Comprehensive energy system scheduling method considering carbon footprint and market incentive

Abstract

The application discloses a kind of integrated energy system scheduling method considering carbon footprint and market incentive.First, the whole process carbon footprint of IES is calculated, and a ladder type CCER income model is constructed and the CCER income obtained by IES through wind and light power generation project and load low-carbon demand response is calculated according to the model.Then, considering the whole process carbon footprint of IES, the CCER income obtained by IES through wind and light power generation project and load low-carbon demand response, an upper integrated energy operator scheduling model and a lower load aggregator scheduling model are constructed.Finally, the upper integrated energy operator scheduling model and the lower load aggregator scheduling model are solved by alternating cyclic iteration, and the low-carbon economic dispatch of IES is realized.The method divides the whole process carbon footprint of IES into exogenous carbon footprint and endogenous carbon footprint, which can improve the completeness and accuracy of system carbon emission calculation, and combined with the CCER market mechanism of ladder type pricing, it can greatly improve the overall low-carbon economic benefit of IES.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization scheduling, specifically an integrated energy system scheduling method that considers carbon footprint and market incentives. Background Technology

[0002] Integrated energy systems (IES), characterized by multi-energy complementarity and flexible dispatch, have become an important carrier for the transformation of the energy structure towards green and low-carbon development. However, how to achieve accurate measurement of carbon emissions throughout the entire process of integrated energy systems has become an urgent issue to be studied in the context of low-carbon development.

[0003] Existing studies use the Life Cycle Assessment (LCA) method to quantify and trace the carbon footprint of a system. However, the average carbon emission factor obtained by the LCA method cannot meet the spatiotemporal heterogeneity of carbon emissions, and most studies have established IES carbon emission models that do not take into account the carbon emissions generated by external energy purchases, thus failing to fully measure the carbon emissions of the system.

[0004] Accurate quantification of carbon emissions at energy input sources is a prerequisite for accurate assessment of system-wide carbon emissions; therefore, the carbon emission characteristics of source-side power generation units must be considered. Existing research utilizes fluid dynamics theory to construct mathematical models of coal-fired power boilers, forming carbon emission factor calculation models that integrate the effects of coal type and carbon oxidation rate by considering changes in carbon and sulfur oxidation rates. Other studies employ linearized models to describe turbine heat rates and analyze the unit's carbon emission characteristics; alternatively, they treat the carbon emission factors of source-side power generation units as fixed values ​​and include them in the system's carbon emission accounting framework to achieve low-carbon economic dispatch for the Energy System Engineering (IES). However, traditional source-side power generation units have a large output adjustment range to smooth load fluctuations, resulting in significant differences in carbon emission factors under low-load conditions. Traditional linear carbon emission factor models are insufficient to reflect the actual situation and cannot provide a reliable basis for system carbon accounting.

[0005] Furthermore, based on accurate carbon metering and accounting, energy user participation in carbon trading is an effective means to promote energy structure optimization and achieve energy conservation and emission reduction in energy systems (IES). Some studies have used energy-carbon interaction pricing to guide the response of electricity-gas-heat IES users, effectively improving the level of new energy consumption and the low-carbon economy of the system. Other studies have analyzed the emission reduction mechanism of green certificates and constructed an IES low-carbon economic dispatch model with a joint green certificate-carbon trading mechanism, enabling the system to further reduce carbon emissions while obtaining green certificate benefits. These studies use dynamic electricity prices or carbon prices as carbon reduction incentives, but obtaining real-time price signals is difficult under my country's current market system. The Chinese certified emission reduction (CCER) trading mechanism has become an important supplement to my country's carbon quota market, further broadening emission reduction pathways. Some studies share the benefits obtained from CCER projects with thermal power units, compensating for their deep peak-shaving costs and promoting the multi-path transformation of coal-fired power generation projects. Some studies have also explored the carbon reduction benefits of demand response in power system dispatch based on life cycle carbon emission assessment methods combined with the CCER mechanism, but there are shortcomings in the spatiotemporal resolution of carbon emission metering. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the technical problem this invention aims to solve is to provide a comprehensive energy system scheduling method that considers carbon footprint and market incentives; by combining a CCER market mechanism with tiered pricing, and using complete and accurate dynamic load carbon emission intensity to guide IES load demand response, an upper and lower level scheduling model is formed, which can improve the overall low-carbon economic benefits of IES to a greater extent.

[0007] The present invention solves the aforementioned technical problem by adopting the following technical solution: A comprehensive energy system dispatching method considering carbon footprint and market incentives, characterized by comprising the following steps: Step 1: Calculate the carbon footprint of the entire IES process according to equation (18). ; (18) In the formula, for Time period Carbon emission intensity of load type for Time period Energy consumption after load demand response For the number of load types, The scheduling period; If the carbon emission intensity of the load equals the carbon potential of the corresponding energy transmission network node, then: (17) In the formula, for Time period Carbon potential of nodes in energy transmission networks; The energy transmission network includes the IES internal electrical network, gas network and heat network. The carbon potential of the IES internal electrical network nodes is calculated according to Equation (14). (14) In the formula, for Carbon potential of IES intra-terminal electrical network nodes , for IES intra-time network branch Power and branch carbon flux density at the injection node; This represents the number of electrical network branches connected to the node within the IES. for Public energy grid Power injected into the node; for Periodic IES Consumption of Public Energy Network Exogenous carbon footprint factor at energy levels; The number of public energy power grids; for Time-of-use energy conversion equipment The electrical power injected into the node at the output end; for Time-of-use energy conversion equipment Carbon footprint factor at the output end; The total number of energy conversion devices connected to the node; Calculate the carbon potential of the gas network and heat network nodes in the IES according to equations (15) and (16): (15) (16) In the formula, , They are respectively Carbon potential of gas network and thermal network nodes within the IES period; , , They are respectively IES intra-period gas network branch Public energy gas network and energy conversion equipment The gas power injected into the output node; for IES intra-period gas network gas branch Carbon flux density in the injection node's branch; This represents the number of gas network branches connected to the nodes within the IES. for Periodic IES Consumption Public Energy Gas Network Exogenous carbon footprint factor at energy levels; The number of public energy gas networks; , They are respectively IES Intra-period heat network branch and energy conversion equipment Thermal power injected into the node at the output end; for IES Intra-period heat network branch Carbon flux density in the injection node's branch; The number of hot network branches connected to the node within the IES; Step 2: Construct a tiered CCER revenue model and calculate the CCER revenue obtained by IES through wind and solar power projects and load low-carbon demand response based on this model; The ladder-type CCER revenue model is expressed as follows: (twenty three) In the formula, For IES projects CCER revenue obtained Indicates wind and solar power generation projects. This indicates a low-carbon demand response from the load; This refers to the unit price of CCER (CCER). For the project CCER obtained; The length of the CCER interval; The percentage increase in the tiered CCER price; CCERs are used to offset part of the system's carbon allowances. CCERs represent China's certified voluntary emission reductions. Step 3: Construct an upper-level integrated energy operator scheduling model and a lower-level load aggregator scheduling model; The objective function of the upper-level integrated energy operator scheduling model is: (twenty four) (25) (26) (27) (28) In the formula, The total operating cost for the upper-level integrated energy operator; for IES energy purchase cost during the period; for IES equipment operation and maintenance costs during the period; for The cost of wind and solar power curtailment during specific time periods; for IES carbon trading costs for different time periods; CCER revenue obtained by IES through wind and solar power projects; , , These are the unit price of standard coal, the unit price of natural gas, and the carbon trading price, respectively. , , Source-side power generation units Coal consumption characteristic coefficient; for Public energy grid Power injected into the node; for Public energy gas network during the period Power injected into the node; The number of public energy power grids; The number of public energy gas networks; The total number of energy conversion devices connected to the node; Energy conversion equipment The operation and maintenance cost coefficient; for Time-of-use energy conversion equipment Total output power; , , for Time-of-use energy conversion equipment The electrical power, gas power, and thermal power injected into the node at the output end; This refers to the penalty coefficient for wind and solar power curtailment. , They are respectively Total power generation of wind and solar turbines during the time period; Power generation of wind and solar turbines connected to the grid during the time period; , This refers to the carbon quota coefficient; for Total gas consumption of gas turbines and combined heat and power units during a given period; The objective function of the lower-level load aggregator scheduling model is: (29) (30) (31) In the formula, The overall cost for the lower-level load aggregator; for Energy costs for users during specific time periods; CCER revenue obtained from low-carbon demand response to loads; for Time-based user utility function; Energy price for load; , For users' consumption Preference coefficient for load type; Step 4: Alternately iterate and solve the upper-level integrated energy operator scheduling model and the lower-level load aggregator scheduling model to achieve IES low-carbon economic scheduling.

[0008] Furthermore, the formula for calculating the carbon footprint factor at the output of the energy conversion device is as follows: (13) In the formula, for Time-of-use energy conversion equipment The nodal carbon potential at the output end, Energy conversion equipment Endogenous carbon footprint factor; Endogenous carbon footprint factors mainly consider energy storage devices, power-to-gas conversion devices, and wind and solar power units; the endogenous carbon footprint factor of energy storage devices... The calculation formula is: (1) In the formula, and These are the carbon footprint factors for the production and transportation of energy storage equipment, respectively. For energy storage equipment conversion efficiency; and These are the first stages of the energy storage equipment production process. Phase 1 Carbon emission intensity and energy consumption of the materials; The first consumption in the production process of energy storage equipment The unit loss coefficient of the material; and These represent the number of stages and the number of material types in the production process of energy storage equipment; and These are the first stages of the energy storage equipment transportation process. The first mode of transportation Carbon emission intensity and energy consumption of the materials; and The total number of transportation methods and material types in the transportation of energy storage equipment; The formula for calculating the endogenous carbon footprint factor of electro-gas conversion equipment is as follows: (2) In the formula, , , These are the endogenous carbon footprint factors for power-to-gas conversion equipment, electrolyzers, and methane reactors, respectively. and These are the carbon footprint factors for the production and transportation of electrolytic cells, respectively. The conversion efficiency of the electrolytic cell; and These are the first steps in the electrolytic cell production process. Phase 1 Carbon emission intensity and energy consumption of the materials; The first consumption in the electrolytic cell production process The unit loss coefficient of the material; and These are the first steps in the electrolytic cell transportation process. The first mode of transportation Carbon emission intensity and energy consumption of the materials; and These represent the number of stages and the number of material types in the electrolytic cell production process, respectively. and These represent the total number of transportation methods and the number of material types involved in the electrolytic cell transportation process. Endogenous carbon footprint factor of wind and solar power units The calculation formula is: (3) In the formula, , These are the endogenous carbon footprint factors for wind turbines and photovoltaic units, respectively. The formula for calculating the node carbon potential at the output end of an electric boiler is: (7) In the formula, , They are respectively The node carbon potential at the input and output ends of the time-limited electric boiler; , They are respectively Energy power at the input and output terminals of the time-limited electric boiler; For electric boiler conversion efficiency; The formula for calculating the node carbon potential at the output end of the electro-gas converter is as follows: (8) In the formula, , They are respectively The node carbon potential at the input and output ends of the time-phase electrolyzer; for The electrical energy consumed by the electrolyzer during a given time period; for Hydrogen power output from the electrolyzer during the time period; and They are respectively The node carbon potential at the input and output of the methane reactor during the time period; for Carbon flow rate of the methane reactor during the specified time period; for Hydrogen power consumed by the methane reactor during the specified time period; for The amount of natural gas produced by the methane reactor during the specified time period; The conversion efficiency of the methane reactor; The formula for calculating the nodal carbon potential at the gas turbine output is: (9) In the formula, , They are respectively The node carbon potential at the input and output of the gas turbine during the time period; for The amount of natural gas consumed by the gas turbine during a given period; for The electrical power output of the gas turbine during a given period; For gas turbine conversion efficiency; The formula for calculating the node carbon potential at the output end of a combined heat and power (CHP) unit is as follows: (10) In the formula, , , They are respectively The node carbon potential at the input, electrical output, and heat output ends of the time-phase cogeneration unit; 、 , They are respectively The natural gas power consumed, electrical power output, and thermal power output of the combined heat and power unit during the specified time period; , These refer to the gas-to-electricity conversion efficiency and the gas-to-heat conversion efficiency of the combined heat and power unit, respectively. In energy storage mode, the formula for calculating the node carbon potential at the output end of the energy storage device is: (11) In the formula, for Carbon emissions from charging energy storage devices during specific time periods; for Charging power of time-limited energy storage devices; In energy storage state The nodal carbon potential at the output of the time-of-use energy storage device. For energy storage duration; In the energy release state, the formula for calculating the node carbon potential at the output end of the energy storage device is: (12) In the formula: for The carbon emissions from energy storage devices are released during specific time periods; for Discharge power of time-limited energy storage devices; In the state of energy release The nodal carbon potential at the output of the time-of-use energy storage device; For the duration of energy release; The discharge efficiency of energy storage devices; , They are respectively , Carbon flux density of electricity stored in time-limited energy storage devices; for Available capacity of time-of-use energy storage devices , for Carbon emissions from charging and releasing energy storage devices during specific time periods.

[0009] Furthermore, the formula for calculating the exogenous carbon footprint factor when IES consumes energy from the public energy grid is as follows: (4) (5) In the formula, for Public energy network branch during the time period Power injected into the node; for Public energy network branch during the time period Carbon flux density in the injection node's branch; The number of public energy network branches connected to the nodes; for Time-based source-side capacity units Power injected into the node; for Time-based source-side capacity units Dynamic carbon emission factors; The number of source-side power generation units connected to the node; The power segmentation threshold; , 、 、 is a constant coefficient, and is the parameter to be fitted; This refers to the capacity of the source-side generating units; The formula for calculating the exogenous carbon footprint factor when IES consumes energy from the public energy gas network is as follows: (6) In the formula, , They are respectively Public energy gas network branch during the period Natural gas source Gas power injected into the node; , They are respectively Public energy gas network branch during the period Natural gas source Carbon flux density in the injection node's branch; The number of public energy gas network branches connected to the nodes; This represents the number of natural gas sources connected to the node.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention divides the entire carbon footprint of an Integrated Ecosystem for Manufacturing (IES) into exogenous and endogenous carbon footprints, which improves the completeness and accuracy of system carbon emission calculations and provides reliable evaluation indicators for subsequent low-carbon scheduling strategies. The exogenous carbon footprint of the IES is calculated based on carbon emission flow theory to improve the spatiotemporal resolution of system carbon emissions; the endogenous carbon footprint of the IES is obtained by calculating the carbon emissions of each device in production, transportation, and other stages based on life cycle assessment methods.

[0011] 2. Considering the carbon reduction benefits of the CCER market mechanism on clean energy and load side, a tiered CCER revenue model is proposed. Compared with the existing fixed CCER price, the revenue per unit is increased step by step as the amount of CCER obtained by the system using various methods increases. This can more effectively improve the enthusiasm of wind and solar power projects or load demand response projects in IES to use CCER market means for carbon reduction, while ensuring the fairness of CCER projects with strong carbon reduction capabilities and willingness.

[0012] 3. Combining the carbon emission characteristics of source-side power generation units, a piecewise nonlinear model of dynamic carbon emission factors of source-side power generation units is proposed to refine the functional relationship between unit output power and carbon emission factors. The piecewise parameter regression method is used to fit the continuous curve of dynamic carbon emission factors, which can improve the quantification accuracy of the actual carbon emission factors of the units, thereby improving the calculation accuracy of carbon emissions of source-side power generation units in IES, and providing a foundation for refining the carbon footprint of the entire IES process. Attached Figure Description

[0013] Figure 1 This is a diagram of the architecture of an integrated energy system. Figure 2 This is a flowchart of the model solution process for the present invention; Figure 3 This is a schematic diagram of the integrated energy system structure for an example. Figure 4 Typical daily wind and solar power generation and load power prediction curves for the example; Figure 5 The piecewise regression fitting curve of the dynamic carbon emission factor of the source-measured capacity unit is shown in the example. Figure 6 The IES carbon footprint distribution map for the 1:00 time period is shown in the example. Figure 7 The IES carbon footprint distribution map for the 11:00 time period in the example; Figure 8 This is a power distribution diagram before the power load demand response in an embodiment. Figure 9 The following is a diagram showing the carbon emission factors at different time periods after the demand response and the load distribution before and after the demand response, as an example. Detailed Implementation

[0014] Specific embodiments are given below with reference to the accompanying drawings. These specific embodiments are only used to further describe the technical solution of the present invention in detail, and are not intended to limit the scope of protection of this application.

[0015] This invention provides a comprehensive energy system scheduling method that considers carbon footprint and market incentives, comprising the following steps: Step 1: Calculate the carbon footprint of the entire IES process; The IES's total carbon footprint consists of two parts: endogenous carbon footprint and exogenous carbon footprint. The endogenous carbon footprint mainly includes the carbon emissions generated by various equipment within the system during production, transportation, and maintenance. The exogenous carbon footprint mainly includes the carbon emissions generated by the consumption of public energy during the operation of the IES.

[0016] 1.1 Calculation of the endogenous carbon footprint of energy conversion equipment based on LCA method; Energy conversion equipment in the IES mainly includes energy storage equipment, power-to-gas equipment, wind and solar turbines, electric boilers, gas turbines, and combined heat and power (CHP) units. Because electric boilers, gas turbines, and CHP units have long service lives, high material recycling rates, and relatively low life-cycle carbon emissions, the endogenous carbon footprint generated by these devices is negligible and is set to zero. The LCA (Life Cycle Assessment) method is used to calculate the carbon emissions per unit of energy consumption in the production and transportation stages of the three types of equipment with high life-cycle carbon emissions in the IES: energy storage equipment, power-to-gas equipment, and wind and solar turbines, and the endogenous carbon footprint factor is used to characterize it.

[0017] (1) The endogenous carbon footprint model of energy storage equipment is: (1) In the formula: The intrinsic carbon footprint factor of energy storage devices; and These are the carbon footprint factors for the production and transportation of energy storage equipment, respectively. For energy storage equipment conversion efficiency; and These are the first stages of the energy storage equipment production process. Phase 1 Carbon emission intensity and energy consumption of the materials; The first consumption in the production process of energy storage equipment The unit loss coefficient of the material; and These represent the number of stages and the number of material types in the production process of energy storage equipment; and These are the first stages of the energy storage equipment transportation process. The first mode of transportation Carbon emission intensity and energy consumption of the materials; and The total number of transportation methods and material types in the transportation of energy storage equipment.

[0018] (2) The power-to-gas conversion equipment includes an electrolyzer and a methane reactor. The endogenous carbon footprint model of the power-to-gas conversion equipment is as follows: (2) In the formula: , , These are the endogenous carbon footprint factors for power-to-gas conversion equipment, electrolyzers, and methane reactors, respectively. and These are the carbon footprint factors for the production and transportation of electrolytic cells, respectively. The conversion efficiency of the electrolytic cell; and These are the first steps in the electrolytic cell production process. Phase 1 Carbon emission intensity and energy consumption of the materials; The first consumption in the electrolytic cell production process The unit loss coefficient of the material; and These are the first steps in the electrolytic cell transportation process. The first mode of transportation Carbon emission intensity and energy consumption of the materials; and These represent the number of stages and the number of material types in the electrolytic cell production process, respectively. and These represent the total number of transportation methods and the number of material types involved in the electrolyzer's transportation process; existing research has extensively analyzed carbon emissions from LCA (Liquid Carbon Atomizer) methane reactors, therefore... Take a constant value.

[0019] (3) Wind and solar energy are clean energy sources. The carbon emissions generated by wind and solar turbines during operation are relatively small. Considering mainly the carbon emissions generated during production and transportation, the endogenous carbon footprint model of wind and solar turbines is as follows: (3) In the formula, The endogenous carbon footprint factor of wind and solar power units. , These are the endogenous carbon footprint factors for wind turbines and photovoltaic (PV) generators, respectively. Existing research has extensively analyzed the LCA carbon emissions of wind and solar turbines, therefore... , Take a constant value.

[0020] 1.2 Calculate the carbon footprint of IES from the consumption of energy from public energy networks based on carbon emission flow theory, i.e., the exogenous carbon footprint of IES; public energy networks refer to external networks that transmit energy into IES, including public energy electricity networks and gas networks; (1) Combine the dynamic carbon emission factor of the source-side power generation unit to calculate the carbon footprint generated per unit energy consumption during the process of IES consuming energy from the public energy grid, and characterize it with the exogenous carbon footprint factor. (4) In the formula: for Periodic IES Consumption of Public Energy Network Exogenous carbon footprint factor at energy levels; for Public energy network branch during the time period Power injected into the node; for Public energy network branch during the time period Carbon flux density in the injection node's branch; The number of public energy network branches connected to the nodes; for Time-based source-side capacity units Power injected into the node; for Time-based source-side capacity units Dynamic carbon emission factors; This refers to the number of source-side power generation units connected to the node.

[0021] The dynamic carbon emission factor of source-side power generation units is calculated using a piecewise nonlinear model, and is expressed as follows: (5) In the formula: The power segmentation threshold; , 、 、 is a constant coefficient, and is the parameter to be fitted; This refers to the capacity of the source-side generating units.

[0022] (2) The carbon footprint generated per unit of energy consumption during the process of IES consuming public energy gas network energy is characterized by exogenous carbon footprint factor; (6) In the formula: for Periodic IES Consumption Public Energy Gas Network Exogenous carbon footprint factor at energy levels; , They are respectively Public energy gas network branch during the period Natural gas source Gas power injected into the node; , They are respectively Public energy gas network branch during the period Natural gas source Carbon flux density in the injection node's branch; The number of public energy gas network branches connected to the node. This represents the number of natural gas sources connected to the node.

[0023] 1.3 Calculate the carbon footprint transferred from the public energy network to each energy conversion device, and construct an exogenous carbon footprint model for the energy conversion device; The carbon footprint of energy conversion equipment transferred from the public energy network mainly refers to the carbon footprint generated per unit of energy consumption during the process of energy conversion equipment drawing energy from the public energy network and converting it, i.e., the exogenous carbon footprint of energy conversion equipment, which is calculated based on the principle of carbon emission energy conservation and characterized by nodal carbon potential. The exogenous carbon footprint model for electric boilers is as follows: (7) In the formula, , They are respectively The node carbon potential at the input and output ends of the time-limited electric boiler; , They are respectively Energy power at the input and output terminals of the time-limited electric boiler; This refers to the conversion efficiency of the electric boiler.

[0024] The exogenous carbon footprint model for electro-gas conversion equipment is as follows: (8) In the formula: , They are respectively The node carbon potential at the input and output ends of the time-phase electrolyzer; for The electrical energy consumed by the electrolyzer during a given time period; for Hydrogen power output from the electrolyzer during the time period; and They are respectively The node carbon potential at the input and output of the methane reactor during the time period; for Carbon flow rate of the methane reactor during the specified time period; for Hydrogen power consumed by the methane reactor during the specified time period; for The amount of natural gas produced by the methane reactor during the specified time period; This represents the conversion efficiency of the methane reactor.

[0025] The exogenous carbon footprint model of a gas turbine is as follows: (9) In the formula, , They are respectively The node carbon potential at the input and output of the gas turbine during the time period; for The amount of natural gas consumed by the gas turbine during a given period; for The electrical power output of the gas turbine during a given period; This refers to the gas turbine conversion efficiency.

[0026] The exogenous carbon footprint model for combined heat and power units is as follows: (10) In the formula, , , They are respectively The node carbon potential at the input, electrical output, and heat output ends of the time-phase cogeneration unit; 、 , They are respectively The natural gas power consumed, electrical power output, and thermal power output of the combined heat and power unit during the specified time period; , These refer to the gas-to-electricity conversion efficiency and the gas-to-heat conversion efficiency of the combined heat and power (CHP) unit, respectively.

[0027] Energy storage devices, as a special type of energy regulation unit, have two states: energy storage and energy release. In energy storage mode, they act as a load, absorbing some carbon emissions; in energy release mode, they act as a power supply device, releasing some carbon emissions. Analogous to the state of charge of electrical energy storage, the exogenous carbon footprint per unit of energy consumption of an energy storage device is characterized as the ratio of the accumulated carbon flux to the remaining electrical energy. In energy storage mode, the exogenous carbon footprint model of an energy storage device is as follows: (11) In the formula: for Carbon emissions from charging energy storage devices during specific time periods; for Charging power of time-limited energy storage devices; In energy storage state The nodal carbon potential at the output of the time-of-use energy storage device. For energy storage duration; Under the condition of energy release, the exogenous carbon footprint model of the energy storage device is as follows: (12) In the formula: for The carbon emissions from energy storage devices are released during specific time periods; for Discharge power of time-limited energy storage devices; In the state of energy release The nodal carbon potential at the output of the time-of-use energy storage device; For the duration of energy release; The discharge efficiency of energy storage devices; , They are respectively , Carbon flux density of electricity stored in time-limited energy storage devices; for Available capacity of time-of-use energy storage devices , for Carbon emissions from charging and releasing energy storage devices during specific time periods.

[0028] Wind and solar power units are clean energy sources, and their exogenous carbon footprint is close to zero.

[0029] 1.4 Calculate the carbon footprint of each energy conversion device's output; The carbon footprint factor at the output end of the energy conversion device is obtained by adding the endogenous carbon footprint factor of the energy conversion device and the node carbon potential at the output end. (13) In the formula: , They are respectively Time-of-use energy conversion equipment The carbon footprint factor at the output end and the nodal carbon potential at the output end; Energy conversion equipment Endogenous carbon footprint factor.

[0030] 1.5 The carbon footprint of each energy network node in the IES is calculated based on the carbon emission flow theory and characterized by the node carbon potential. Combining the exogenous carbon footprint factor of the IES and the carbon footprint factor at the output of each energy conversion device, the carbon potential of the IES internal electrical network nodes is calculated using carbon emission flow theory, and expressed as: (14) In the formula: for Carbon potential of IES network nodes during the time period; , for IES intra-time network branch Power and branch carbon flux density at the injection node; This represents the number of electrical network branches connected to the node within the IES. for Public energy grid Power injected into the node; The number of public energy power grids; for Time-of-use energy conversion equipment The electrical power injected into the node at the output end; This represents the total number of energy conversion devices connected to the node.

[0031] Similar to the carbon emission flow of the IES internal electrical network, the carbon emission flow of the IES internal gas and heat networks is a virtual "carbon flow" dependent on the natural gas flow and hot water transportation. Therefore, the carbon footprint of each node in the IES internal gas and heat networks per unit of energy consumption is characterized by the node carbon potential as follows: (15) (16) In the formula: , They are respectively Carbon potential of gas network and thermal network nodes within the IES period; , , They are respectively IES intra-period gas network branch Public energy gas network and energy conversion equipment The gas power injected into the output node; for IES intra-period gas network gas branch Carbon flux density in the injection node's branch; This represents the number of gas network branches connected to the nodes within the IES. The number of public energy gas networks; , They are respectively IES Intra-period heat network branch and energy conversion equipment Thermal power injected into the node at the output end; for IES Intra-period heat network branch Carbon flux density in the injection node's branch; This represents the number of hot network branches connected to the node within the IES.

[0032] 1.6 Calculate the carbon footprint generated per unit of energy consumption for various load types, and characterize it using load carbon emission intensity; Based on the carbon emission flow theory, the carbon footprint generated per unit of energy consumption of various loads is equal to the carbon potential of the energy transmission network nodes connected to its nodes. Therefore, the carbon emission intensity of a load can be expressed as: (17) In the formula, for Time period Carbon emission intensity of load type for Time period Carbon potential of nodes in energy transmission networks; Ultimately, the carbon footprint of the entire IES process is expressed as: (18) In the formula, For the entire carbon footprint of IES, for Time period Energy consumption after load demand response For the number of load types, The scheduling period is [number].

[0033] Energy consumption after load demand response is calculated using the following formula: (19) In the formula, for Time period Energy used before load demand response; , for Time period The amount of load reduction and increase.

[0034] Step 2: Construct a tiered CCER revenue model and calculate the CCER revenue obtained by IES through wind and solar power projects and load low-carbon demand response based on this model; The CCER (China Certified Emission Reduction) market is an important type of carbon market in my country besides the CET market. Constructing a tiered pricing CCER market mechanism can not only provide precise incentives for clean energy emission reduction behavior on the source side, but also drive load-side users to participate in low-carbon demand response.

[0035] 2.1 CCERs obtained by wind and solar power projects The grid baseline emission factor is a core parameter for quantitatively verifying the carbon dioxide emission reduction benefits of clean energy projects. Based on the dynamically updated marginal emission factors of regional power grids published by the Ministry of Ecology and Environment of my country, the grid baseline emission factor is obtained as follows: (20) In the formula: , and These are the grid baseline emission factor, the electricity marginal emission factor, and the capacity marginal emission factor, respectively. and These are the weights of the marginal emission factor for electricity and the marginal emission factor for capacity, respectively. The CCER obtained by the wind and solar power generation project is: (twenty one) In the formula: CCER for wind and solar power projects; for Power generation and grid connection of wind and solar turbines during specific time periods.

[0036] 2.2 CCERs obtained from low-carbon demand response of load Load participation in low-carbon demand response can also generate significant carbon emission reduction benefits. By quantifying the carbon emission reduction of load low-carbon demand response based on load carbon emission intensity, and enabling certified carbon assets to realize economic value transformation in the CCER market, the CCER for load low-carbon demand response is: (twenty two) In the formula: CCERs obtained for low-carbon demand response to loads.

[0037] 2.3 Tiered CCER Revenue Model The tiered CCER unit price refers to dividing the salable CCERs into multiple ranges and calculating the unit price. The more CCERs the system obtains through wind and solar power projects or low-carbon demand response, the higher the CCER unit price, and the greater the CCER revenue the system receives. The tiered CCER revenue model is as follows: (twenty three) In the formula, For IES projects CCER revenue obtained Indicates wind and solar power generation projects. This indicates a low-carbon demand response from the load; This refers to the unit price of CCER (CCER). For the project CCER obtained; The length of the CCER interval; The price increase for CCERs is tiered; for each tier increase, the unit price of CCERs increases. ; CCERs are used to offset part of the system's carbon quota.

[0038] Step 3: Construct an upper-level integrated energy operator scheduling model and a lower-level load aggregator scheduling model; 3.1 Upper-level integrated energy operator dispatch model If the upper-level integrated energy operator aims to optimize the economic efficiency of IES operation, then the objective function of the upper-level integrated energy operator's scheduling model is: (twenty four) In the formula: The total operating cost for the upper-level integrated energy operator; for IES energy purchase cost during the period; for IES equipment operation and maintenance costs during the period; for The cost of wind and solar power curtailment during specific time periods; for IES carbon trading costs for different time periods; This refers to the CCER revenue obtained by IES through wind and solar power projects.

[0039] The cost calculation formulas are as follows: (25) (26) (27) (28) In the formula: , , These are the unit price of standard coal, the unit price of natural gas, and the carbon trading price, respectively. , , Source-side power generation units Coal consumption characteristic coefficient; for Public energy gas network during the period Power injected into the node; Energy conversion equipment The operation and maintenance cost coefficient; for Time-of-use energy conversion equipment Total output power; This refers to the penalty coefficient for wind and solar power curtailment. , They are respectively Total power generation of wind and solar turbines during the time period; Power generation of wind and solar turbines connected to the grid during the time period; For the entire carbon footprint of IES; , This refers to the carbon quota coefficient; for The total gas consumption of the gas turbine and combined heat and power unit during the period, i.e. ; CCERs can be used to offset part of the system's carbon quota.

[0040] The constraints of the upper-level integrated energy operator dispatch model include power balance constraints, output and ramp-up constraints of energy conversion equipment, power flow constraints of energy transmission network, and operation constraints of energy storage equipment. These are all basic constraints and will not be elaborated here.

[0041] 3.2 Lower-level load aggregator scheduling model The lower-level load aggregator aims to minimize overall costs. To reduce deviations in user energy consumption behavior during demand response, a utility function is used to represent user requirements for energy comfort. Therefore, the objective function of the lower-level load aggregator scheduling model is: (29) (30) (31) In the formula: The overall cost for the lower-level load aggregator; for Energy costs for users during specific time periods; CCER revenue obtained from low-carbon demand response to loads; for Time-based user utility function; Energy price for load; , For users' consumption The preference coefficient for similar loads.

[0042] The lower-level load aggregator scheduling model must satisfy the load demand response constraint: (32) (33) (34) (35) In the formula: , They are respectively Time period The status variables for load demand response transition in and out are 0-1 variables; , These are the maximum values ​​of the load demand response; This represents the maximum value of the total load demand response within the scheduling cycle.

[0043] Step 4: Alternately and iteratively solve the upper-level integrated energy operator scheduling model and the lower-level load aggregator scheduling model to achieve IES low-carbon economic scheduling; The upper-level integrated energy operator receives the energy consumption after responding to the load demand feedback from the user side. Subsequently, the power injected into the public energy network was adjusted. and Total output power of energy conversion equipment Power generation of wind and solar turbines to the grid Update the transmission power of each network branch within the IES, and thus update the load carbon emission intensity. And feed it back to the lower-level load aggregator; Lower-level load aggregators based on load carbon emission intensity To guide demand response under the incentive of the CCER market mechanism with tiered pricing, and to meet user utility needs. Minimize user energy costs under the premise of and IES full-process carbon footprint And the energy used after load demand response The data is then fed back to the upper-level integrated energy operator; through iterative optimization at both the upper and lower levels, the model convergence condition is met.

[0044] The convergence condition of the model is: (36) In the formula: and They represent the first 、 The objective function value of the upper-level integrated energy operator scheduling model in the next iteration; and They represent the first 、 The objective function value of the next iteration of the lower-level load aggregator scheduling model; and These represent the model convergence thresholds.

[0045] Example like Figure 3 As shown, this embodiment uses an IES system as an example, consisting of an external power supply network of 14 nodes and a gas network of 6 nodes, and an internal power supply network of 14 nodes, a gas network of 6 nodes, and a heat network of 6 nodes. MATLAB programming and the GUROBI solver are used to solve the model. The scheduling cycle is 24 hours, and the time interval is 1 hour. Parameter settings are shown in Tables 1 and 2, and typical daily wind and solar power generation and load power prediction curves are shown in Tables 1 and 2. Figure 4 .

[0046] Table 1 System Parameters

[0047] Table 2 Parameters of Source-Side Power Generation Units and Energy Conversion Equipment

[0048] The dynamic carbon emission factors of source-side generating units were fitted using piecewise regression, and the fitted curve is shown below. Figure 5 As shown. By Figure 5 It is evident that the piecewise regression method can fit the dynamic changes in the actual carbon emission factor of the source-side power generation units with a relatively small error. At lower power output stages, due to incomplete combustion, more fuel is required per unit of capacity, and since carbon emission levels are inversely proportional to thermal power, more carbon emissions are generated. As the power output of the source-side power generation units increases, the carbon emission factor gradually decreases until it stabilizes.

[0049] Considering the dynamic carbon emission factors of source-side power units, the IES carbon emissions are calculated using a full-process carbon footprint measurement method. Taking the low-load period of 1:00 and the high-load period of 11:00 as examples, the IES carbon footprint distribution results are as follows: Figure 6 , 7 As shown.

[0050] Based on the dynamic carbon emission factors of source-side generating units and the precise quantification of source-side carbon emissions at different time periods, the system input carbon flow rate at 1:00 was 1527.32 kgCO2 / h. The carbon footprint of wind turbines operating at peak output, measured using the life cycle method, was 14.15 kgCO2 / h. The carbon flow rate near node 14 was relatively low, reducing the overall carbon emission intensity of the system. Electric energy storage devices charging at this time effectively diluted the existing high-carbon energy with low-carbon electricity, thus reducing their own carbon flow density, which was precisely characterized as 10.68 kgCO2 / h using the carbon flow model and life cycle method. Electric boilers converted excess electricity into heat, generating a carbon footprint of 148.32 kgCO2 / h, achieving flexible resource utilization. As energy load increased, the output of source-side generating units increased at 11:00, and the system input carbon flow rate rose to 2630.75 kgCO2 / h. The carbon footprints of wind and solar power units, measured using the life cycle method, are 6.69 kgCO2 / h and 22.13 kgCO2 / h, respectively. At this time, the electrolyzer converts excess electrical energy into hydrogen energy, which is then used to produce natural gas through a methane reactor combined with carbon storage equipment, achieving multi-energy complementarity. Figure 6 , 7 The carbon footprint of the entire process of "source-grid-load-storage" in the China IES is clearly and completely distributed in spatial dimensions, providing a reliable data foundation for analyzing changes in flexible resource scheduling in the system and formulating low-carbon scheduling strategies.

[0051] Based on a clear understanding of the complete and precise distribution characteristics of the IES's carbon footprint throughout the entire process, a tiered CCER market mechanism is introduced to incentivize IES energy users to implement low-carbon demand response. The power distribution before demand response is as follows: Figure 8 As shown, the carbon emission factors at different times before demand response and the loads before and after demand response are as follows: Figure 9 As shown.

[0052] Due to the relatively low installed capacity of wind power, the carbon emission factor is mainly affected by the source-side power generation units and remains relatively high. Between 6:00 and 15:00, in addition to meeting the electricity load demand, some electricity from power generation units flows into energy storage or supplies gas load through power-to-gas equipment. The output of high-carbon units adjusts synchronously with load changes, resulting in a trend of first increasing and then decreasing the system's carbon emission factor. Between 10:00 and 14:00, the output of photovoltaic units mitigates the adjustment range of high-carbon units, keeping the carbon emission factor relatively stable within the range of 0.54-0.61 kgCO2 / kWh. Between 16:00 and 21:00, with the gradual increase in wind power penetration and the discharge of energy storage, although the electricity load increases, the output of high-carbon units is suppressed, and the carbon emission factor continues to decline. Between 00:00 and 5:00 and between 22:00 and 24:00, the electricity load is at its lowest point, and the wind power penetration rate is high, causing the system's carbon emission factor to drop to a lower level within the day. After the load demand response, the load peak-valley difference was reduced by 570.02 kW, the system carbon emissions decreased by 3.16%, and the load-side carbon footprint became controllable and optimized.

[0053] Table 3 IES Low-Carbon Demand Response Optimization Scheduling Results

[0054] By combining the tiered CCER market incentives for IES energy users to implement low-carbon demand response, the overall optimized scheduling results after low-carbon demand response are shown in Table 3. The system's carbon emissions were reduced by 3715.05 kg, or 6.27%, and the total cost was reduced by 7443.77 yuan, or 10.48%, effectively improving the low-carbon economics of IES operation.

[0055] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A comprehensive energy system dispatching method considering carbon footprint and market incentives, characterized in that, Includes the following steps: Step 1: Calculate the carbon footprint of the entire IES process according to equation (18). ; (18) In the formula, for Time period Carbon emission intensity of load type for Time period Energy consumption after load demand response For the number of load types, The scheduling period; If the carbon emission intensity of the load equals the carbon potential of the corresponding energy transmission network node, then: (17) In the formula, for Time period Carbon potential of nodes in energy transmission networks; The energy transmission network includes the IES internal electrical network, gas network and heat network. The carbon potential of the IES internal electrical network nodes is calculated according to Equation (14). (14) In the formula, for Carbon potential of IES intra-terminal electrical network nodes , for IES intra-time network branch Power and branch carbon flux density at the injection node; This represents the number of electrical network branches connected to the node within the IES. for Public energy grid Power injected into the node; for Periodic IES Consumption of Public Energy Network Exogenous carbon footprint factor at energy levels; The number of public energy power grids; for Time-of-use energy conversion equipment The electrical power injected into the node at the output end; for Time-of-use energy conversion equipment Carbon footprint factor at the output end; The total number of energy conversion devices connected to the node; Calculate the carbon potential of the gas network and heat network nodes in the IES according to equations (15) and (16): (15) (16) In the formula, , They are respectively Carbon potential of gas network and thermal network nodes within the IES period; , , They are respectively IES intra-period gas network branch Public energy gas network and energy conversion equipment The gas power injected into the output node; for IES intra-period gas network gas branch Carbon flux density in the injection node's branch; This represents the number of gas network branches connected to the nodes within the IES. for Periodic IES Consumption Public Energy Gas Network Exogenous carbon footprint factor at energy levels; The number of public energy gas networks; , They are respectively IES Intra-period heat network branch and energy conversion equipment The thermal power injected into the node at the output end; for IES Intra-period heat network branch Carbon flux density in the injection node's branch; This represents the number of hot network branches connected to the node within the IES. Step 2: Construct a tiered CCER revenue model and calculate the CCER revenue obtained by IES through wind and solar power projects and load low-carbon demand response based on this model; The ladder-type CCER revenue model is expressed as follows: (23) In the formula, For IES projects CCER revenue obtained Indicates wind and solar power projects. This indicates a low-carbon demand response from the load; This refers to the unit price of CCER (CCER). For the project CCER obtained; The length of the CCER interval; The percentage increase in the tiered CCER price; CCERs are used to offset part of the system's carbon quotas. CCERs represent China's certified voluntary emission reductions. Step 3: Construct an upper-level integrated energy operator scheduling model and a lower-level load aggregator scheduling model; The objective function of the upper-level integrated energy operator scheduling model is: (24) (25) (26) (27) (28) In the formula, The total operating cost for the upper-level integrated energy operator; for IES energy purchase cost during the period; for IES equipment operation and maintenance costs during the period; for The cost of wind and solar power curtailment during specific time periods; for IES carbon trading costs for different time periods; CCER revenue obtained by IES through wind and solar power projects; , , These are the unit price of standard coal, the unit price of natural gas, and the carbon trading price, respectively. , , Source-side power generation units Coal consumption characteristic coefficient; for Public energy grid Power injected into the node; for Public energy gas network during the period Power injected into the node; The number of public energy power grids; The number of public energy gas networks; The total number of energy conversion devices connected to the node; Energy conversion equipment The operation and maintenance cost coefficient; for Time-of-use energy conversion equipment Total output power; , , for Time-of-use energy conversion equipment The electrical power, gas power, and thermal power injected into the node at the output end; This refers to the penalty coefficient for wind and solar power curtailment. , They are respectively Total power generation of wind and solar turbines during the time period; Power generation of wind and solar turbines connected to the grid during the time period; , This refers to the carbon quota coefficient; for Total gas consumption of gas turbines and combined heat and power units during a given period; The objective function of the lower-level load aggregator scheduling model is: (29) (30) (31) In the formula, The overall cost for the lower-level load aggregator; for Energy costs for users during specific time periods; CCER revenue obtained from low-carbon demand response to loads; for Time-based user utility function; Energy price for load; , For users' consumption Preference coefficient for load type; Step 4: Alternately and iteratively solve the upper-level integrated energy operator scheduling model and the lower-level load aggregator scheduling model to achieve IES low-carbon economic scheduling.

2. The integrated energy system dispatching method considering carbon footprint and market incentives according to claim 1, characterized in that, The formula for calculating the carbon footprint factor at the output of energy conversion equipment is as follows: (13) In the formula, for Time-of-use energy conversion equipment The nodal carbon potential at the output end, Energy conversion equipment Endogenous carbon footprint factor; Endogenous carbon footprint factors mainly consider energy storage devices, power-to-gas conversion devices, and wind and solar power units; the endogenous carbon footprint factor of energy storage devices... The calculation formula is: (1) In the formula, and These are the carbon footprint factors for the production and transportation of energy storage equipment, respectively. For energy storage equipment conversion efficiency; and These are the first stages of the energy storage equipment production process. Phase 1 Carbon emission intensity and energy consumption of the materials; The first consumption in the production process of energy storage equipment The unit loss coefficient of the material; and These represent the number of stages and the number of material types in the production process of energy storage equipment; and These are the first stages of the energy storage equipment transportation process. The first mode of transportation Carbon emission intensity and energy consumption of the materials; and The total number of transportation methods and material types in the transportation of energy storage equipment; The formula for calculating the endogenous carbon footprint factor of electro-gas conversion equipment is as follows: (2) In the formula, , , These are the endogenous carbon footprint factors for power-to-gas conversion equipment, electrolyzers, and methane reactors, respectively. and These are the carbon footprint factors for the production and transportation of electrolytic cells, respectively. The conversion efficiency of the electrolytic cell; and These are the first steps in the electrolytic cell production process. Phase 1 Carbon emission intensity and energy consumption of the materials; The first consumption in the electrolytic cell production process The unit loss coefficient of the material; and These are the first steps in the electrolytic cell transportation process. The first mode of transportation Carbon emission intensity and energy consumption of the materials; and These represent the number of stages and the number of material types in the electrolytic cell production process, respectively. and These represent the total number of transportation methods and the number of material types involved in the electrolytic cell transportation process. Endogenous carbon footprint factor of wind and solar power units The calculation formula is: (3) In the formula, , These are the endogenous carbon footprint factors for wind turbines and photovoltaic units, respectively. The formula for calculating the node carbon potential at the output end of an electric boiler is: (7) In the formula, , They are respectively The node carbon potential at the input and output ends of the time-limited electric boiler; , They are respectively Energy power at the input and output terminals of the time-limited electric boiler; For electric boiler conversion efficiency; The formula for calculating the node carbon potential at the output end of the electro-gas converter is as follows: (8) In the formula, , They are respectively The node carbon potential at the input and output ends of the time-phase electrolyzer; for The electrical energy consumed by the electrolyzer during a given time period; for Hydrogen power output from the electrolyzer during the time period; and They are respectively The node carbon potential at the input and output of the methane reactor during the time period; for Carbon flow rate of the methane reactor during the specified time period; for Hydrogen power consumed by the methane reactor during the specified time period; for The amount of natural gas produced by the methane reactor during the specified time period; The conversion efficiency of the methane reactor; The formula for calculating the nodal carbon potential at the gas turbine output is: (9) In the formula, , They are respectively The node carbon potential at the input and output of the gas turbine during the time period; for The amount of natural gas consumed by the gas turbine during a given period; for The electrical power output of the gas turbine during a given period; For gas turbine conversion efficiency; The formula for calculating the node carbon potential at the output end of a combined heat and power (CHP) unit is as follows: (10) In the formula, , , They are respectively The node carbon potential at the input, electrical output, and heat output ends of the time-phase cogeneration unit; 、 , They are respectively The natural gas power consumed, electrical power output, and thermal power output of the combined heat and power unit during the specified time period; , These refer to the gas-to-electricity conversion efficiency and the gas-to-heat conversion efficiency of the combined heat and power unit, respectively. In energy storage mode, the formula for calculating the node carbon potential at the output end of the energy storage device is: (11) In the formula, for Carbon emissions from charging energy storage devices during specific time periods; for Charging power of time-limited energy storage devices; In energy storage state The nodal carbon potential at the output of the time-of-use energy storage device. For energy storage duration; In the energy release state, the formula for calculating the node carbon potential at the output end of the energy storage device is: (12) In the formula, for The carbon emissions from energy storage devices are released during specific time periods; for Discharge power of time-limited energy storage devices; In the state of energy release The nodal carbon potential at the output of the time-of-use energy storage device; For the duration of energy release; The discharge efficiency of energy storage devices; , They are respectively , Carbon flux density of electricity in time-segmented energy storage devices; for Available capacity of time-of-use energy storage devices , for Carbon emissions from charging and releasing energy storage devices during specific time periods.

3. The integrated energy system dispatching method considering carbon footprint and market incentives according to claim 1 or 2, characterized in that, The formula for calculating the exogenous carbon footprint factor when IES consumes energy from the public energy grid is: (4) (5) In the formula, for Public energy network branch during the time period Power injected into the node; for Public energy network branch during the time period Carbon flux density in the injection node's branch; The number of public energy network branches connected to the nodes; for Time-based source-side capacity units Power injected into the node; for Time-based source-side capacity units Dynamic carbon emission factors; The number of source-side power generation units connected to the node; The power segmentation threshold; , 、 、 is a constant coefficient, and is the parameter to be fitted; This refers to the capacity of the source-side generating units; The formula for calculating the exogenous carbon footprint factor when IES consumes energy from the public energy gas network is as follows: (6) In the formula, , They are respectively Public energy gas network branch during the period Natural gas source Gas power at the injection node; , They are respectively Public energy gas network branch during the period Natural gas source Carbon flux density in the injection node's branch; The number of public energy gas network branches connected to the nodes; This represents the number of natural gas sources connected to the node.

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