Low-carbon optimal scheduling method and system of integrated energy system

Abstract

This invention proposes a low-carbon optimization scheduling method and system for integrated energy systems, belonging to the field of low-carbon optimization scheduling technology. It includes: establishing a carbon emission mapping model for energy conversion and storage devices in the integrated energy system; constructing a unified carbon emission flow model for the integrated energy system; establishing a load-side dynamic demand response model based on node carbon potential; the load-side dynamic demand response model based on node carbon potential includes constructing a carbon potential response signal model and a transferable and reducible load model, wherein the carbon potential response signal model includes constructing a demand elasticity matrix of carbon emissions based on interactive power mapping, transferring out during high carbon emissions and transferring in during low carbon emissions; constructing a two-stage low-carbon optimization scheduling model for the integrated energy system, and solving for the optimal low-carbon scheduling scheme of the integrated energy system. This invention achieves unified characterization and dynamic control of carbon emission responsibility in multiple types of energy systems.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon optimization scheduling technology, and in particular relates to a low-carbon optimization scheduling method and system for integrated energy systems. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the large-scale integration of renewable energy into integrated energy systems, the coupling between power, heating, and natural gas systems is deepening. Energy flows within these systems exhibit complex characteristics of multiple sources, nodes, and paths, leading to increasingly complex carbon emission transmission relationships across different energy networks. Existing research often focuses on single energy systems, calculating and analyzing carbon emissions separately for power, heating, or natural gas systems. This approach struggles to characterize the unified transmission mechanism of carbon emissions across multiple energy systems (electricity, gas, and heating) and lacks a unified carbon emission flow modeling method applicable to multiple energy networks simultaneously.

[0004] Furthermore, in the problem of optimal scheduling of integrated energy systems, most existing studies take global carbon accounting as the control objective and use carbon emission costs to control low carbon emissions on the power generation side. They fail to reveal the transmission relationship of carbon emission responsibility among different nodes and branches from the load side, making it difficult for the load side to perceive the carbon emission impact of its own energy consumption behavior. At the same time, research on systematic methods for assessing the impact of load-side control on system carbon emissions is not comprehensive enough. Summary of the Invention

[0005] To overcome the shortcomings of the existing technologies, this invention proposes a low-carbon optimization scheduling method and system for integrated energy systems. It establishes a unified carbon emission flow modeling framework applicable to power systems, heating systems, and natural gas systems, and introduces a load-side dynamic demand response mechanism based on nodal carbon potential. At the same time, it combines the split-blown bar method to handle the uncertainty of new energy output, providing a systematic modeling and scheduling method for the low-carbon operation of integrated energy systems. This enables unified tracking and quantitative allocation of carbon emission responsibilities in integrated energy systems, and guides source-load coordinated low-carbon operation under uncertain environments.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In a first aspect, the present invention discloses a low-carbon optimized scheduling method for an integrated energy system, comprising: A carbon emission flow model for an integrated energy system is established, which includes a carbon emission flow model for an electricity system, a carbon emission flow model for a heating system, and a carbon emission flow model for a natural gas system. Establish carbon emission mapping models for energy conversion and storage devices in integrated energy systems, and construct a unified carbon emission flow model for integrated energy systems. Establish a load-side dynamic demand response model based on nodal carbon potential; the load-side dynamic demand response model based on nodal carbon potential includes constructing a carbon potential response signal model and a transferable and reducible load model based on nodal carbon potential, wherein the carbon potential response signal model includes constructing a demand elasticity matrix of carbon emissions based on interactive power mapping, which transfers out when carbon emissions are high and transfers in when carbon emissions are low. A two-stage low-carbon optimization scheduling model for an integrated energy system is constructed with the goal of minimizing system operating costs and under the constraints of energy balance, equipment operation, and carbon emission flow. The optimal low-carbon scheduling scheme for the integrated energy system is then obtained by solving the model. The two-stage low-carbon optimization scheduling model includes a first stage using the day-ahead scheduling model of the power system as the objective function, and a second stage using a demand response model for day-ahead scheduling and a demand response model for the nodal carbon potential of load nodes to optimize load power demand response.

[0007] Secondly, this invention discloses a low-carbon optimized scheduling system for an integrated energy system, comprising: The preliminary model building module is used to establish a carbon emission flow model for a comprehensive energy system, which includes a carbon emission flow model for a power system, a carbon emission flow model for a heating system, and a carbon emission flow model for a natural gas system. The mapping model construction module is used to establish carbon emission mapping models for energy conversion devices and energy storage devices in integrated energy systems, and to construct a unified carbon emission flow model for integrated energy systems. The demand response module is used to establish a load-side dynamic demand response model based on node carbon potential. The load-side dynamic demand response model based on node carbon potential includes a carbon potential response signal model and a transferable and reducible load model. The carbon potential response signal model includes a demand elasticity matrix of carbon emissions based on interactive power mapping, which transfers out when carbon emissions are high and transfers in when carbon emissions are low. The optimization scheduling module is used to construct a two-stage low-carbon optimization scheduling model for the integrated energy system with the objective function of minimizing system operating costs and conditions of energy balance constraints, equipment operation constraints, and carbon emission flow constraints. The model is then used to solve for the optimal low-carbon scheduling scheme of the integrated energy system. The two-stage low-carbon optimization scheduling model includes a first stage using the day-ahead scheduling model of the power system as the objective function, and a second stage using a demand response model for day-ahead scheduling and a demand response model for the nodal carbon potential of load nodes to optimize load power demand response.

[0008] Thirdly, the present invention discloses an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when run by the processor, complete the steps of the low-carbon optimization scheduling method of the above-mentioned integrated energy system.

[0009] Fourthly, the present invention discloses a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the aforementioned low-carbon optimization scheduling method for an integrated energy system.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a unified carbon emission flow model and introduces a carbon emission mapping model for energy conversion equipment. While satisfying system energy balance and equipment operation constraints, it achieves unified characterization and dynamic control of carbon emission responsibility in multiple types of energy systems, effectively improving the low-carbon operation capability of integrated energy systems.

[0011] This invention adopts a two-stage low-carbon optimization scheduling model. The first stage is the day-ahead scheduling of the power system, which can fully reflect the system scheduling results and low-carbon demand. The second stage is rescheduling, which can carry out load low-carbon demand response with carbon emission responsibility as the response signal, realize source-load coordinated low-carbon scheduling, and fully explore the low-carbon scheduling potential of the load side of the integrated energy system.

[0012] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0014] Figure 1 This is a flowchart of the low-carbon optimization scheduling method for the integrated energy system described in Embodiment 1 of the present invention.

[0015] Figure 2 This is a schematic diagram of the integrated energy system described in Embodiment 1 of the present invention. Detailed Implementation

[0016] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0017] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0018] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0019] Example 1 In one or more embodiments, a low-carbon optimized scheduling method for an integrated energy system is disclosed, such as... Figure 1 As shown, it includes the following steps: Step S1: Establish a carbon emission flow model for the electricity-heat-gas system of the integrated energy system.

[0020] The integrated energy system includes an electric system, a heating system, and a natural gas system, with the system structure as follows: Figure 2 As shown, the power system includes wind turbine generators, photovoltaic generators, conventional generators, and electrical load nodes; the thermal system includes heat source equipment, heat load nodes, and heat network pipelines; and the natural gas system includes gas sources, compressors, gas load nodes, and gas network pipelines.

[0021] The carbon emission flow model of the integrated energy system includes the carbon emission flow model of the power system, the carbon emission flow model of the heating system, and the carbon emission flow model of the natural gas system. The coupled carbon emission flow matrix is ​​shown in equations (3)-(6). Carbon emission flow is usually associated with physical matter. In order to fully consider the impact of multiple energy types on carbon emissions in the integrated energy system, the carbon emission flow model is extended and associated with the pipeline flow of the heating system and the natural gas system, respectively, to establish a unified carbon emission flow model for the integrated energy system. Although the power transmitted by the power grid, the flow rate transmitted by the heating network, and the volume transmitted by the gas network all follow the proportional sharing principle in carbon flow tracking, a unified CEF model for the integrated energy system can be constructed based on the generalized flux matrix.

[0022] First, a generalized branch flux matrix is ​​constructed to describe the transmission relationship of carbon emissions in different energy networks within an integrated energy system.

[0023] Secondly, constructing a generalized carbon potential matrix It is used to characterize the carbon emission intensity corresponding to a unit of energy or a unit of mass carrier at each node of an integrated energy system.

[0024] Next, construct the external injection matrix. It is used to describe the equivalent carbon emissions injected into the system by power generation equipment, heat generation equipment, gas sources, and energy conversion equipment.

[0025] Finally, a unified carbon emission flow balance equation is established based on the above matrix, as shown in equation (4), to realize the unified tracking and quantitative allocation of carbon emission responsibility in the multi-energy network of electricity, gas and heat.

[0026] Specifically: Constructing the branch distribution matrix, the external unit injection distribution matrix, and the external injection matrix: (1) (2) (3) in, This is the branch distribution matrix, used to represent the injection topology relationship between branches and nodes; An external generator distribution matrix is ​​used to represent the connection relationship between the energy injection units and the system. For the power system, it represents the generator units; for the thermal system, it represents the heat source; and for the natural gas system, it represents the gas source. Inject matrix into external source; For branch flow, forward flow hour For flow rate values, when the current flows in the opposite direction. , It is the flow value, and ; Generator k injected power; This represents the number of system nodes. This represents the number of generating units.

[0027] Construct a generalized branch flux matrix based on the branch distribution matrix and the external unit injection distribution matrix: (4) (5) (6) (7) (8) (9) in, The generalized branch flux matrix represents the carbon flow direction in the integrated energy system, where, This represents the power flow balance matrix within subsystem x, including the unit injection from energy conversion equipment. This represents the carbon emission coupling matrix of the energy coupling device in different energy systems; x and y are system symbols, x,y∈{E,H,G}, where E represents the power system, H represents the thermal system, and G represents the natural gas system; For the generalized carbon potential matrix of the power system, For the generalized carbon potential matrix of the thermodynamic system, The generalized carbon potential matrix of the natural gas system; express The diagonal elements are the sum of the node-injected power; Represents the set of branches ji that are injected into node i; for Off-diagonal elements The off-diagonal elements are not 0; This indicates that the energy conversion device is located at node n of system x. x To node n of system y y The injected power is taken as a negative value. Taking an electric boiler as an example, when the power system injects power into the heating system... This indicates that the electric boiler is located at node n1 of the power system and is injecting power into node n2 of the thermal system. The n2*n1 position; For the generalized carbon potential matrix of the integrated energy system; The carbon potential matrix for injection includes the corresponding carbon potential injected by external units. This corresponds to the branch distribution matrix of the system; Inject the distribution matrix into the corresponding external units of the system; Let x be the number of nodes in system x; Let y be the number of nodes in system y; Let be the branch power flowing from node j to node i in system x; The branch injection power at node i; The generator injection power at node i.

[0028] The power system carbon emission flow model quantitatively determines the flow state of the power system carbon emission flow based on the power flow distribution.

[0029] Specifically, the carbon potential at power system nodes is: (10) in, Represents the nodal carbon potential of node i; , These represent the injection power and carbon flux density of branch S, respectively. Represents the set of branches s connected to node i; and Let represent the generator power and generator carbon emission intensity connected to the node, respectively. This formula indicates that the carbon potential of node i is determined by the combined effect of the carbon emission flow generated by the generator set connected to the node and the carbon emission flow flowing into the node from other nodes.

[0030] In a thermal system, heat energy is transferred through water as a medium, resulting in energy-mass decoupling. Since heat loss caused by heat dissipation from pipes has no impact on carbon responsibility allocation, using heat energy as a carbon flow medium will create a physical contradiction of mismatch in the transfer of carbon emission responsibility. Therefore, carbon emissions are regarded as a virtual fluid attached to the heat medium (water), and a carbon emission flow model of the thermal system is established.

[0031] Based on the relative independence of hydraulic and thermal operating conditions in the heating network, a time-varying hydraulic flux matrix independent of heat transfer is constructed, simplifying the impact of heat loss. Specifically, the carbon emission flow model for the thermal system is as follows: (11) (12) in, , and These represent the branch flux matrix, branch distribution matrix, and unit injection matrix of the thermal system, respectively. Represents the nodal flux of a thermal system. This represents the sum of the branch injection flux and the unit injection flux connected to node i.

[0032] Furthermore, the carbon potential at the nodes of the thermal system is: (13) in, Represents the nodal carbon potential of node i; The mass flow rate of the heat medium from node j to node i; and Let i be the set of upstream inflow nodes and the set of downstream outflow nodes, respectively. The carbon emission rate injected by the heat source at node i, in units of .

[0033] Natural gas is both an energy carrier and a source of carbon emissions. The carbon emission intensity of a natural gas system is defined as the carbon emission responsibility implied per unit mass of natural gas.

[0034] Specifically, the carbon emission flow model for natural gas systems is as follows: (14) (15) in, , and These represent the branch flux matrix, branch distribution matrix, and unit injection matrix of the natural gas system, respectively. This represents the node flux of the natural gas system. This represents the sum of the branch injection flux and the unit injection flux connected to node i.

[0035] Furthermore, the nodal carbon potential model is as follows: (16) in, Let be the nodal carbon potential of node i; The flow rate of natural gas from node j to node i; Let i be the load flow at node i; Let i be the set of upstream inflow nodes and downstream outflow nodes, respectively. The equivalent carbon emission rate injected at node i by an external gas source (such as a natural gas well or a P2G injection point).

[0036] Step S2: Establish carbon emission mapping models for energy conversion devices and energy storage devices in the integrated energy system, and construct a unified carbon emission flow model for the integrated energy system, namely, a set of carbon emission flow models for the electricity-heat-gas system, which can be represented by a unified form to express the solution formula for the carbon potential of the three system nodes.

[0037] In this embodiment, the energy conversion equipment includes a combined heat and power (CHP) unit, an electricity-to-gas (EHG) unit, an electric boiler, and a carbon capture unit. This invention views the energy conversion equipment as the physical hub for the mutual conversion of heterogeneous energy within a multi-energy flow system and a key node for the migration and redistribution of carbon emission responsibility among the electrical, gas, and thermal subsystems. The key to the unified model is to accurately model the energy conversion equipment capable of transferring or converting energy between multi-energy systems, thereby obtaining a carbon potential mapping.

[0038] Based on the principles of energy conservation and carbon emission conservation, the carbon emission responsibility on the input side is mapped to the energy network on the output side according to the access node and its own characteristics. The carbon emission mapping model for energy conversion and storage devices in the integrated energy system includes mapping models for cogeneration equipment, power-to-gas equipment, electric boilers, and carbon capture equipment, as well as electric energy storage, thermal energy storage, and gas energy storage. Energy conversion equipment is considered a carbon emission coupling node across the energy network in the integrated energy system, and its input-side carbon emission responsibility is uniformly tracked. Specifically: First, cogeneration equipment is subdivided into natural gas system load, power system carbon input source, and heat system carbon input source. As the coupling element connecting the gas network and the power / heat network, the natural gas input to the CHP unit carries the responsibility for carbon emissions. After combustion, this responsibility is distributed to the output electrical and thermal energy. Since the work potential of electrical energy is higher than that of thermal energy, electrical energy bears a greater share of the carbon emission responsibility. Based on the principle of carbon flow conservation, the equivalent carbon emission injection rate on the output side of the CHP unit should be equal to the total carbon flow rate of the natural gas input side.

[0039] The carbon emission mapping model for combined heat and power (CHP) units is as follows: (17) (18) (19) in, The nodal carbon potential at the gas network node; The flow rate of natural gas consumed by the CHP unit; These represent the carbon emission rates injected by CHP into the power grid and heating network, respectively. and These represent the electrical and thermal power generated by CHP, respectively. The thermoelectric ratio of the CHP unit; The Carnot factor characterizes the difference in quality between thermal and electrical energy. and These are ambient temperature and heating temperature, respectively.

[0040] Furthermore, a carbon capture system (CCS) is installed at the tail flue of the CHP (Consumer-to-Pollution) system to separate and seal CO2 through methods such as chemical adsorption. The CO2 is ultimately used to produce syngas in the power-to-gas (PHPoS) system. In this embodiment, by introducing a capture efficiency parameter into the carbon flow model, the captured carbon emissions are deducted to obtain the equivalent carbon emission injection amount considering the effect of the CCS, thereby reducing the net carbon amount flowing from the CHP to the power / heat network at the source.

[0041] The net emission rate after CCS treatment is corrected to: (20) in, The original carbon emission rate without CCS installation; For carbon capture efficiency; This represents the proportion of flue gas diversion.

[0042] Carbon capture equipment can reduce the original carbon emission liability generated during the energy conversion process by setting carbon capture efficiency parameters, so that the equivalent carbon emission liability after capture can be included in the calculation of carbon emission flow of the integrated energy system.

[0043] Secondly, power-to-gas (P2G) equipment is further subdivided into power system loads and natural gas system input sources. P2G equipment consumes electricity to produce syngas or methane, achieving cross-system conversion of electricity to the natural gas system and a physical transfer of carbon emission responsibility from the power system to the natural gas system. In the power grid, P2G equipment is not specially considered, but only as a load; in the gas grid, the P2G device itself does not directly generate new carbon emissions. The carbon emissions carried on its input side are determined by the nodal carbon potential of the upstream power system and are transferred to the natural gas system along with the equivalent energy flow during the energy conversion process, thus achieving a consistent transfer of carbon flow between the power and gas systems.

[0044] The carbon emission mapping model for power-to-gas conversion equipment is as follows: (twenty one) in, Let be the node carbon potential of P2G access node i at time t; This refers to the power consumption of P2G devices; The carbon flux generated by the additional CO2 feedstock purchased for P2G equipment besides the CO2 supplied by the CCS equipment.

[0045] Furthermore, electric boilers are further subdivided into power system loads and thermal system carbon input sources. Electric boilers supply heat to the thermal system by consuming electrical energy, making them a key device connecting the power grid and the heating network. In the unified carbon flow model, electric boilers are considered a "virtual carbon injection source" of the heating network. This embodiment assumes that electric boilers do not directly generate additional carbon emissions during operation, and that the carbon emissions corresponding to their output heat energy are entirely determined by the nodal carbon potential of the input electrical energy.

[0046] The carbon emission mapping model for electric boilers is as follows: (twenty two) in, The electrical power consumed by the electric boiler; Let be the node carbon potential of EB access node i at time t. This mapping relationship ensures that even if the electric boiler itself has no direct physical carbon emissions, the heat energy it produces still bears the implicit carbon emission responsibility of the upstream power generation side, reflecting the fairness of full-chain traceability.

[0047] Finally, energy storage devices achieve carbon emission time-shifting through the time-shifting effect of matter. Energy storage devices (including electrical energy storage, thermal energy storage, and gas storage tanks) have significant time-shifting characteristics, causing carbon flow to lag and mix in the time dimension. Since energy storage devices do not introduce new carbon emissions during charging and discharging, their carbon emission characteristics are mainly manifested as the time shifting of carbon flow carried by input energy or matter. Therefore, they act as carbon flow loads during the energy storage phase and as carbon flow sources during the energy release phase.

[0048] The carbon emission flow model for energy storage devices is as follows: (twenty three) in, and The energy storage capacity states at time t and time t-1 are respectively. and These are the charging and discharging power, respectively. The average carbon potential on the network side during charging; This represents the self-loss rate.

[0049] Step S3: Establish a load-side dynamic demand response model based on nodal carbon potential. The load-side dynamic demand response model based on nodal carbon potential includes constructing a carbon potential response signal model and a transferable and reducible load model based on nodal carbon potential. The carbon potential response signal model includes constructing a demand elasticity matrix of carbon emissions based on interactive power mapping, which transfers out when carbon emissions are high and transfers in when carbon emissions are low.

[0050] Based on the nodal carbon potential, load-side low-carbon response signals and demand response load models are established, whereby the low-carbon response signals are obtained from a unified carbon emission flow model.

[0051] Carbon emissions are mapped based on interactive energy mapping as follows: (twenty four) in, Let be the carbon emissions of node i in time period t; Let be the nodal carbon potential of node i in time period t; Let be the load power of node i during time period t.

[0052] Furthermore, the interactive power mapping model is as follows: (25) in, Let be the load change of node i during time period t; The demand elasticity coefficient for carbon emissions; denoted as t, where t represents the change in carbon emissions over time period t; T represents the total number of time periods in the scheduling cycle.

[0053] Construct a demand elasticity matrix based on carbon emissions: (26) in, This indicates the impact of changes in carbon emissions on changes in electrical power. Let be the load of node i during time period t.

[0054] Based on the nodal carbon potential, a carbon potential response signal is constructed, and the load-side carbon emissions are divided into high-carbon responsibility intervals, medium-carbon responsibility intervals, and low-carbon responsibility intervals to achieve dynamic response of the system's carbon emission status.

[0055] The carbon emission range segmentation model includes dividing the carbon emission range into high-carbon emission range, medium-carbon emission range, and low-carbon emission range based on the carbon emission level at each node. (twenty four) (25) in, , , These are high-carbon, medium-carbon, and low-carbon state variables, respectively. This represents the maximum carbon emissions at the node.

[0056] Further, a load regulation model for high- and low-carbon zones was obtained: The load change when in the high carbon emission range is: (26) The load change when in the low-carbon emission range is: (27) in, , For response coefficients; This is the scheduling time interval.

[0057] Furthermore, the transferable load model is as follows: (28) in, As a reference transferable load; This refers to the amount of load transfer. Maximum transferable power; The carbon transfer coefficient is high. This is the low-carbon conversion coefficient.

[0058] The load reduction model is as follows: (29) in, The load can be reduced based on the baseline; For reduction amount; To achieve maximum power reduction; To reduce the response coefficient; This represents the maximum amount that can be reduced within the scheduling cycle.

[0059] Step S4: Construct a two-stage low-carbon optimization scheduling model for the integrated energy system with the objective function of minimizing system operating costs and conditions of energy balance constraints, equipment operation constraints, and carbon emission flow constraints, and solve for the optimal low-carbon scheduling scheme of the integrated energy system.

[0060] Among them, the two-stage low-carbon optimization scheduling model of the integrated energy system establishes an optimization scheduling model with optimal cost as the objective. The optimization objectives include operating cost, demand response cost, and carbon emission cost. Finally, the optimal low-carbon scheduling scheme of the integrated energy system is obtained by solving the problem. The two-stage objective function specifically includes: Objective function of the first-stage power system day-ahead dispatch model: The objective function of the day-ahead economic dispatch model for the power system is to minimize operating costs. (30) in, The cost of purchasing electricity for the system; For the system's gas purchase cost; Cost of wind curtailment; Cost of wasted light; Time-of-use pricing; For the amount of electricity purchased; The price for purchasing gas; This refers to gas consumption. As a penalty factor for abandoning wind and solar power; This refers to the amount of air that is forcibly abandoned. This refers to the amount of light discarded.

[0061] The constraints include capacity constraints for each generator set; generator set ramping constraints; line power flow constraints; balancing node constraints; and power balance constraints.

[0062] Generator set capacity constraints: (31) in, The generator output power is represented by 'i', which indicates that the generator includes gas turbines, wind turbines, and photovoltaic units. , These represent the maximum and minimum power of the unit, respectively.

[0063] Generator set ramping constraints: (32) in, , These represent the maximum and minimum climbing power.

[0064] Power flow constraints on the line: (33) in, For line power flow; This represents the maximum permissible power flow for the line.

[0065] Balance node constraints: (34) in, The phase angle is the reference node.

[0066] Power balance constraints: (35) in, For load power; This refers to line loss power.

[0067] Second-stage demand response model: Line power flow is obtained through day-ahead scheduling, nodal carbon potential of each node in the system is calculated using a unified carbon emission flow model, load power demand response is optimized using a demand response model based on the nodal carbon potential of load nodes, and the system carbon emission cost is calculated.

[0068] (36) (37) in, Let be the carbon emission cost of node i; Cost of responding to demand.

[0069] This invention proposes a low-carbon optimization scheduling method based on a unified carbon emission flow model for an integrated energy system encompassing electricity, gas, and heat. Building upon the traditional carbon emission flow model, it extends the model to natural gas and heat systems. By introducing a carbon emission mapping model for energy conversion equipment, a unified carbon emission flow model for the integrated energy system is established. Under a demand response mechanism based on node carbon potential, it achieves unified tracking and quantitative allocation of carbon emission responsibility across multiple energy systems (electricity, gas, and heat), providing a systematic modeling and scheduling method for the low-carbon operation of integrated energy systems.

[0070] This invention is expected to further promote the application of integrated electric-gas-heat energy systems in the field of low-carbon energy, and provide strong support for achieving the goals of green transformation of energy systems and carbon emission reduction.

[0071] Example 2 In one or more embodiments, a low-carbon optimized scheduling system for an integrated energy system is disclosed, specifically including: The preliminary model building module is used to establish a carbon emission flow model for a comprehensive energy system, which includes a carbon emission flow model for a power system, a carbon emission flow model for a heating system, and a carbon emission flow model for a natural gas system. The mapping model construction module is used to establish carbon emission mapping models for energy conversion devices and energy storage devices in integrated energy systems, and to construct a unified carbon emission flow model for integrated energy systems. The demand response module is used to establish a load-side dynamic demand response model based on node carbon potential. The load-side dynamic demand response model based on node carbon potential includes a carbon potential response signal model and a transferable and reducible load model. The carbon potential response signal model includes a demand elasticity matrix of carbon emissions based on interactive power mapping, which transfers out when carbon emissions are high and transfers in when carbon emissions are low. The optimization scheduling module is used to construct a two-stage low-carbon optimization scheduling model for the integrated energy system with the objective function of minimizing system operating costs and conditions of energy balance constraints, equipment operation constraints, and carbon emission flow constraints. The model is then used to solve for the optimal low-carbon scheduling scheme of the integrated energy system. The two-stage low-carbon optimization scheduling model includes a first stage using the day-ahead scheduling model of the power system as the objective function, and a second stage using a demand response model for day-ahead scheduling and a demand response model for the nodal carbon potential of load nodes to optimize load power demand response.

[0072] Example 3 This embodiment provides an electronic device, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, they complete the steps of the low-carbon optimization scheduling method of the integrated energy system described above.

[0073] Example 4 This embodiment provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the aforementioned low-carbon optimization scheduling method for an integrated energy system.

[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A low-carbon optimization scheduling method for an integrated energy system, characterized in that, include: A carbon emission flow model for an integrated energy system is established, which includes a carbon emission flow model for an electricity system, a carbon emission flow model for a heating system, and a carbon emission flow model for a natural gas system. Establish carbon emission mapping models for energy conversion and storage devices in integrated energy systems, and construct a unified carbon emission flow model for integrated energy systems. Establish a load-side dynamic demand response model based on nodal carbon potential; the load-side dynamic demand response model based on nodal carbon potential includes constructing a carbon potential response signal model, a transferable load model, and a load reduction model based on nodal carbon potential. The carbon potential response signal model includes constructing a demand elasticity matrix of carbon emissions based on interactive power mapping, which transfers out when carbon emissions are high and transfers in when carbon emissions are low. A two-stage low-carbon optimization scheduling model for an integrated energy system is constructed with the goal of minimizing system operating costs and under the constraints of energy balance, equipment operation, and carbon emission flow. The optimal low-carbon scheduling scheme for the integrated energy system is then obtained by solving the model. The two-stage low-carbon optimization scheduling model includes a first stage using the day-ahead scheduling model of the power system as the objective function, and a second stage using a demand response model for day-ahead scheduling and a demand response model for the nodal carbon potential of load nodes to optimize load power demand response.

2. The low-carbon optimized scheduling method for an integrated energy system as described in claim 1, characterized in that, The construction of the integrated energy system carbon emission flow model includes: A generalized branch flux matrix is ​​constructed to describe the transmission relationship of carbon emissions in different energy networks within an integrated energy system; Construct a generalized carbon potential matrix to characterize the carbon emission intensity per unit energy or per unit mass carrier at each node of the integrated energy system; Construct an external injection matrix to describe the equivalent carbon emissions injected into the system by power generation equipment, heat generation equipment, gas sources, and energy conversion equipment; Based on the above matrix, a unified carbon emission flow balance equation is established to achieve unified tracking and quantitative allocation of carbon emission responsibility in multi-energy networks of electricity, gas, and heat.

3. The low-carbon optimized scheduling method for an integrated energy system as described in claim 2, characterized in that, The establishment of a unified carbon emission flow balance equation: in, The generalized branch flux matrix represents the carbon flow direction in the integrated energy system, where... This represents the internal power flow balance matrix of subsystem x, including the unit injection from energy conversion equipment. This represents the carbon emission coupling matrix of the energy coupling device in different energy systems; x and y are system symbols, x,y∈{E,H,G}, where E represents the power system, H represents the thermal system, and G represents the natural gas system. For the generalized carbon potential matrix of the power system, For the generalized carbon potential matrix of the thermodynamic system, The generalized carbon potential matrix of the natural gas system; express The diagonal elements; Represents the set of branches ji that are injected into node i; for Off-diagonal elements; This indicates that the energy conversion device is located at node n of system x. x To node n of system y y Injection power; For the generalized carbon potential matrix of the integrated energy system; The carbon potential matrix for injection includes the corresponding carbon potential injected by external units. This corresponds to the branch distribution matrix of the system; Inject the distribution matrix into the corresponding external units of the system; Let x be the number of nodes in system x; Let y be the number of nodes in system y; Let be the branch power flowing from node j to node i in system x; The branch injection power at node i; The generator injection power at node i.

4. The low-carbon optimized scheduling method for an integrated energy system as described in claim 1, characterized in that, The construction of the demand elasticity matrix for carbon emissions based on interactive electricity mapping is specifically as follows: The interactive power mapping model is as follows: Construct a demand elasticity matrix based on carbon emissions: in, Let be the load change of node i during time period t; This is the demand elasticity coefficient for carbon emissions; denoted as t, where t represents the change in carbon emissions over time period t; and T represents the total number of time periods in the scheduling cycle. Let be the load of node i during time period t; Let represent the carbon emissions of node i during time period t.

5. The low-carbon optimized scheduling method for an integrated energy system as described in claim 1, characterized in that, Based on the nodal carbon potential, a carbon potential response signal is constructed, and load-side carbon emissions are divided into high-carbon responsibility zones, medium-carbon responsibility zones, and low-carbon responsibility zones: in, , , These are high-carbon, medium-carbon, and low-carbon state variables, respectively. The maximum carbon emissions at the node; The load adjustment model for high- and low-carbon zones includes: The load change when in the high carbon emission range is: The load change when in the low-carbon emission range is: in, , For response coefficients; This is the scheduling time interval.

6. The low-carbon optimized scheduling method for an integrated energy system as described in claim 1, characterized in that, The transferable load model is as follows: in, As a reference transferable load; This refers to the amount of load transfer. Maximum transferable power; The carbon transfer coefficient is high. The low-carbon conversion factor; The load reduction model is as follows: in, The load can be reduced based on the baseline; For reduction amount; To achieve maximum power reduction; To reduce the response coefficient; This represents the maximum amount that can be reduced within the scheduling cycle.

7. The low-carbon optimized scheduling method for an integrated energy system as described in claim 1, characterized in that, The first stage adopts the objective function of the day-ahead dispatch model of the power system, and the objective function of the day-ahead economic dispatch model of the power system is to minimize the operating cost. in, The cost of purchasing electricity for the system; For the system's gas purchase cost; Cost of wind curtailment; Cost of wasted light; Time-of-use pricing; For the amount of electricity purchased; The price for purchasing gas; This refers to gas consumption. As a penalty factor for abandoning wind and solar power; This refers to the amount of air that is forcibly abandoned. This refers to the amount of light discarded. The second stage adopts a demand response model, obtains line power flow through day-ahead scheduling, calculates the nodal carbon potential of each node in the system through a unified carbon emission flow model, optimizes the load power demand response based on the nodal carbon potential of the load nodes, and calculates the system carbon emission cost. in, Let be the carbon emission cost of node i; Cost of responding to demand.

8. A low-carbon optimized scheduling system for an integrated energy system, characterized in that, include: The preliminary model building module is used to establish a carbon emission flow model for a comprehensive energy system, which includes a carbon emission flow model for a power system, a carbon emission flow model for a heating system, and a carbon emission flow model for a natural gas system. The mapping model construction module is used to establish carbon emission mapping models for energy conversion devices and energy storage devices in integrated energy systems, and to construct a unified carbon emission flow model for integrated energy systems. The demand response module is used to establish a load-side dynamic demand response model based on node carbon potential. The load-side dynamic demand response model based on node carbon potential includes a carbon potential response signal model and a transferable and reducible load model. The carbon potential response signal model includes a demand elasticity matrix of carbon emissions based on interactive power mapping, which transfers out when carbon emissions are high and transfers in when carbon emissions are low. The optimization scheduling module is used to construct a two-stage low-carbon optimization scheduling model for the integrated energy system with the objective function of minimizing system operating costs and conditions of energy balance constraints, equipment operation constraints, and carbon emission flow constraints. The model is then used to solve for the optimal low-carbon scheduling scheme of the integrated energy system. The two-stage low-carbon optimization scheduling model includes a first stage using the day-ahead scheduling model of the power system as the objective function, and a second stage using a demand response model for day-ahead scheduling and a demand response model for the nodal carbon potential of load nodes to optimize load power demand response.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, complete the low-carbon optimized scheduling method for the integrated energy system as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the low-carbon optimization scheduling method for the integrated energy system as described in any one of claims 1-7.

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