurrence position of each carbon emission event starting node is determined; the carbon emission event starting node refers to a node with an abnormally high carbon emission intensity and a significant growth rate in a certain time period, which can be regarded as an emission source; the preset residual threshold value and the preset climbing rate threshold value are judgment reference values predefined by a system administrator or a model, and are used to determine whether the emission behavior constitutes an “event”; the event occurrence position can correspond to an actual device number, a system number or a GIS geographic coordinate. That is, when the carbon emission residual value of a certain node is greater than or equal to the residual threshold value preset by the system, that is, the emission level of the node is significantly higher than the normal level, and the carbon emission climbing rate is greater than or equal to the preset climbing rate threshold value, that is, the abnormal value shows a rapid upward trend, the node is identified as a starting node of a carbon emission event, indicating that the node is likely to be an initial source of a sudden carbon emission propagation behavior. Further, the system records the position information of the above starting node in the park entity network graph as the occurrence position of the carbon emission event, and takes the position information as a basic reference point for subsequent carbon propagation path simulation and control scheduling.

[0077] The node edge weight value between each node is calculated according to the event occurrence position, and the propagation path set is determined according to the node edge weight value; the event occurrence position refers to the position of the node identified as the starting node of the carbon emission event, which can be a thermal power device, an energy exchange station or the like; the node edge weight value refers to a numerical parameter used to describe the carbon emission propagation possibility or influence intensity between two nodes in the network graph; the propagation path set is a set of paths through which carbon emission can propagate in the network, which is determined by the edge weight value. For any two nodes i and j having a direct connection relationship, the edge weight w ij ,

[0078] wherein w ij represents the node edge weight value; Mij represents the energy flow intensity; Nij represents the historical carbon propagation frequency; and a1, b1 and g1 represent weight coefficients. The values of a1, b1 and g1 can be set according to actual conditions. The energy flow intensity represents the energy transmission amount through the connection between the node i and the node j per unit time; and the historical carbon propagation frequency represents the number or frequency of carbon emission propagation events that have occurred on the path from the node i to the node j in the past operation period.

[0079] Determine the duration of the carbon emission event state of each node in each propagation path set; from the carbon emission event starting point, based on the propagation probability, historical propagation record, network topology and all possible path sets inferred, each path is a sequence composed 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, in all possible routes of the carbon emission event spreading from the starting node, the length of time from the occurrence of the carbon emission anomaly to the recovery of each node affected by the anomaly on each route is calculated. Simulate the propagation process of carbon emissions in the path set with the event starting point as the source; record the time when each node enters the event affected state; calculate the duration, and take the duration as one of the risk indicators of the node in the propagation path.

[0080] Subsequently, high carbon emission sources are screened in combination with the duration and the propagation path set, and the high carbon emission sources are used to represent the carbon emission event state. The high carbon emission source refers to a key source node with strong propagation ability, long carbon emission time and high propagation rate in multiple paths; the carbon emission event state reflects whether the current system is in an abnormal carbon emission state, for example, a device produces excessive carbon emission due to efficiency decline. By analyzing the event duration of each node in the path, it is found out which nodes are in a high emission state for a long time, and the "propagation breadth" of the node is evaluated in combination with the path quantity, path length and other factors. If a node not only emits strongly but also has a wide influence range, it is more likely to be determined as a "high carbon emission source". The carbon emission event state can include high carbon emission sources, low intensity carbon emission sources and medium intensity carbon emission sources, and the high carbon emission source is considered in this embodiment.

[0081] By calculating the carbon emission residual value and the climbing rate, screening out the carbon emission event starting node, and simulating the propagation path of carbon emission, the diffusion trend of carbon emission can be effectively prevented, and prospective information can be provided for park managers. By screening out high carbon emission sources, it is helpful to help the park to accurately reduce carbon and optimize energy, to build a dynamic and intelligent carbon emission propagation analysis and prediction mechanism, and to improve the efficiency of carbon emission management.

[0082] In one of the embodiments of the present embodiment, the preliminary control instruction is generated according to the carbon emission factor, the supply carbon emission propagation network and the demand carbon emission propagation network, and the preliminary control instruction includes the device execution instruction of the supply side and the load control instruction of the demand side, including the following steps: S610, obtaining the fuel type and average power generation efficiency of the thermal power equipment and the marginal carbon emission factor of the purchased energy through the carbon management system; S620, determining the standard emission factor corresponding to the fuel type; S630, calculating the effective carbon emission factor according to the standard emission factor and the average power generation efficiency; S640, respectively determine the supply node centrality between nodes in the supply carbon emission propagation network and the demand node centrality between nodes in the demand carbon emission propagation network, and the supply node centrality and the demand node centrality are used to show the importance of the nodes; S650, for any node in the supply carbon emission propagation network and the demand carbon emission propagation network, a centrality coefficient is assigned to each node according to the supply node centrality and the demand node centrality; S660, respectively determine the effective carbon emission factor and the marginal carbon emission factor corresponding to each node, and calculate the node superposition weight corresponding to each node according to the effective carbon emission factor, the marginal carbon emission factor and the centrality coefficient assigned to each node; S670, generate a preliminary control instruction according to the node superposition weight corresponding to each node.

[0083] The fuel type and average power generation efficiency of the thermal power equipment and the marginal carbon emission factor of the purchased energy are obtained through the carbon management system. The fuel type of the thermal power equipment refers to the type of energy used by the combined heat and power or thermal power equipment, such as different fuel types such as coal, natural gas, biomass fuel, fuel oil, etc. Different fuel types correspond to different carbon emission levels. The average power generation efficiency refers to the ratio of the output of electric energy to the input of energy per unit; the marginal carbon emission factor of the purchased energy refers to the carbon emission per kilowatt-hour of power purchased from the power grid or a third party, which depends on the current energy structure of the power grid, such as high carbon factor for coal and low carbon factor for hydropower.

[0084] The standard emission factor corresponding to the fuel type is determined, and the unit heat value carbon content and the standard CO2 emission factor corresponding to the fuel type can be searched in the preset standard emission factor database; the standard emission factor refers to the carbon dioxide emission generated by unit fuel in the complete combustion process. Different types of fuel contain different carbon element density, and the amount of carbon dioxide generated after combustion is also different. Therefore, the standard emission factor is a numerical index determined according to the type, heat value, carbon content and other factors of the fuel.

[0085] The effective carbon emission factor is calculated according to the standard emission factor and the average power generation efficiency; the standard emission factor refers to the carbon dioxide emission generated by unit heat value fuel in complete combustion; the average power generation efficiency refers to the comprehensive efficiency of the power generation equipment in converting the input fuel heat value into electric energy in a certain period; the effective carbon emission factor refers to the actual carbon emission per unit of electric energy after considering the efficiency loss of the equipment, reflecting the real carbon intensity. The formula for calculating the effective carbon emission factor is as follows: Effective carbon emission factor = standard emission factor × average power generation efficiency In the process of power generation, the carbon pollutant emissions of the fuel are not directly related to the actual generated electricity, but are affected by the power generation efficiency. The average power generation efficiency determines the proportion of the fuel energy actually used for power generation and electricity generation, and the standard emission factor determines the carbon pollutant emissions corresponding to the part of the fuel actually participating in the energy conversion of power generation. Through the mathematical correlation calculation of the two, the effective carbon pollutant emission factor (i.e. effective carbon emission factor) corresponding to each unit of electricity generation can be obtained.

[0086] The supply node centrality between nodes in the supply carbon emission propagation network and the demand node centrality between nodes in the demand carbon emission propagation network are determined respectively, and the supply node centrality and the demand node centrality are used to show the importance of the nodes; specifically, topological analysis is performed on all nodes in the supply carbon emission propagation network and the demand carbon emission propagation network, and the degree centrality of each node is calculated. In this embodiment, the supply node centrality and the demand node centrality are determined based on the degree centrality, which is used to measure the importance or influence of nodes in the network. The higher the degree centrality of a node, the greater the control weight or emission radiation range it has in the carbon emission propagation process, and it is a key node in the carbon emission propagation. The node centrality is an index for measuring how important a node is in the supply carbon emission propagation network and the demand carbon emission propagation network. For example, a power plant or power equipment supplies power to many enterprise users, and the more downstream nodes connected in the supply network, the higher the importance of the node.

[0087] Next, for any node in the supply carbon emission propagation network and the demand carbon emission propagation network, a centrality coefficient is assigned to each node according to the supply node centrality and the demand node centrality. The centrality coefficient is a numerical index used in network analysis to quantify the importance or influence of a single node in the entire network; that is, after obtaining the centrality of each node in the supply carbon emission propagation network and the demand carbon emission propagation network, the centrality value of each node in the corresponding network is determined through a network analysis tool, such as the NetworkX library of Python. The higher the coefficient, the more central the node is in the network, for example, the betweenness centrality coefficient of A steel plant in the supply network is 0.8, and the betweenness centrality coefficient of B steel plant is 0.3, which indicates that A is a more critical carbon emission hub.

[0088] The effective carbon emission factor and the marginal carbon emission factor corresponding to each node are determined respectively, and the node superposition weight corresponding to each node is calculated according to the effective carbon emission factor, the marginal carbon emission factor and the centrality coefficient of each node; first, the attribute of each node is determined, and according to the attribute of the node, it is judged what the current node is, when the node is a thermal power equipment node, the node superposition weight of the node is calculated by using the effective carbon emission factor of the node and the centrality coefficient corresponding to the node; when the node is a clean energy, the node superposition weight of the node is calculated by using the marginal carbon emission factor of the node and the centrality coefficient corresponding to the node. The thermal power equipment burns fossil fuels (such as coal, natural gas, fuel oil), and the carbon emission amount is determined by the equipment efficiency and the fuel characteristics; therefore, the node superposition weight of this kind of node is mainly based on the effective carbon emission factor and the centrality coefficient of the node, and the node superposition weight of the node is equal to the effective carbon emission factor of the node multiplied by the centrality coefficient, the thermal power equipment with high carbon emission intensity and important position in the network will get higher weight, and the control priority is also higher. The carbon emission of clean energy itself is close to zero, but its scheduling needs to consider the "external substitution effect", that is, the emission reduction contribution when clean power replaces high-carbon power; therefore, the weight of this kind of node is mainly based on the marginal carbon emission factor and the centrality coefficient, and the node superposition weight of the node is equal to the marginal carbon emission factor of the node multiplied by the centrality coefficient, which represents that the clean energy node has greater control value in the period with high marginal emission intensity of the power grid. Therefore, each node will get a superposition weight, which not only considers the carbon factor, but also considers the importance of the node in the network structure. For the thermal power equipment node, because the thermal power equipment burns fossil fuels, and the carbon emission amount is determined by the equipment efficiency and the fuel characteristics, the effective carbon emission factor (related to the fuel type and power generation efficiency) and the centrality coefficient are used to calculate the node superposition weight. For the clean energy node, since the carbon emission of clean energy itself is close to zero, but its scheduling needs to consider the external substitution effect (that is, the emission reduction contribution when clean power replaces high-carbon power, which is related to the marginal carbon emission factor of the external purchased energy), so the marginal carbon emission factor and the centrality coefficient are used to calculate the node superposition weight. In addition, the node superposition weight is a quantitative index that comprehensively reflects the importance of the node in the carbon emission propagation network and its own carbon emission related characteristics. Specifically, the centrality coefficient reflects the importance of the node in the supply carbon emission propagation network or the demand carbon emission propagation network. The node with high centrality may play a more critical role in connecting or transmitting in the network, and has a greater influence on the whole carbon emission propagation. The effective carbon emission factor is related to the fuel type and power generation efficiency of the equipment (such as thermal power equipment), and reflects the carbon emission characteristics in the energy conversion process of the node. Different fuel types and power generation efficiencies will result in different effective carbon emission factors, which reflect the carbon emission level in the energy utilization process of the node. The marginal carbon emission factor is the carbon emission of the external purchased energy, which reflects the marginal carbon emission influence when additional unit energy is obtained.The node superposition weight calculated by comprehensively considering the three factors can quantify the comprehensive influence of the node, combine the structural importance of the node in the network (through the centrality coefficient) with the carbon emission characteristics of the purchased energy (through the effective carbon emission factor and the marginal carbon emission factor), and comprehensively measure the comprehensive influence degree of the node on the entire carbon emission system. When generating the preliminary control instruction, the node superposition weight provides a basis for judging which nodes need to be preferentially controlled and how much control is needed. A node with a high weight may mean that it has a great influence on the overall carbon emission, whether due to its key position in the network or its high carbon emission level or the high marginal carbon emission of the purchased energy, and more attention and more targeted measures need to be given in the control. For example, if a node is in a core position of the carbon emission propagation network (high centrality coefficient), and the fuel carbon emission factor used by the node is high (the effective carbon emission factor is large) and the marginal carbon emission factor of the purchased energy is also high, the node superposition weight of the node will be high, and the optimization of energy use, equipment modification or energy procurement strategy adjustment of the node needs to be considered in the carbon emission control to reduce the overall carbon emission.

[0089] Finally, the preliminary control instruction is generated according to the node superposition weight corresponding to each node. The larger the node superposition weight, the more significant the influence of the node on the overall carbon emission and the network operation. Therefore, the system will preferentially consider the nodes with large weights when formulating the control instruction. The preliminary control instruction is a supply-side instruction and a demand-side instruction. On the supply side, instructions to reduce the output or reduce the power generation of high-carbon emission equipment with a large weight are generated. For example, instructions to increase the output or preferentially schedule clean energy equipment with a large weight are generated. On the demand side, instructions to reduce the load or peak load shifting of high-load nodes with a large weight are generated. Instructions to maintain the status quo or low-priority control of flexible load nodes with a small weight are generated.

[0090] By generating the preliminary control instruction according to the carbon emission factor, the supply carbon emission propagation network and the demand carbon emission propagation network, the actual carbon emission level of the equipment can be more truly reflected, thereby improving the dynamic precision of carbon accounting. By assigning the node centrality coefficient, the topological structure and the operation influence are included in the weight calculation, which helps to avoid judging the importance of the node only by the single-point emission amount. The preliminary control instruction generated based on the node superposition weight can realize the differentiated adjustment of different types of nodes, preferentially control the high-weight nodes and secondarily control the low-weight nodes, thereby improving the overall carbon emission reduction efficiency of the system.

[0091] In one of the embodiments of the present embodiment, determining the supply node centrality between nodes in the supply carbon emission propagation network and the demand node centrality between nodes in the demand carbon emission propagation network respectively includes the following steps: S710, determining the number of power transmission lines between each node and another adjacent node for any one node; S720, obtaining the demand node centrality and the supply node centrality based on the total number of nodes and the number of power transmission lines using a preset centrality calculation formula.

[0092] For any one node, the number of power transmission lines between each node and another adjacent node is determined; in this embodiment, the adjacent node refers to other nodes having a direct power transmission connection relationship with the node; the number of power transmission lines represents the actual number of lines between the node and the adjacent node. When constructing the carbon emission propagation network of the park, each node is connected to other nodes through power transmission lines, wherein in this embodiment, the power transmission lines are used to represent the number of adjacent nodes powered by the node, for example, the number of power transmission lines is used to represent the number of power supplied by a heat and power equipment node to downstream users. The more the number of power transmission lines, the higher the degree of connection of the node in the network; in the carbon emission propagation model, the number of lines can be used as the weighted basis of "degree".

[0093] Based on the total number of nodes and the number of power transmission lines, the demand node centrality and the supply node centrality are obtained using a preset centrality calculation formula. The total number of nodes refers to the number of nodes in the entire network. The number of power transmission lines refers to the number of direct connections between a node and an adjacent node, that is, the "degree" of the node. The higher the degree, the closer the contact with other nodes and the more important the position. The preset centrality calculation formula is as follows:

[0094] Wherein, C D(V) represents the node centrality; D eg(v) represents the number of power transmission lines; n represents the total number of nodes.

[0095] By first determining the number of power transmission lines between any node and an adjacent node, and then based on the total number of nodes and the number of power transmission lines, the supply node centrality of the nodes in the supply carbon emission propagation network and the demand node centrality of the nodes in the demand carbon emission propagation network are obtained using a preset centrality calculation formula, so as to quantitatively evaluate the importance of the nodes in the supply end and the demand end in the carbon emission propagation network, which helps to more scientifically understand and manage the carbon emission propagation process.

[0096] In one of the embodiments of the present embodiment, the device execution instruction includes a heat and power energy equipment and a clean energy equipment, and the carbon cooperative control instruction is obtained by adjusting the device execution instruction and the load control instruction of the target park according to the carbon deviation result and the preset carbon integration mechanism, which includes the following steps: S810, obtain carbon credits of high-carbon emission equipment and carbon credits of clean energy equipment in the thermal power energy equipment, and if the carbon credits are less than a preset carbon credit threshold, determine a carbon deviation allocation value corresponding to the high-carbon emission equipment according to a carbon deviation result; S820, calculate a power generation reduction amount of the thermal power energy equipment according to the carbon deviation allocation value; S830, if the carbon credits of the clean energy equipment are greater than the preset carbon credit threshold, calculate a clean energy power generation amount according to the carbon credits of the clean energy equipment; S840, respectively convert the power generation reduction amount and the clean energy power generation amount into power adjustment values in a preset time period to obtain a reduced power generation of the high-carbon emission equipment and an increased power generation of the clean energy equipment; S850, predict a park load demand of the demand side according to the reduced power generation and the increased power generation of the supply side; S860, identify a net adjustment gap of the demand side according to the park load demand and a load regulation instruction; S870, adjust the load regulation instruction through the net adjustment gap to obtain a carbon coordinated regulation instruction.

[0097] obtain carbon credits of high-carbon emission equipment and carbon credits of clean energy equipment in the thermal power energy equipment, and if the carbon credits are less than a preset carbon credit threshold, determine a carbon deviation allocation value corresponding to the high-carbon emission equipment according to a carbon deviation result; the thermal power energy equipment refers to an energy equipment or system that produces both heat and electricity. For example, a combined heat and power plant, which burns fuel to generate steam to drive a steam turbine to generate electricity, and uses the waste heat generated during the power generation process to supply heat to the surrounding area. The carbon credit is an index for quantitatively measuring the performance of equipment in terms of carbon emission, which can be understood as scoring the "behavior" of the carbon emission of the equipment. For high-carbon emission equipment, the carbon credit can be calculated based on its actual carbon emission, carbon emission per unit of energy output, and other factors; for clean energy equipment, the carbon credit can be calculated based on the amount of carbon emission reduced by replacing traditional high-carbon energy, and other factors. The preset carbon credit threshold is a standard value set in advance; the carbon deviation result refers to the difference between the actual carbon emission and the target carbon emission. When there is a carbon deviation, i.e., the carbon emission exceeds the standard, according to certain rules, the amount of carbon deviation that the high-carbon emission equipment needs to bear is calculated.

[0098] According to the carbon deviation allocation value, the power generation reduction amount of the thermal power energy equipment is calculated; the carbon deviation allocation value refers to the part of carbon deviation that the high-carbon emission equipment needs to bear when the actual carbon emission of the thermal power energy system deviates. The power generation of the high-carbon emission equipment is closely related to carbon emission. It is assumed that the high-carbon emission equipment will produce a fixed proportion of carbon emission for generating a certain amount of power. Given the carbon deviation allocation value, the power generation reduction amount of the equipment can be deduced according to the corresponding relationship between power generation and carbon emission. For example, if X tons of carbon dioxide corresponds to a reduction of Y degrees of power generation according to the proportion, Y degrees is the power generation reduction amount of the thermal power energy equipment.

[0099] If the carbon credit of the clean energy equipment is greater than the preset carbon credit threshold, the clean energy power generation is calculated according to the carbon credit of the clean energy equipment; the preset carbon credit threshold is a standard value set in advance, which is used to judge whether the carbon emission reduction contribution of the clean energy equipment reaches the expectation or requirement. First, it is judged whether the carbon credit of the clean energy equipment is greater than the preset carbon credit threshold. If it is greater, it means that the equipment performs well in carbon emission reduction and reaches or exceeds the expected carbon contribution standard. Then, the clean energy power generation of the clean energy equipment is calculated according to its carbon credit. Because the setting of carbon credit is often related to power generation, the power generation can be deduced from the carbon credit. For example, given that the carbon credit calculation rule is 10 carbon credits for generating 1000 degrees of power, and the carbon credit of a certain solar photovoltaic power station is 600 credits, the clean energy power generation of the solar photovoltaic power station is 60000 degrees by calculation of 600÷10×1000=60000 degrees.

[0100] The power reduction of the high-carbon emission equipment and the power increase of the clean energy equipment are obtained by converting the power reduction amount and the clean energy power into power adjustment values in a preset time period respectively. The power reduction amount refers to the amount of power reduction of the high-carbon emission equipment according to the previous calculation (for example, calculated according to the carbon deviation allocation value). The clean energy power is the actual or should-be power of the clean energy equipment calculated by carbon integration or the like. The preset time period is a predetermined time length for unified measurement of power. The power reduction is for the high-carbon emission equipment, which is the power reduction value of the power per unit time in the preset time period converted from the power reduction amount. For example, if the power reduction amount is 10,000 degrees and the preset time period is 1 hour, the power reduction is 10,000 ÷ 1 = 10,000 kW (i.e. 10,000 degrees less per hour, and the power reduction is 10,000 kW). The power increase is for the clean energy equipment, which is the power increase value of the power per unit time in the preset time period converted from the clean energy power. For example, if the clean energy power is 80,000 degrees and the preset time period is 1 hour, the power increase is 80,000 ÷ 1 = 80,000 kW (i.e. 80,000 degrees more per hour, and the power increase is 80,000 kW). The power reduction amount and the clean energy power are both power, but the operation control of the power system pays more attention to power. Therefore, unit conversion is needed to divide the power by the preset time period to obtain the power adjustment value. For example, if the power reduction amount is E (degrees) and the preset time period is T (hours), the power reduction P of the high-carbon emission equipment is E ÷ T; if the clean energy power is E (degrees) and the preset time period is T (hours), the power increase P of the clean energy equipment is E ÷ T. 减 降 减 清 增 清

[0101] ​​​​​​According to the supply side of the drop power and the increase in power demand side of the park load demand; supply side of the power generation power adjustment will affect the power supply of the entire power system. Assuming that the power system is a balanced whole (ideal case, supply = demand), when the supply side of the high carbon emission equipment drop power and clean energy equipment increase power, the influence of this supply change on the demand side of the park load demand needs to be predicted. The prediction model can be established based on historical data and the operation law of the power system. For example, past data shows that when the supply side clean energy increases power by 10 MW, the load demand of a certain type of industrial enterprise in the park (which has a certain preference or adaptability for clean energy power) may increase by 5 MW (because clean energy power may be lower in price or more stable, enterprises expand production); at the same time, the high carbon emission equipment drop power by 8 MW, another part of the park that relies on traditional power and is sensitive to price may reduce the load demand by 3 MW because of the shortage of power supply or the increase in price. By comprehensively considering the historical relationship between these supply side power adjustment (drop power and increase power) and the demand side of the park load demand change (determined by big data analysis, mathematical modeling, etc.), the load demand of the demand side of the park can be predicted.

[0102] According to the park load demand and load control instruction to identify the demand side of the net regulation gap; the park load demand refers to the demand of various electrical equipment in the park within a certain period of time; the load control instruction is the command issued by the power system dispatching department or the park energy management department to adjust the park load; the net regulation gap refers to the difference between the actual load demand of the park and the load demand after adjusting according to the load control instruction. First of all, the park load demand (based on the operation of electrical equipment in the park, production plan, etc. Normal load demand) is determined. Then receive the load control instruction (such as the above-mentioned drop load or increase load instruction). Calculate the target load after adjusting according to the load control instruction, for example, the original load demand of the park is L 原 , the control instruction requires to drop load by 10%, then the target load L 目 =L 原 ×(1-10%). The net regulation gap = |L 原 -L 目 | (take absolute value because only pay attention to the gap size, regardless of more or less). For example, the original load demand of the park is 100 MW, and the control instruction requires to drop to 90 MW, and the net regulation gap is |100-90|=10 MW.

[0103] The net adjustment gap is the difference between the actual load demand of the park and the target load after adjustment according to the load regulation instruction. The load regulation instruction is the command issued by the power system dispatching department or the park energy management department to adjust the load of the park. The energy-carbon collaborative regulation instruction is a regulation instruction that comprehensively considers the balance of energy (power) supply and demand and carbon emission control, not only focusing on the adjustment of the power load to meet the operation demand of the power system, but also focusing on how to reduce carbon emissions through load adjustment. The net adjustment gap reflects the deviation of the load regulation instruction in actual execution. By analyzing this gap, the original load regulation instruction can be optimized. For example, if the net adjustment gap is because the load regulation instruction requires too high power generation of clean energy equipment, and the related equipment in the park cannot actually achieve it, then when adjusting the instruction, the requirement for the power generation of the clean energy equipment needs to be reduced, and a new energy-carbon collaborative regulation instruction is formed in combination with the carbon emission target. In another case, if the net adjustment gap is due to the fact that some high-energy-consumption and high-carbon-emission enterprises in the park do not respond to the load reduction instruction, then when adjusting the instruction, the load regulation intensity on these enterprises can be increased, and carbon emission reward and punishment measures are matched, so that the new instruction can guarantee the balance of power supply and demand and effectively control carbon emissions.

[0104] By identifying the net adjustment gap and adjusting the load regulation instruction to obtain the energy-carbon collaborative regulation instruction, fine management of thermal power equipment and clean energy equipment can be realized, the balance of power supply and demand can be guaranteed, carbon emissions can be effectively controlled, energy and environment can be collaboratively optimized, the operation efficiency of the power system can be improved, and the sustainable development ability can be improved, thereby providing a scientific, systematic and operable solution for park energy management and energy-carbon collaborative regulation.

[0105] The embodiment of the present application also provides a machine readable storage medium, which stores instructions for causing a machine to execute the energy-carbon integrated management method based on digital twinning.

[0106] The embodiment of the present application also provides an electronic device, which comprises: a memory configured to store instructions; and a processor configured to call the instructions from the memory and capable of realizing the energy-carbon integrated management method based on digital twinning when executing the instructions.

[0107] Those skilled in the art will appreciate that embodiments of the application can be readily used as a method, a system or a computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0108] The application is described herein with reference to the Figures, which illustrate the described embodiments. The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0109] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0110] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0111] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0112] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM) for storing structural information and / or instruction code. Both can be within one or more memory devices 1225. Alternatively, some memory devices can provide a combination of one or more of the foregoing types of memories. Since memory is a computer-readable medium, it can also include a medium that reflects, either mentally or electronically, data pending output by a storage or retrieval device. The memory can be used for storing various data used during the execution of operating systems or other code, such as application programs, which utilize or otherwise rely on the

[0113] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0114] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0115] The above only is the embodiment of the present application, and is not used to limit the present application. The present application can have various changes and variations for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A carbon integration management method based on digital twinning, characterized in that, The method is applied to an energy-carbon management system, the energy-carbon management system comprises a supply side and a demand side, the supply side comprises clean energy, thermal power energy and outsourcing energy, and the method comprises the following steps: Real-time acquisition of clean energy data, thermal power equipment data and outsourcing energy data of the supply side and planned load data and production process data of the demand side through the energy-carbon management system; Prediction of a supply carbon emission propagation network according to the clean energy data, the thermal power equipment data and the outsourcing energy data by using a preset first carbon emission propagation model; Prediction of a demand carbon emission propagation network according to the planned load data and the production process data of the demand side by using a preset second carbon emission propagation prediction model; Calculation of carbon emission factors according to the clean energy data, the thermal power equipment data and the outsourcing energy data respectively; Generation of preliminary control instructions according to the carbon emission factors, the supply carbon emission propagation network and the demand carbon emission propagation network, the preliminary control instructions comprising equipment execution instructions of the supply side and load control instructions of the demand side; Simulation in a digital twin model in combination with the equipment execution instructions and the load control instructions to obtain simulation results, and carbon deviation checking according to the simulation results to obtain carbon deviation results; Adjustment of the equipment execution instructions and the load control instructions of the target park according to the carbon deviation results and a preset carbon integration mechanism to obtain energy-carbon collaborative control instructions.

2. The method of claim 1, wherein, The method further comprises the following steps: In the case that the power generation amount of the clean energy is greater than a first preset threshold and the real-time power consumption data is less than a second preset threshold, the type of the fuel for power generation of the thermal power energy is acquired; Determination of the unit calorific value carbon content and the fuel low-heat value corresponding to the type of the fuel for power generation in a preset database; Acquisition of the fuel input amount and the electric energy output amount of each power generation device, and calculation of the average power generation efficiency of each power generation device in combination with the product of the fuel input amount and the fuel low-heat value and the electric energy output amount; Calculation of the average carbon emission value per kilowatt-hour according to the average power generation efficiency and the unit calorific value carbon content; Multiplication of the power generation amount of the clean energy and the carbon emission value to determine the equivalent carbon emission reduction amount of the target park, which is used to determine the carbon emission reduction amount reduced by the clean energy instead of the thermal power energy.

3. The method of claim 1, wherein, The method of predicting the supply carbon emission propagation network according to the clean energy data, the thermal power equipment data and the outsourcing energy data by using the preset first carbon emission propagation model comprises the following steps: Integration of the clean energy data, the thermal power equipment data and the outsourcing energy data to construct a park entity network graph, the park entity network graph being a network graph of the supply side; Calculation of the instantaneous carbon emission intensity for any node of the park entity network graph; Determination of the carbon emission event state of each node according to the instantaneous carbon emission intensity; Simulation of carbon emission paths by using the preset first carbon emission propagation model in combination with the carbon emission event state of each node and the park entity network graph; Calculation of the carbon emission propagation rate, the carbon emission recovery rate and the carbon emission propagation time lag between each node according to the carbon emission paths; Obtaining of a propagation probability matrix, a carbon emission propagation earliest arrival time and a node risk heat value by using a Monte Carlo sampling algorithm in combination with the carbon emission propagation rate, the carbon emission recovery rate and the carbon emission propagation time lag. The supply carbon emission propagation network is obtained according to the propagation probability matrix, the earliest arrival time of carbon propagation, the node risk heat value and the park entity network graph.

4. The method of claim 3, wherein, The node of the park entity network graph includes a thermal power equipment node, and the instantaneous carbon emission intensity of any one node of the park entity network graph includes the following steps: In the case that the node is the thermal power equipment node, the basic operation data of each thermal power equipment node is obtained, and the basic operation data includes fuel consumption, unit carbon emission factor, actual power generation and actual heat supply; The electric side allocation coefficient and the heat side allocation coefficient are calculated according to the basic operation data; The total carbon emission of the thermal power equipment is calculated by multiplying the fuel consumption and the unit carbon emission factor; The electric side carbon emission and the heat side carbon emission are obtained by multiplying the total carbon emission of the thermal power equipment with the electric side allocation coefficient and the heat side allocation coefficient respectively; The electric side carbon emission intensity and the heat side carbon emission intensity are calculated by using the ratio between the electric side carbon emission and the actual power generation and the ratio between the heat side carbon emission and the actual heat supply, and the carbon emission intensity and the heat side carbon emission intensity are used to represent the instantaneous carbon emission intensity.

5. The method of claim 3, wherein, The carbon emission event state of each node is determined according to the instantaneous carbon emission intensity, and the method includes the following steps: The carbon emission residual value in the preset time period is calculated by combining the instantaneous carbon emission intensity and the preset intensity reference value, and the carbon emission climbing rate is calculated according to the carbon emission residual value in the preset time period; The node with the carbon emission residual value greater than or equal to the preset residual threshold value and the carbon emission climbing rate greater than or equal to the preset climbing rate threshold value is taken as the carbon emission event starting node, and the event occurrence position of each carbon emission event starting node is determined; The node edge weight value between each node is calculated according to the event occurrence position, and the propagation path set is determined according to the node edge weight value; The duration of the carbon emission event state corresponding to each node in each propagation path set is determined; The high carbon emission source is screened by combining the duration and the propagation path set, and the high carbon emission source is used to represent the carbon emission event state.

6. The method of claim 1, wherein, The preliminary control instruction is generated according to the carbon emission factor, the supply carbon emission propagation network and the demand carbon emission propagation network, and the preliminary control instruction includes the device execution instruction of the supply side and the load control instruction of the demand side, and the method includes the following steps: The fuel type and the average power generation efficiency of the thermal power equipment and the marginal carbon emission factor of the purchased energy are obtained through the carbon management system; The standard emission factor corresponding to the fuel type is determined; The effective carbon emission factor is calculated according to the standard emission factor and the average power generation efficiency; The supply node centrality between the nodes in the supply carbon emission propagation network and the demand node centrality between the nodes in the demand carbon emission propagation network are determined respectively, and the supply node centrality and the demand node centrality are used to display the importance of the nodes; For any one node in the supply carbon emission propagation network and the demand carbon emission propagation network, a centrality coefficient is given to each node according to the supply node centrality and the demand node centrality; The effective carbon emission factor and the marginal carbon emission factor corresponding to each node are determined respectively, and the node superposition weight corresponding to each node is calculated according to the effective carbon emission factor, the marginal carbon emission factor and the centrality coefficient given to each node. Generate preliminary regulation instructions according to the node superposition weight corresponding to each node.

7. The method of claim 6, wherein, The steps for determining the supply node centrality between nodes in the supply carbon emission propagation network and the demand node centrality between nodes in the demand carbon emission propagation network respectively include: For any node, determine the number of power transmission lines between each node and another adjacent node; Based on the total number of nodes and the number of power transmission lines, the demand node centrality and the supply node centrality are obtained by using a preset centrality calculation formula.

8. The method of claim 1, wherein, The device execution instructions include thermal power generation devices and clean energy devices, and the carbon cooperative regulation instructions obtained by adjusting the device execution instructions and the load regulation instructions of the target park according to the carbon deviation result and the preset carbon credit mechanism include the following steps: Obtain the carbon credit of the high-carbon emission device in the thermal power generation device and the carbon credit of the clean energy device. If the carbon credit is less than the preset carbon credit threshold, the carbon deviation allocation value corresponding to the high-carbon emission device is determined according to the carbon deviation result; Calculate the power generation reduction amount of the thermal power generation device according to the carbon deviation allocation value; If the carbon credit of the clean energy device is greater than the preset carbon credit threshold, calculate the clean energy power generation amount according to the carbon credit of the clean energy device; Convert the power generation reduction amount and the clean energy power generation amount into power adjustment values in a preset time period to obtain the reduced power of the high-carbon emission device and the increased power of the clean energy device; According to the reduced power and the increased power on the supply side, the park load demand on the demand side is predicted; According to the park load demand and the load regulation instruction, the net regulation gap on the demand side is identified; Adjust the load regulation instruction through the net regulation gap to obtain the carbon cooperative regulation instruction.

9. A carbon integration management system based on digital twinning, characterized by, Comprise: a memory configured to store instructions; and a processor configured to call the instructions from the memory and enable the implementation of the carbon integrated management method based on digital twinning according to any one of claims 1-8 when executing the instructions.

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

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